esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Description  
The dataset available for download is comprised of 320 ground truth data points, as well as digitized field notes and field photographs. The dataset is available as a comma separated value file (.csv) (Figure 1) and as an ESRI shapefile (.shp) with associated metadata. The field notes are available as an Excel workbook (.xlsx) (Figure 2). The field photographs are provided in Portable Network Graphics format (.png) (Figure 3). The ground truth points cover the extent of lies-de-Boucherville National Park. The positions are reported in the NAD83-CSRS (epoch 2010.01.01) coordinate frame and ellipsoidal height for elevation (see Section 3.2). The user must apply a conversion of the GNSS ellipsoidal height to orthometric height (height above mean sea level) by applying a gravimetric geoid model. The Natural Resources Canada GPS-H tool [8] is recommended for applying the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).  
The ground truth dataset contains 15 fields for each of the 320 points collected, and these fields correspond to the fields within the shapefile's attribute table. The field names and a brief description are given in Table 2. The accompanying field notes file contains auxiliary information about the points collected, and consists of 6 fields for each of the 320 points collected. The field names and a brief description are given in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Easting** & **Northing** \\ \hline Mean error (m) & 0.91 & 0.67 \\ Standard deviation (m) & \(\pm\)0.98 & \(\pm\)0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The reported mean and standard deviation in positional accuracy of the CASI-1500 HSI acquired over lies-de-Boucherville National Park in 2019.  
Figure 1: A subset of the 320 ground truth data points listed in the.csv file. The fields contain the FID, longitude, latitude, elevation, the date and time of the beginning/end of collection, the rover’s solution status, and antenna height above the ground. Additionally, there are fields for the calculated precision (i.e., standard deviation, SD) in the x, y, and z dimensions, as well as the radial standard deviation (SD_r). The approximate horizontal and vertical accuracy at the 95% confidence level is also reported. Antenna height, SD, and approximate accuracy at the 95% confidence level are reported in meters.  
Figure 3: A subset of the field photographs taken for each measured location. Each photograph has an identifier that corresponds to a specific point’s FID, as well as in the accompanying metadata containing location and copyright information. The full set of photos are available for download.  
Figure 2: A subset of the digitized field notes for the ground truth dataset. For each point collected, there is auxiliary information detailing the survey date, FID, presence/absence of _Phragmites_, the site characteristics, the corresponding field photograph ID, and any other notes. The full set of digitized field notes is available for download.  
_Phragmites_ in the province of Quebec is located in the Courant Channel along the western edge of the park.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 3 Problem Statement
The PCA component is an entity deployed by the blockchain and preserves data privacy. When the distance of the critical sensors is far from the SDP, energy consumption is very large because body sensors directly send the sensed data to the SDP and then to the PDA. It is not appropriate to transmit emergency data packets since the use of SDP intermediately causes a longer delay when delivering critical data to the PCA. These packets are encrypted using the AES counter mode (AES-CTR). In this algorithm, the bit-flip error is high, and after decryption, it is presented in some of the blocks of ciphertext. If a counter value is inappropriately used, then all security is lost. Delay aware scheduling is executed for various kinds [48] of medical packet transmissions in IoT-based healthcare applications. This paper focuses on beyond WBAN communication, which is data transmissions between gateways and the base station to the remote monitoring system. For scheduling, medical packets are sorted according to the priority awareness and delay constraints. However, a single BS collects all the sensed packets from K-gateways, which is ineffectual to collect all packets. A single BS suffers from large network density, handling different traffic flows and higher energy consumption. * Each medical packet is transferred to the BS based on delay constraints. For example, the glucose monitoring delay requirement is <20 ms. When it is transmitted frequently, then the other delay-constrained emergency packets are dropped. If a medical packet has waited more than the delay time, then the packet is dropped from the scheduler. * The Markov chain does not perform well when the actions are independent and it does not learn the environment to find the current state. Especially, it is needed for WBAN. * The participation of multiple sensor nodes (256 heterogeneous sensors) causes congestion in data transmission and thus fails to transmit critical data or is dropped. A robust authentication scheme was presented in [49] for patient authentication. User smart cards, biometric features and pseudo identities are used, which are held in gateway for each patient. In this step, the timestamp was used to verify the user authentication. This paper uses one-way (1 W) hashing for user credential hashing. In this work, the user authenticated to GW via a public channel, and thus, it is vulnerable to obtaining patient's medical data. To compute the new password, a smart card is necessary. If the smartcard is lost, then it is possible to change the password by any hackers. A one-way hash does not provide high security since the hashed data can be retrieved easily.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### The Coefficient of Absorption \"\(\alpha\)\"  
Several studies have been performed on the acoustic absorption method in seawater. Many models have been developed that serve as the basis for existing scientific research. The absorption coefficient is calculated using two commonly used models: [PERSON]'s [155] and [PERSON] & Simmons' [156]. To attain the absorption coefficient, each of these models is described and contrasted. First, [PERSON] and Simmons' model is discussed. It considers the influence of the depth of seawater, its temperature, and relaxation frequencies produced due to the existence of boric acid and magnesium sulfate [157]. The constants of these models are determined by the following:  
\[A_{1}=1.03\times 10^{8}+2.3\times 10^{-10}T-5.22\times 10^{-12}T^{2} \tag{12}\]  
\[A_{2}=5.62\times 10^{-8}+7.52\times 10^{-10}T \tag{13}\]  
\[A_{3}=\Big{[}55.9-2.37T+4.77\times 10^{-2}T^{2}-3.48\times 10^{-4}T^{3}\Big{]}.1 0^{-15} \tag{14}\]  
\[f_{1}=1.32\times 10^{3}(T+273.1)e^{-1700/T+273.1} \tag{15}\]  
\[f_{2}(inHz)=1.55\times 10^{7}(T+273.1)e^{-3052/T+273.1} \tag{16}\]  
\[P_{1}=1 \tag{17}\]  
\[P_{2}=1-10.3\times 10^{-4}P+3.7*10^{-7}P^{2} \tag{18}\]  
\[P_{3}=1-3.84\times 10^{-4}P+7.57\times 10^{-8}P^{2} \tag{19}\]  
where, \(A_{1}\), \(A_{2}\), and \(A_{3}\) designate the influence of temperature on the absorption of signal; \(P_{1}\),\(P_{2}\), and \(P_{3}\) show the effects of depth; and \(f_{1}\) and \(f_{2}\) demonstrate relaxation frequencies introduced by absorption in sea water-induced due to the existence of magnesium sulfate and boric acid, respectively. Here, \(A\) measures in sec/m \(f\) stands for hertz, and \(P\) is atmospheric pressure (atm). The absorption coefficient may be computed using Equation (20) below, and then multiplied by 8686 to change \(\alpha\) to dB/km:  
\[\alpha=\frac{\left(A_{1}P_{1}f^{2}\right)}{f_{1}^{2}+f^{2})}+\frac{\left(A_{2 }P_{2}f^{2}\right)}{f^{2}+f^{2})}+A_{3}P_{3}f^{2} \tag{20}\]  
[PERSON]'s model can be used to determine the attenuation constant for the UWAC across a frequency \(f\)(kHz) and range of transmission [155; 158].  
\[10 log\alpha(f)=0.11\frac{f^{2}}{(1+f^{2})}+44\frac{f^{2}}{(4100+f^{2})}2.75 \times 10^{-4}f^{2}+0.003 \tag{21}\]The attenuation value for the transmission of the signal can be found using the calculation \(\alpha(f)\). It is also in dB/km and can be placed straight into Equation (10).
## 4 Case Studies
### Discussion
#### 4.3.3 Relaxation Accuracy Analysis  
In order to verify the accuracy of SOCP relaxation, the gap of conic relaxation is defined as  
\[\Delta_{ij}=\left\|I_{ij}-\frac{P_{ij}^{2}+Q_{ij}^{2}}{V_{i}}\right\|. \tag{38}\]  
The gaps of all branches of the 69-bus distribution network during 24 h are shown in Figure 12. It is obvious that the order of magnitude of all the gaps is \(10^{-6}\), which implies that the conic relaxation is actually exact to the original nonconvex model. Thus, the accuracy of SOCP relaxation is verified.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**33-bus Distribution Network**} & \multicolumn{2}{c}{**69-bus Distribution Network**} \\ \cline{2-5}  & **Objective** & **Computational Time (min)** & **Objective** & **Computational Time (min)** \\ \hline Case 1 (MINLP) & 47,820.32 & \textgreater{}120 & 49,501.39 & \textgreater{}120 \\ Case 2 (MISOCP) & 47,788.41 & 5.13 & 49,469.17 & 9.08 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal results of the two methods.  
Figure 12: Scatter diagram of relaxation error of 69-bus distribution network.
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
###### Abstract  
In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.  
high-speed railway system; train positioning technology; deep learning 2020
## 4 Simulation and Discussion
### BER Performance of the UWOC System  
Figure 12 shows the BER performance with various attenuation coefficients, turbulence properties, and transmission distances. The BER will increase as the absorption and scattering coefficients (\(a+b\)) increase, the rate of dissipation of kinetic energy \(\varepsilon\) decreases, the rate of dissipation of mean-squared temperature \(\chi_{\mathrm{T}}\) increases, the approach of the ratio \(\omega\) towards 0, and the transmission distance \(z\) extends.  
Figure 11: Light scintillation index versus (a) \(\mathrm{lg}\), (b) \(\mathrm{lg}\chi_{\mathrm{T}}\), and (c) \(\omega\) with different flow velocities and transmission distances.  
Figure 10: Average received light intensity versus the transmission distance with different absorption and scattering coefficients.  
Turbulence strength can be quantitatively defined with reference to the BER data in Figure 12b-d. Here, we consider weak turbulence as \(\epsilon=10^{-1}\) m\({}^{2}\)/s\({}^{3}\), \(\chi_{\rm T}=10^{-10}\) K\({}^{2}\)/s, \(\omega=-5\); medium turbulence as \(\epsilon=10^{-8}\) m\({}^{2}\)/s\({}^{3}\), \(\chi_{\rm T}=10^{-5}\) K\({}^{2}\)/s, \(\omega=-0.5\); strong turbulence as \(\epsilon=10^{-10}\) m\({}^{2}\)/s\({}^{3}\), \(\chi_{\rm T}=10^{-4}\) K\({}^{2}\)/s, \(\omega=-0.1\). We also consider three groups of absorption and scattering values [13]: \(a=0.0405\) m\({}^{-1}\) and \(b=0.025\) m\({}^{-1}\) for pure seawater; \(a=0.114\) m\({}^{-1}\) and \(b=0.037\) m\({}^{-1}\) for ocean water; \(a=0.179\) m\({}^{-1}\) and \(b=0.219\) m\({}^{-1}\) for coastal water. Using these quantitative definitions, the effects of absorption, scattering, and turbulence on the UWOC performance can be analyzed comprehensively.  
As illustrated in Figure 13, the BER performance will get worse with the intensification of the turbulence strength and the variation of the water quality. In coastal water or under strong turbulence, the BER is too poor to meet the forward error correction requirement. This deterioration can be partly compensated by increasing the transmitting light power. In our study, the transmission distance is 50 m and the AWGN intensity of the UWOC system is \(-10\) dBm. Under weak turbulence in pure seawater, the BER is below \(10^{-6}\) with a transmitting power higher than 0.7 dBm, but it becomes about \(10^{-2}\) under medium turbulence in ocean water. By increasing the transmitting power to 7.5 dBm, the BER can return to \(10^{-6}\). With the BER threshold of \(10^{-6}\), the power penalty for changing pure seawater to ocean water is 5.8 dBm under weak or medium turbulence. The power penalty for changing weak turbulence to medium turbulence is 1.0 dBm in pure seawater or in ocean water. However, the increase of the light power will synchronously increase the turbulence-caused scintillation and the corresponding noise, so the power compensation will become invalid and can no longer improve the BER when the turbulence is excessively strong, that is, when the scintillation noise is dominant.
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 1 Introduction
Theoretically, structures have many linear buckling modes linked to relevant critical load values. But in engineering applications, the smallest critical load value, namely, the critical buckling load factor (BLF), is under consideration, as exceeding this load leads to structural instability and failure. Therefore, in optimization, it is essential to maximize the critical BLF of the structure to enhance its stability [16; 28; 30; 31]. However, it should also be considered that in practice, structural instability often occurs before reaching the critical buckling load. This is due to the potential presence of imperfections in the structure or the material reaching its yield point. In this paper, a level-set-based density method proposed in our previous study [15] is used to optimize the critical BLF of structures with curved grid stiffeners. This provides new insights for the buckling research of stiffened structures. Two level set functions are converted by cosine function, combined using the maximum function, and projected to real densities using an approximate Heaviside function. The Mindlin plate theory is employed for finite element analysis of buckling. After that, the sensitivity analysis is performed, and the design variables are updated. Ultimately, the optimized results are obtained. The effectiveness of this approach in enhancing structural stability is proven by several numerical examples. The main structure of the paper is outlined as follows. Section 2 presents the definition of the grid stiffener structure. Section 3 discusses the relevant constraints for stiffener optimization. The optimization framework and sensitivity analysis are detailed in Section 4. Several numerical examples and result analyses are presented in Section 5. Section 6 gives the conclusions.
## 3 Results and Discussion
### Correlation between Aroma-Active Compounds and Sensory Attributes  
Based on the GC-O analysis, aldehydes, alcohols and terpen were the main contributors to the flavor of HY EO and GF EO. The aroma attributes of key aroma-active compounds in HY EO and GF EO were mainly classified into six categories (sweet/fruity, floral, fatty, woody, green and unpleasant) and a flavor wheel was drawn on this basis (Figure 4A). Specifically, the aldehydes, such as octanal, citronellal, decanal, etc., mostly provided fatty and citrus scents which formed the characteristic citrus flavor. Some alcohols such as linalool, \(\alpha\)-terpineol and geraniol offered floral and fruity odors, whereas elemol, \(T\)-murolol and \(\alpha\)-cadinol contributed off-flavors like pungent and spicy. The scents of terpen were various and complex, with \(\beta\)-myrcene and \(D\)-limonene contributing to fruity notes, _cis-\(\beta\)_-Farnesene, germacrene D and germacrene B to woody notes, copaene and \(\beta\)-elemene to green and herbal notes and \(\gamma\)-terpinene presenting an engine oil note.  
Additionally, PLSR was applied to assess the correlation between aroma-active compounds (X) and sensory evaluation data (Y). As shown in Figure 4B, the PLSR model included two principal components: factor 1 and factor 2, explaining 94% of the X-variance and 3% of the Y-variance, respectively. All the sensory attributes and aroma-active compounds were situated between the inner and outer ellipses, indicating that they were adequately explained by the PLSR model. The sensory variables of sweet, fruity and floral were concentrated on the positive X-axis, exhibiting positive correlations with \(\beta\)-myrcene, linalool, geraniol, geraniol, and so on. Conversely, fatty, woody and green were located on the negative X-axis, and they were positively correlated with undecanal, \(\beta\)-elemene, nootkatone and others. These results were generally consistent with the olfactory description in the GC-O experiment.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 5 Discussion
### Industrial Strength Data
The Canadian and Chinese studies [131; 134] used large sample counts and produced the most consistent and representative m values of contemporary HSLA steels, that is, \(\mathrm{m_{est}}\) is 22.3 to 28.2. These values are approximately one-half of the lower limit of \(\mathrm{m_{obs}}\) observed in the laboratory studies as discussed in Sections 4.2 and 5.1. As noted previously, this reduction in m (or increase in CV) is caused by additional deviation due to chemistry variations, process differences between steel mills, and test procedures, among others. In terms of the parameters used in the ASTM E28 study [24], it is the R parameter that governs the deviation for large-scale studies. Thus, the observed reduction in \(\mathrm{m_{est}}\) is expected. Reinforcing bars [135; 136]  
Two studies examined steel-reinforcing bars and showed m values of 18 to 34. High strength suspension cable wires [15]  
Two datasets of high C steel strength, one from the Bisan Seto Bridge in Japan with N = 38,470 (completed in 1988), showed m values of 94 to 97, with the strength levels reaching 1.65-1.66 GPa. Cast iron pipes [137]In spite of its known lack of ductility, cast iron pipes have been used for water distribution systems at many cities. This study reported results of a systematic examination of buried cast iron pipes in and around London, UK. Samples were excavated from 119 locations that were known to be in four different stages of deterioration: undamaged, lightly damaged, moderately damaged, and heavily damaged. The number of pipe samples, which were 0.5 to 1 m in length, was 34, 43, 36, and 36 at each stage, and about 15 samples were tested in flexure for each pipe sample. Nearly 1800 flexure strength tests were conducted. The results were analyzed, and most of the Weibull modulus values were found to be below 10, as indicated in Table 8. The damage stages and median m\({}_{\text{obs}}\) values appear to be correlated, but the m\({}_{\text{obs}}\) value is so low, even in the sound state. Thus, improved nondestructive testing methods may be more beneficial for identifying the damage states of buried pipes [139].
###### Abstract  
The development of space-based Internet of Things is limited by insufficient allocable frequency resources and low spectrum utilization. To meet the demand for massive access users under the condition of restricted frequency resources, a multi-dimensional hybrid multiple-access method for space-time-frequency-code division based on user distribution (MHSTFC-UD) is established. It divides the beam cell of a low orbit satellite into the central and edge area and dynamically adjusts the radius of the central area and the allocation of frequency resources according to the distribution of users. The optimization model for the radius of the central area and the allocation of frequency resources is established and solved by the genetic algorithm. Also, it takes the edge area as the protection interval to realize the full-frequency multiplexing between the beam cells in the time domain, space domain and code domain. The simulation results show that compared with the traditional method of frequency reuse in two or three dimensions, the multi-dimensional hybrid multiple-access method can improve the maximum access capacity of a single satellite user by one to three orders of magnitude. Moreover, the MHSTFC-UD can increase users by an additional 11.5% to 33.1% compared to fixed area division and frequency resource allocation.  
beam layout; multiple access; user distribution; user capacity; space-based Internet of Things +
Footnote †: journal: _Sensors_
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 5 Results
### Hybrid Discrete Wavelet-CNN Model
#### 5.2.1 Training Parameters  
The training process of the hybrid discrete wavelet-CNN model is depicted in pseudocode form in Figure 9. First, the time series of Current A were analyzed using DWT with the efficient Dmey wavelet. Then, the raw values of detail coefficients of Level 5 were extracted. The samples of the time series were 100,000, while the samples of the batches were 1562 after DWT analysis. In this way, the data were compressed without losing information, and the training time of CNN model was extremely shorter. The model's architecture is presented in Table 10, and the hyperparameters employed for training were as follows:  
* Epochs = 100.
* Batch size = 4.
* Learning rate = 0.001.
* Optimizer = ADAM.
* Dataset split = 70%, 15%, 15%.  
Figure 8: Reconstructed W–N signals using DWT.