esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Indoor 3D reconstruction and navigation element extraction with point cloud data has become a research focus in recent years, which has important application in community refinement management, emergency rescue and evacuation, etc. Aiming at the problem that the complete wall surfaces cannot be obtained in the indoor space affected by the occluded objects and the existing methods of navigation element extraction are over-segmented, we propose a method to automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. First, the outline and occupancy information provided by the horizontal projection of the point cloud was used to guide the wall segment restoration. Second, we simulate the scanning process of a laser scanner for segmentation. Third, we use projection statistical graphs and given rules to identify missing wall surfaces and \"hidden doors\". The method is tested on several building datasets with complex structures. The results show that the method can detect and reconstruct indoor navigation elements without viewpoint information. The means of deviation in the reconstructed models is between 0-5 cm, and the completeness and correction are greater than 80%. However, the proposed method also has some limitations for the extraction of \"thick doors\" with a large number of occluded, non-planar components.  
point cloud; wall segment restoration; room segmentation; openings; indoor navigation elements Perm  
Despite many proposed methods, studies for reconstructing indoor navigation elements are still ongoing [9; 10]. With the development of the construction industry, room walls are not sometimes perpendicular to each other. In addition, complex scenes, such as emergency rescue, usually have many occluded objects, which causes missing walls, misclassification of indoor context labels, and incorrect extraction of openings [11; 12]. In order to solve these problems, this paper presents a new processing method. In the absence of viewpoint and color information, this method can automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. The main contributions of this paper are as follows:  
1. A method of wall segment restoration using the outline and occupancy information from the horizontal projection of point cloud.
2. A method of room segmentation that simulates the work of a laser scanner that not only divides separate rooms, but also distinguishes the connecting transition space between rooms.
3. Identify the doors in the room and optimize the connections between the rooms by means of the planimetric projection statistical graph and the given rules.  
The remainder of this paper is organized as follows: Section 2 discusses the related research on indoor navigation element extraction. Section 3 presents the proposed method for indoor navigation element extraction and construction. Section 4 validates the proposed method using publicly available datasets. Section 5 evaluates the results. Finally, Section 6 summarizes this study.
## 5 Conclusions  
This study explores a polarization invariance model for GSM laser beam propagation for optical communication in a downlink channel based on the unified theory of polarization coherence and the Huygens-Fresnel principle. It developed models for the probability density distribution and scintillation index of optical intensity under atmospheric turbulence, along with a BER model for a CPolSK-modulated system using a GSM beam. The influence of laser spatial coherence on these parameters was thoroughly analyzed. Numerical simulations revealed that partially coherent GSM beams exhibited enhanced robustness to atmospheric turbulence in the downlink. Using fully coherent light, the GSM-beam  
Figure 5: The curve of the BER of CPolSK and OOK modulated optical communication system with the mean receiving signal-to-noise ratio at different \(\zeta\)s.  
based CPoISK modulated system achieved optimal performance with reduction of 1.51 dB in the required SNR compared to that of an OOK system using fully coherent light. Using an incoherent source with PolSK can approach the BER of coherent OOK at similar power levels, demonstrating that alternative transmitter designs may offer different tradeoffs. The proposed partially coherent scheme requires higher transmitted power than the fully coherent case to achieve the same BER. Note that this study focuses on theoretical channel performance; hardware implementation and system design are beyond its scope. It is worth noting that the multi-phase screen transmission method is an alternative simulation approach that can complement our analysis by providing real-time and statistical insights. Its implementation will be considered in future work.  
Conceptualization, J.W., Y.Z., Q.L., S.Y. and J.Y.; Methodology, J.W. and Y.Z.; Writing--original draft, Y.Z.; Writing--review & editing, J.W., Q.L., S.Y. and J.Y.; Supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Funding Project of Huzhou City (Grant No. 2022Y252).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables
## 2 Materials and Methods
### System Load Balancing Based on the Intrinsic Properties of the Chord DHT  
This section points out, mathematically, using the dispersion properties of the hash function, that these features within the DHT are the ones that implicitly yield load balancing among the nodes, that provide the service of processing their clients' requests.  
The dispersion or hash functions, such as SHA-1 [38] and MD-5 [39], which are also used in DHTs such as Chord [9], are functions that map a very large domain of values into a much smaller one, while needing a very short time to retrieve the hashed value, based on its hash identifier.  
So, if the hash function is denoted by \(f\), we have:  
\[f:LargeDataSet->IDSet\]  
and it is known that any hash function \(f\) has the following important property:  
\[cardinal(LargeDataSet)\ggg cardinal(IDSet).\]  
Moreover, if \(f\) is a good hash function, the input values that yield the same value \(y\in IDset\), are very different, i.e., the difference between them is very large:  
\[\forall y_{i},y_{j}\in LargeDataSet,f(y_{i})=f(y_{j}):\]  
\[|y_{i}-y_{j}|\rightarrow\infty.\]  
Besides that, the property of having almost equal number of input values that yield each hash identifier also stands:  
\[\forall y_{i},y_{j}\in LargeDataSet,1\leq i,j\leq n,y_{i}\
eq y_{j}:\]  
\[|cardinal(f^{-1}(y_{j}))-cardinal(f^{-1}(y_{j}))|<e,\]where \(\epsilon\to 0\) and \(N\) are a large number, \(N\leq cardinal(IDSet)\). For the hash function used in the Chord DHT, \(N\) is a power of 2, i.e., \(N=2^{M}\).  
This actually means that each \"bucket\" of data, which is each entry in the hash table, \"contains\" almost the same amount of data and that each pair of pieces of input data that reaches the same \"hash bucket\" is extremely different. It also follows that the probability for a piece of data to find itself in a hash entry is equal for any hash entry, which gives an even distribution of data in a classical hash table and also in a _Distributed Hash Table_ (DHT).  
From the improved highly available system's point of view, if many peers have been launched, each DHT peer \(k\) would handle \(cardinal(f^{-1}(y_{k}))\) values, thus, from the equations above, each peer would have almost the same load as the other peers in the DHT. This mathematically argues the load balancing introduced by the Chord DHT in the hereby presented improved solution for high availability.
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 3 Results  
### Reflectance Simulation under Several SZA  
Firstly, the effect of SZA was investigated on each canopy shape. Figure 6 plots the relative changes in reflectance at 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\) SZA for each canopy shape, based on reflectance at 10\({}^{\circ}\) SZA. For cylinders and inverted half ellipsoids, relative reflectance decreased to 0.9 when SZA increased from 10\({}^{\circ}\) to 20\({}^{\circ}\); however, when SZA increased from 20\({}^{\circ}\) to 40\({}^{\circ}\), the values were almost 0.9. For ellipsoid, relative reflectance decreased to 0.8 when SZA increased from 10\({}^{\circ}\) to 20\({}^{\circ}\), but even when SZA increased from 20\({}^{\circ}\) to 40\({}^{\circ}\), the mean values did not change. For the half ellipsoid, relative reflectance decreased to 0.8 when SZA increased from 10\({}^{\circ}\) to 20\({}^{\circ}\), and when SZA increased 20\({}^{\circ}\) to 40\({}^{\circ}\), the values decreased to 0.7. Secondly, the effect of canopy shape on the same SZA was investigated. Figure 7 shows the reflectance simulation results for the same SZA with different canopy shapes. As shown in Figure 7, when SZA is 10\({}^{\circ}\) to, the simulated reflectances of the four canopy geometries are almost the same. When SZA is greater than 20\({}^{\circ}\) to, the cylinder and the inverted half ellipsoid have almost the same reflectance and higher reflectance than the other two. It also shows that the ellipsoid is higher in reflectance than the half ellipsoid. Table 4 shows the results of multiple comparisons by [PERSON]'s HSD test on the results. The p value was set to 0.05. No significant difference in reflectance obtained from each canopy shape was observed when SZA was 10\({}^{\circ}\) to, but when SZA was greater than 10\({}^{\circ}\) to, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Time** & **SAA (\({}^{\circ}\))** & **SZA (\({}^{\circ}\))** \\ \hline
08:00 & 98 & 61 \\
10:00 & 115 & 33 \\
12:00 & 181 & 16 \\
13:00 & 244 & 34 \\
14:00 & 261 & 62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Solar geometric conditions for estimating shadow fraction. differences in reflectance among canopy shapes became larger. However, as mentioned above, no significant difference was observed between the canopy shapes of the cylinder and the inverted half ellipsoid.
## 1 Introduction
On this basis, an optimization method for long- and short-sequence fault diagnosis of gearbox transmissions based on variational mode decomposition substitution entropy and the chameleon search algorithm is proposed, and the actual test data of a gearbox transmission are divided into a training set and a test set, and the training set and the test set are further divided into two sets each. The features of the training set are extracted using VMD-PE, and models in the test set are trained and fault-diagnosed using CSA-LSTM. The results show that the method can accurately and quickly identify gearbox faults under 60% to 110% rated pressure test conditions, which is better than existing methods. The second part of this study describes the construction, inference, and optimization process of the gearbox fault diagnosis model based on the VMD algorithm and the improved LSTM algorithm. The third part describes the setup of the experimental platform and the relevant parameters and conducts experimental validation and comparative experiments on the method proposed in this paper through examples, which further verifies the superiority of the method. Fault classification and diagnosis are carried out by the improved long short-term memory neural network algorithm and the experimental results are analyzed. The fourth part is the summary of the whole paper and discusses the outlook for future work. Feature Extraction of Vibration Signals Based on Variational Mode Decomposition and Arrangement Entropy  
As depicted in Figure 1, the proposed model operates in a series of steps to assess the condition of a gearbox. Initially, vibration signals are captured from the gearbox at a motor speed of 1420 rpm, employing a sampling frequency of 10 kHz. These signals are recorded in various states of gear looseness and wear, as well as when the gearbox is in optimal condition. The second phase involves the implementation of VMD to dissect the vibration signals. For a gearbox in pristine condition, the VMD solely is adept at extracting pertinent data. However, when evaluating a gearbox with transmission defects, a combination of VMD and arrangement entropy proves instrumental in isolating the characteristic features of these imperfections. In the subsequent stage, the identification of patterns indicative of gear wear states is executed by the chameleon search-optimized long short-term memory sequence algorithm. This innovative approach is meticulously designed to delineate and recognize intricate patterns associated with various stages of gear deterioration. The final comparative analysis reveals the superior efficacy of the presented model over conventional methodologies. VMD outperforms empirical mode decomposition in signal decomposition efficiency. Similarly, arrangement entropy exhibits enhanced precision in quantifying fault features compared to multi-scale arrangement entropy and approximate entropy, among other time series complexity calculations. This rigorous assessment underscores the proposed model's enhanced accuracy and reliability in diagnosing and characterizing gearbox anomalies.
## 5 Pre-Identification Method for Continuous Low Thrust Parameters  
### Continuous Low Thrust Direction Identification Methods  
The results of the continuous thrust parameter pre-identification are derived from the inverse solution of two radar observations separated by a certain length of time. Let the initial moments of the two radar observation arcs be \(t_{10}\), \(t_{20}\), the ending moments be \(t_{1k}\),\(t_{2k}\), and the observation durations be \(\Delta t_{1}\), \(\Delta t_{2}\). The time intervals between are denoted as \(\Delta t\), as shown in Figure 6. The methodology of Section 2 is applied to perform orbit determination based on two radar arc segments.  
Let the results of the two orbit determinations be  
\[\sigma_{1}=[a_{1},e_{1},i_{1},\Omega_{1},\omega_{1},M_{1}]\]  
\[\sigma_{2}=[a_{2},e_{2},i_{2},\Omega_{2},\omega_{2},M_{2}]\]  
The orbital elements are converted to the mean orbital elements, denoted as  
\[\overline{\sigma}_{1}=[\overline{a}_{1},\overline{e}_{1},\overline{i_{1}}, \overline{\Omega}_{1},\overline{\omega}_{1},\overline{M}_{1}]\]  
\[\overline{\sigma}_{2}=[\overline{a}_{2},\overline{e}_{2},\overline{i_{2}}, \overline{\Omega}_{2},\overline{\omega}_{2},\overline{M}_{2}]\]  
We compared the results of the two orbit determinations, focusing on the changes in semi-major axis, eccentricity, inclination and RAAN. Set the thresholds to \(a_{g},e_{g},i_{g},\Omega_{g}\). If  
Figure 4: Variation in orbit inclination under normal thrust.  
Figure 5: Variation in RAAN under normal thrust.  
\(|\overline{a}_{1}-\overline{a}_{2}|>a_{\xi}\), consider that there is a tangential thrust. If \(\left|\overline{i}_{1}-\overline{i}_{2}\right|>i_{\xi}\) or \(\left|\overline{\Omega}_{1}-\overline{\Omega}_{2}\right|>\Omega_{\xi}\), consider that there is a normal thrust. Since the RAAN is strongly influenced by the orbit perturbation, \(\Omega_{\xi}\) is the variable associated with \(\Delta t\). Different parameter pre-identification methods are used for different directions of thrust, as shown in Figure 7.  
The selection of the threshold is related to the accuracy of the single-arc segment orbit determination. If the accuracy of each orbit element is  
\[\sigma_{\varepsilon}=[a_{\varepsilon},e_{\varepsilon},i_{\varepsilon},\Omega_ {\varepsilon},\omega_{\varepsilon},M_{\varepsilon}]\]  
Figure 6: Schematic of the two observation arcs.  
Figure 7: Flowchart of the continuous low thrust parameter pre-identification method.  
after the single-arc segment orbit is determined, then the threshold for each orbit element can be  
\[a_{\xi}=a_{\xi};e_{\xi}=e_{\xi};i_{\xi}=i_{\xi};\] \[\Omega_{\xi}=\Omega_{\xi};\omega_{\xi}=\omega_{\xi};M_{\xi}=M_{\xi}\]  
The accuracy of the single-arc segment orbit determination is obtained from the orbit determination results statistics, or historical empirical values can be used directly.
# SPME-GC/MS Analysis of Methanol in Biospecimen by Derivatization with Pyran Compound
###### Abstract  
Methanol is metabolized in the body to highly toxic formaldehyde and formate when consumed accidentally. Methanol has been typically analyzed with gas chromatography-flame ionization detector (GC-FID). However, its retention time may overlap with other volatile compounds and lead to confusion. Alternative analysis of methanol using gas chromatography/mass spectrometry (GC/MS) also has limitations due to its similar molecular weight with oxygen and low boiling point. In this study, methanol and internal standard of deuterium-substituted ethanol were derivatized with 3_A_-dihydro-2H-pyran under acid catalysis using concentrated hydrochloric acid. The reaction products including 2-methoxytetrahydropyran were extracted with solid-phase microextraction followed by GC/MS analysis. This method was successfully applied to measure the lethal concentration of methanol in the blood of a victim with a standard addition method to overcome the complex matrix effect of the biospecimen. Identification of the metabolite formate by ion chromatography confirmed the death cause to be methanol poisoning. This new method was a much more convenient and reliable process to measure methanol in complex matrix samples by reducing sample pretreatment effort and cost.  
methanol; derivatization; solid-phase microextraction; gas chromatography/mass spectrometry 2020, 25, 41; doi:10.3390/molecules25010041
## 5 Conclusions
#### Limitations  
However, there are a number of limitations to this paper. First, there is a high probability that the experimental image dataset was not represented in the raw dataset, because it was extracted by a random sampling technique. Second, both the ML- and DL-based models were less able to predict the 'Local' failure type accurately (F1-score = 0.8081 with the proposed DCNN) than they were the other seven wafer map failure pattern types. Third, the ResNet50, ResNet101, and EfficientNetB0-based prediction models may not have been generated properly in this experiment, because they had deep structures and were trained using large datasets, while the size of the training dataset was small in this experiment.  
Conceptualization, H.Z. and J.Y.L.; methodology, H.Z.; software, H.Z.; validation, H.Z., S.W.A.S. and S.H.S.; formal analysis, H.Z.; investigation, H.Z., S.W.A.S. and S.H.S.; resources, H.Z. and J.Y.L.; data curation, H.Z. and J.Y.L.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., S.W.A.S., S.H.S. and J.Y.L.; visualization, H.Z., S.W.A.S. and S.H.S.; supervision, J.Y.L.; project administration, J.Y.L.; funding acquisition, J.Y.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F1-score** & **AUC** & **Accuracy** \\ \hline DCNN1 & 0.9032 & 0.8903 & 0.8942 & 0.9912 & 0.8925 \\ DCNN2 & 0.9104 & 0.9127 & 0.9113 & 0.9908 & 0.91 \\ DCNN3 & 0.9037 & 0.9023 & 0.9006 & 0.9901 & 0.9012 \\ DCNN4 & 0.8673 & 0.8746 & 0.867 & 0.9867 & 0.8688 \\ Proposed DCNN & **0.9381** & **0.9379** & **0.9376** & **0.9933** & **0.9375** \\ \hline \hline \end{tabular} Note: The best results are shown in bold.  
\end{table}
Table 5: Overall performance comparison results of the DCNN-based prediction models with different structures.  
**Funding:** This research was funded by Korea Institute for Advancement of Technology (KIAT) grant through the Korea Government by Ministry of Trade, Industry and Energy (MOTIE) (No. N0002429), in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) by the Ministry of Education (No.2017R1D1A1A02018718), and in part by the Ministry of Science and ICT (MSIT), Korea, through the Grand Information Technology Research Center support program supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP) (IITP-2021-2020-0-01462).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data is an open-source dataset and can be downloaded in the MIR Corpora (Available online: [[http://mirlab.org/dataSet/public/](http://mirlab.org/dataSet/public/)]([http://mirlab.org/dataSet/public/](http://mirlab.org/dataSet/public/))) (accessed on 18 October 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 6 Proposed Security Framework
### Sensor Layer in Agriculture 4.0  
This layer's major function is to acquire meaningful information from the external environment via sensors to take required action against it. This sensor layer has a variety of Sensor nodes of various forms and sizes. As a result, they all use the same basic components, such as input and output components, information handling units, communication segments, and many more units for the management of power sections. To analyze and store all detected data, it is sent to the relevant fog nodes. Furthermore, Sensor nodes receive instructions to undertake agricultural operations.