esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 3 Tourist Trip Chain Modeling
### The Number of Tourist Trip Chains  
To facilitate the analysis of tourists' trip chains, this paper specifies that the origin and destination zones in a trip chain are not the same. Furthermore, each trip in a trip chain starts from one traffic zone to another, and there are no repeated traffic zones in a trip chain. This means there are no cyclic structures within the trip chain formed by traffic zones.  
Therefore, it can be defined that the length of a trip chain is the number of selected destination zones in that chain. Consequently, the maximum length of the trip chains of a choice set is the maximum number of destination zones that can be selected, i.e., the value of \(\varphi\). Then, suppose a trip distribution model has a \(\varphi\) dimensional destination zone choice set, because the number of trip chains in a choice set should be less than or equal to the maximum number of accessible destination zones (\(J\)), i.e., \(1\leq\varphi\leq J\), so the number of trip chains in the model is equal to \(M\times\left(A_{J}^{1}+A_{J}^{2}+\ldots+A_{J}^{\varphi}\right)\), where \(M\) is the maximum number of the origin zones, and the calculation of \(A_{J}^{\varphi}\) is based on the permutation formula. For example, if a tourist intent to visit three scenic spots, both the number of origin zones and destination zones is three, and the maximum length of the trip chain is three (i.e., \(\varphi=3\)), then the dimension of the traffic zone choice set is no more than three, and the number of trip chains is \(3\times\left(A_{3}^{1}+A_{3}^{2}+A_{3}^{3}\right)=45\).  
Figure 3: Different structures of the trip chain. \(m\) denotes the origin zone and \(n\) represents the destination zone.  
In practice, the length of a trip chain and further the number of trip chains can be calculated according to the travel behavior of tourists. More specifically, it can be estimated through a questionnaire survey of tourists about the time and distance required to finish the trips.
# Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
## 2 Background
### Approximate Message Passing  
where  
\[\alpha_{(n+1)}=N_{o}^{2}+\frac{K}{N}\frac{\alpha_{(n)}\sigma^{2}}{\sigma^{2}+ \alpha_{(n)}}, \tag{7}\]  
and  
\[\hat{\mathbf{x}}_{(n+1)}=\frac{\sigma^{2}}{\sigma^{2}+\alpha_{(n)}}(\mathbf{H }^{H}\mathbf{z}_{(n)}+\hat{\mathbf{x}}_{(n)}), \tag{8}\]  
where \(\mathbf{z}_{(n+1)}\) is the residual. A common selection of the initial estimations are \(\hat{\mathbf{x}}_{(0)}=[0\cdots 0]\), \(\alpha_{(0)}=\sigma^{2}\), and \(\mathbf{z}_{(0)}=[0\cdots 0]\). It is noteworthy that the AMP algorithm can be implemented by a matrix-vector product, which leads to a low complexity. In [36], the performance of the AMP algorithm is evaluated and justified.  
In [32], a hardware implementation of the MPD algorithm is proposed where approximate updating and serial message updating schemes are utilized to reduce the computational complexity of the MPD algorithm. The algorithm is free of division and exponential operations.
## Materials and Methods  
The objects of study in the work are:  
1. 2 samples of straight-run diesel fuel (DF): F1, F2, obtained from atmospheric distillation units of oil from various fields in Western Siberia;
2. 2 samples of n-paraffins: P1--n-paraffins separated from heavy gasoil, obtained as a residue of atmospheric distillation of oil at an oil refinery and P2--n-paraffins separated from highly paraffinic oil fraction;
3. 2 samples of commercial depressants: A1, A2. According to information from the manufacturer, the type of active component of the additives is the copolymers of vinyl acetate and ethylene.  
The following techniques were used to determine the properties and composition of the samples:  
* CP was determined according to the method presented in [34];
* CFPP was determined according to the method presented in [35];
* PP was determined according to the method presented in [36];  
The method for determining CP and PP is as follows: a double-walled test tube filled with the test sample is immersed in a liquid low-temperature thermostat and gradually cooled. Every 1-2 \({}^{\circ}\)C, the test tube is removed from the thermostat and compared with a previously prepared test fuel sample at room temperature. If there is visible turbidity of the fuel relative to the standard, CP is recorded. With a further decrease in temperature, the fuel solidifies; PP is fixed when, the test tube tilting at 45\({}^{\circ}\) and holding it in this position for 1 min, the meniscus of the sample does not shift.  
The method for determining CFPP is as follows: in a low-temperature liquid thermostat, using a CFPP determination unit on a cold filter, the sample under study was gradually cooled. At intervals of 1 \({}^{\circ}\)C, the test sample was automatically pumped through a standardized wire mesh filter into a glass pipette under a controlled vacuum. If the test sample did not have time to fill the pipette within 60 s, then the experiment was stopped and CFPP was recorded, because fuel has lost the ability to pass through the filter.  
* The content of n-paraffins was determined by gas chromatography (chromatograph \"Chromatek-Kristall 2000\" with a quartz capillary column 30 m \(\times\) 0.25 mm, stationary phase--SE-54, carrier gas--helium).  
The essence of the method is the chromatographic separation of liquid hydrocarbon blends on a capillary column with a non-polar stationary phase, followed by registration of hydrocarbons with a flame ionization detector and automated processing of the obtained information using software.  
A sample of the liquid hydrocarbon blend is introduced into a gas chromatograph equipped with a quartz capillary column containing a stationary phase. Under the influence of a carrier gas, the sample passes through a column in which its components are separated. The components are detected by a flame ionization detector as they elute from the column. The detector signal is processed by an electronic data acquisition system or an integrating computer. Each resulting peak is identified by retention index or visual comparison with standard chromatograms.
# Flexible Shear and Normal Force Sensor Using only One Layer of Polyvinylidene Fluoride Film
## 3 Experimental Setup  
To analyze the performance of the shear and normal force sensor, we used a tensile testing machine (MCT-2150, A&D CO., LTD.) to apply normal and shear loads. By connecting a 3D-printed bar to the tensile testing machine, we were able to apply a force to the blue-colored area (\(8\times 14.7\) mm\({}^{2}\)) shown in Figure 2a. Moreover, the fabricated sensor array could be fixed on an acrylic support to position the sensor horizontally or vertically. For the shear force tests (Figure 2b), the sample was vertically oriented and the test speed was 300 mm/min. The 3D-printed bar for the shear force was designed as shown in Figure 2b. For the normal force tests (Figure 2c), the sample was horizontally oriented and the test speed was 150 mm/min. When applying a normal force, we used a different bar, shown in Figure 2c, with the shear force tests.  
The setup of the sensor output measurement for one module is shown in Figure 3a. Specifically, we only observed signals from the P1, P2, and P3 sensors. The analog voltages were read via the analog-digital converting pins in the Arduino Nano Microcontroller. We connected a load resistor of 100 M\(\Omega\) to each sensor and applied an offset of 3.3 V to measure the analog voltage output in the positive value region. The sampling rate was 300 Hz. A personal computer (PC) used for data processing was connected to the microcontroller through serial communication. For the full testing of the \(2\times 2\) sensor array, a 16-channel analog multiplexer (MUX, CD74 HC4067) was used to measure 12 signals and was controlled via the microcontroller. The sampling rate was adjusted to 50 Hz since the number of sensors increased.  
Figure 2: Schematic of the experimental setup for studying the sensing performance. (**a**) Area where the shear and normal forces were applied to the sensor array. (**b**) Schematic of the experimental setup with the sensor positioned vertically (left) and the 3D-printed bar (right) during the shear force test. (**c**) Schematic of the experimental setup with the sensor positioned horizontally (left) and the 3D-printed bar (right) during the normal force test.
## 6 Necessity of Using UWB Signals  
In this section, we take the single-slot synchronous network shown in Figure 7c as an example and clarify the necessity of using UWB signals in the ISAL networks by comparing the SPEB results achieved with different signal bandwidths. We set the signal bandwidths to 500 MHz (UWB signal) and 5 MHz, respectively, and obtain the corresponding SPEBs, as shown in Figure 13.  
It can be found from Figure 13 that the SPEB obtained by the 5 MHz signal is much larger than that obtained by the 500 MHz UWB signal. Therefore, to achieve high-precision sensing performance, there is a need to use UWB signals in the ISAL networks.  
Figure 12: SPEB comparison between the step-by-step scheme and the integrated scheme in four dual-slot asynchronous networks. **(a)** SPEB comparison in a dual-slot asynchronous network (Figure 8a). **(b)** SPEB comparison in a dual-slot asynchronous network (Figure 8b). **(c)** SPEB comparison in a dual-slot asynchronous network (Figure 8c). **(d)** SPEB comparison in a dual-slot asynchronous network (Figure 8d).
## 4 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**XGBT**} & \multicolumn{2}{c}{**HGBR**} \\ \cline{2-5}
**Method** & \(\mathbf{R^{2}}\) & MAE & \(\mathbf{R^{2}}\) & \(\mathbf{MAE}\) \\ \hline Remove missing & 0.739 (0.111) & 109.408 (20.823) & 0.749 (0.103) & 106.983 (20.386) \\ Similar fill & 0.924 (0.023) & 89.103 (9.608) & 0.926 (0.021) & 86.565 (9.551) \\ Mean values & 0.925 (0.020) & 90.240 (9.794) & 0.929 (0.018) & 86.750 (9.041) \\ Predict (LR) & 0.927 (0.023) & 87.129 (11.312) & 0.927 (0.024) & 85.118 (11.361) \\ Predict (HGBR) & 0.929 (0.025) & 85.901 (11.576) & 0.930 (0.025) & 84.633 (11.123) \\ Predict (XGBT) & 0.930 (0.024) & 83.389 (11.251) & 0.931 (0.023) & 83.619 (11.172) \\ Predict (RF) & 0.935 (0.027) & 78.590 (11.139) & 0.935 (0.027) & 78.853 (11.647) \\ Interpolation (L) & 0.927 (0.016) & 89.823 (8.578) & 0.930 (0.016) & 85.956 (8.972) \\ Interpolation (P) & 0.922 (0.026) & 90.298 (10.035) & 0.925 (0.025) & 87.253 (10.146) \\ Interpolation (S) & 0.924 (0.021) & 90.676 (10.070) & 0.929 (0.020) & 86.279 (10.069) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative results of using different preprocessing methods for missing values, after removing outliers with MCD. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**XGBT**} & \multicolumn{2}{c}{**HGBR**} \\ \cline{2-5}
**Method** & \(\mathbf{R^{2}}\) & \(\mathbf{MAE}\) & \(\mathbf{R^{2}}\) & \(\mathbf{MAE}\) \\ \hline Remove missing & 0.750 (0.100) & 105.738 (19.072) & 0.766 (0.103) & 101.952 (02.124) \\ Similar fill & 0.928 (0.019) & 86.783 (9.188) & 0.931 (0.018) & 83.636 (9.205) \\ Mean values & 0.929 (0.020) & 86.062 (9.985) & 0.932 (0.019) & 83.714 (9.608) \\ Predict (LR) & 0.928 (0.019) & 86.347 (9.696) & 0.929 (0.019) & 84.630 (9.602) \\ Predict (HGBR) & 0.934 (0.022) & 82.611 (10.374) & 0.934 (0.021) & 82.107 (9.662) \\ Predict (XGBT) & 0.935 (0.020) & 81.085 (10.845) & 0.936 (0.019) & 80.581 (9.695) \\ Predict (RF) & 0.937 (0.017) & 77.262 (8.176) & 0.938 (0.017) & 77.689 (7.980) \\ Interpolation (L) & 0.925 (0.021) & 88.573 (8.675) & 0.927 (0.020) & 85.441 (8.142) \\ Interpolation (P) & 0.928 (0.019) & 86.783 (9.188) & 0.931 (0.018) & 83.636 (9.205) \\ Interpolation (S) & 0.925 (0.021) & 88.573 (8.675) & 0.927 (0.020) & 85.441 (8.142) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative results of using different preprocessing methods for missing values, after removing outliers with OC-SVM.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 2 Materials and Methods
### Time Series Analysis
#### 2.2.3 Interannual Polynomial Model  
Since interannual variations play an important role in the determination of the model parameters and their uncertainties [58], especially in GNSS, instead of capturing these variations in the stochastic model, we alternatively chose to directly model most of the interannual variation within the deterministic model function. In addition to the different terms in Equation (1), we added degree 2 and degree 3 polynomial terms. This model is called _tiasd_ (for trend interannual (semi-)annual steps draconitic). The low degree polynomial function was used to fit the long-term (relative to the length of the time series) interannual signal. We adjusted the model with the WLS method using the least squares algorithm of [59] implemented in the Python programming language. The stochastic term \(\varepsilon(t)\) was then reduced to a simple WH noise estimation. We first obtained the optimal parameters of the model and calculated the residuals of the time series. These residuals could still have contained a faster interannual signal (between a year and a decade, depending on the length of the time series) and were then modelled by a polynomial of degree 4 to 11. The degree of the adjusting polynomial was chosen such that it is always inferior to the time series length (in year). For time series with a low completeness (<70%) and high standard deviation (beyond 7 mm in the vertical component), we determined the polynomial degree by analysing each time series individually while verifying that the polynomial did not overfit the series. The polynomial fitting procedure in these two steps (low degrees first and then high degrees on residuals) was decided upon to avoid the strong correlation that could have occurred between high-degree polynomials and the sinusoidal and offset terms. In order to reconstruct the full interannual variations, we added the slow and fast polynomial contributions. We calculated the instantaneous velocity by taking the first derivative of the resulting polynomial function and deducing a value of linear velocity by taking the mean of instantaneous velocities. The uncertainty on this definition of linear velocity should reflect the magnitude of the interannual variation. Then, in addition to considering only the dispersion of the positions in the calculation of the velocity uncertainty, as in the case of WH-only WLS estimation without polynomial adjustment, we also took into account the dispersion of the instantaneous velocity, which should give a more realistic uncertainty on the linear velocity. Thus, we define the uncertainty as the standard deviation of the instantaneous velocity divided by the square root of the time series length [60].
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 2 Background
### Blockchain Implementation in IoT  
When it comes to implementing blockchain within an IoT environment, it can play an important role in more than just security. When used along with a smart contract, it can be used in managing, controlling, and securing IoT devices [21]. An example of this is for wines and spirit, where blockchain technology is being recommended for use in labeling and tracing these products to ensure quality and to prevent illegal trading and adulteration [22]. Blockchain technology in this case is being used to manage and control the smart sensors used to keep track of these liquors and wines. In addition to its capability to make IoT device management more efficient, it allows for IoT devices to be removed from the control of a centralized authority who can manipulate or stop the system from working [23]. This makes attacks against the network a lot more difficult since the network does not revolve around an individual. In addition, the data received from IoT devices and stored in the blockchain network would also be less susceptible to plaintext and cipher attacks due to the hashing of data in the blockchain.  
In Figure 3, we present the proposed architecture for an IoT blockchain platform. It is composed of a large number of IoT devices and sensors, user devices, full nodes acting as local bridges, and data storage, all of which are linked to a peer-to-peer blockchain network. The IoT devices and sensors can be connected directly to the blockchain network or can connect to it through a full node. These IoT devices will collect useful data via sensors or user inputs and can request specific transactions through the blockchain network. Data can be sent or received by the IoT devices along with transactions. These data are then stored within the data storage which itself can be stored in two places. One place can be direct data storage, whether it be hardware or software. The second is the blockchain, where data are stored as blocks and can be viewed by anyone. Transactions, on the other hand, must be validated by a group of miners who in turn will receive some sort of reward for validating these transactions. These transactions will then be stored in existing blockchains and form new blocks that will be added to the ledger.
## 2 Results and Discussion
### GC-MS Analysis of the Volatile Compounds Obtained Using \(Hd\) and HS-SPME
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI**} & \multicolumn{8}{c}{**Sample Symbol and Content (\%)**} \\ \cline{3-11}  & & & **RI\({}_{\text{L}}\)** & **M1** & **M2** & **M3** & **M4** & **M5** & **M6** & **M7** \\ \hline
51 & Menthyl acetate & 1278 & 1278 & 0.68 & - & - & - & - & - & - \\
52 & Neommethyl acetate & 1295 & 1296 & 14.13 & - & - & - & - & - & - \\
53 & Thymol & 1299 & 1297 & 0.16 & - & 5.48 & - & - & 0.20 & - \\
54 & Neoisomenthyl acetate & 1310 & 1311 & 0.15 & - & - & - & - & - & - \\
55 & Dihydrocarryl acetate & 1333 & 1330 & - & - & - & 0.15 & 10.70 & - & - \\
56 & _trans_-Carly acetate & 1342 & 1342 & - & - & - & 0.09 & - & - & - \\
57 & Piperientone & 1345 & 1347 & - & - & - & - & - & 34.68 & - \\
58 & _cis_-Carryl acetate & 1367 & 1365 & - & - & - & 0.15 & 0.19 & - & - \\
59 & Piperientone oxide & 1371 & 1371 & - & - & 1.04 & - & - & 30.96 & 72.69 \\
60 & \(\alpha\)-Copane & 1379 & 1378 & 0.13 & - & - & - & - & - & - \\
61 & \(\beta\)-Bourbonene & 1386 & 1386 & 0.15 & 0.88 & 0.08 & 1.21 & 0.52 & - & - \\
62 & \(\beta\)-Elemene & 1393 & 1394 & 0.11 & - & - & 0.89 & 0.84 & - & 2.22 \\
63 & _cis_-lasme & 1399 & 1396 & - & - & - & 0.26 & - & 0.78 & - \\
64 & Tetradacene & 1400 & 1400 & 0.10 & 0.39 & 0.31 & - & 0.66 & - & 1.63 \\
65 & \(\alpha\)-Gurjunene & 1411 & 1411 & 0.10 & - & - & - & - & - & - \\
66 & _trans_-Caroyophyllene & 1422 & 1423 & 0.39 & 0.90 & 5.15 & 1.73 & 6.00 & 1.49 & 2.63 \\
67 & \(\alpha\)-Humulene & 1456 & 1456 & - & - & 0.20 & 0.06 & 0.21 & 0.10 & - \\
68 & (E)-B-Farmesene & 1460 & 1460 & 0.52 & 0.22 & 0.12 & 0.37 & - & 0.52 & 0.93 \\
69 & \(\alpha\)-Amorphene & 1479 & 1485 & 0.05 & - & - & 0.06 & 0.32 & 0.41 & - \\
70 & Germacrene D & 1483 & 1482 & 1.24 & 0.34 & 1.36 & 1.56 & 3.11 & - & 1.54 \\
71 & Bicyclogermacrene & 1497 & 1499 & 0.47 & - & 0.19 & 0.44 & - & - & - \\
72 & \(\delta\)-Calinene & 1526 & 1524 & 0.12 & - & 0.28 & 0.18 & 0.55 & - & - \\
73 & Elemol & 1557 & 1558 & - & - & - & - & 0.51 & - & - \\
74 & Veridiflorol & 1593 & 1593 & 0.11 & - & - & - & 0.32 & - & - \\
75 & \(\alpha\)-Eudesmol & 1657 & 1651 & - & - & - & - & 0.35 & - & - \\ \hline \hline \end{tabular} -, Not detected; RL\({}_{\text{L}}\) literature values of retention indices.  
\end{table} 
Table 2: _Cont._  
Figure 2: Total ion chromatograms (TICs) of the headspace (**A**) and essential oil (**B**) of _Mentha dulcia citreus_ Hillary’s Sweet Lemon (**M5**). The numbers in (**A**) correspond to the compounds in Table 2, and the numbers on (**B**) correspond to Table 1.
## 1 Introduction
In addition, the occurrence of false positives in the segmentation results can also bring misdiagnosis, leading to deviations in subsequent diagnosis and treatment. For example, the false positive phenomenon in medical segmentation results means that the image segmentation results of the originally healthy patient are positive (ill), which will lead to unnecessary diagnostic procedures and burdens for the patient, and even possibly cause the condition to worsen. However, the above methods did not notice this issue. To solve the false positive phenomenon mentioned above, we design a classification module for predicting false positive cases. To solve the boundary blur problem mentioned, we adapt a subdivision-based point-sampling method to replace the original upsampling method for \"rendering\" high-quality boundaries. Besides, we view medical image segmentation as a render problem and propose a network based on 3D U-Net, named **Render 3D U-Net**, to accurately segment medical 3D CT images. Our contributions in this work conclude the following:  
1. Render 3D U-Net draws on the idea of render in computer graphics and adapts a subdivision-based point-sampling method to replace the original upsampling method for \"rendering\" high-quality boundaries (Section 3.2). 2. Render 3D U-Net integrates the special module (Section 3.3) into the 3D ANU-Net architecture (Section 3.1) for classifying a false positive phenomenon. Render 3D U-Net was evaluated on three public datasets (LiTS, KiTS, and segTHOR) and achieved very competitive performances in three medical image segmentation tasks (Section 4.4). 4. Although the render method has been frequently used in computer graphics, to the best of our knowledge, it is the first time that the main idea of render is introduced into a 3D medical image segmentation task.
## 3 Experiments
### Parameters Discussion  
For our proposed method FSF, only one parameter is to be adjusted, the dimensionality of spectral bases q. When extracting spectral bases by SVD on LR-HSI, the dimensionality of spectral bases will affect the fusion results. Usually, the spectral bases exist in a small subspace, and we vary the dimension of the spectral bases from 1 to 10 to find the most suitable number of spectral bases. Figure 1(a) shows the PSNR values of the fused images when the number of spectral bases is varied from 1 to 10 in the Pavia University dataset, and it can be seen that the best fusion results are obtained when the dimension of spectral bases is 4. In Figure 1(b), corresponding to the results on the CAVE dataset, it is clear that the best results are obtained when the spectral basis dimension is 3. The other methods used for comparison, CSTF [[https://github.com/renweidian/CSTF](https://github.com/renweidian/CSTF)]([https://github.com/renweidian/CSTF](https://github.com/renweidian/CSTF)) (accessed date: 9 August 2021), Hysurc [[https://github.com/alfaiate/HySurc](https://github.com/alfaiate/HySurc)]([https://github.com/alfaiate/HySurc](https://github.com/alfaiate/HySurc)) (accessed date: 9 August 2021), LTTR [[https://github.com/renweidian/LTTR](https://github.com/renweidian/LTTR)]([https://github.com/renweidian/LTTR](https://github.com/renweidian/LTTR)) (accessed date: 9 August 2021), NLSTF [[https://github.com/renweidian/NLSTF](https://github.com/renweidian/NLSTF)]([https://github.com/renweidian/NLSTF](https://github.com/renweidian/NLSTF)) (accessed date: 9 August 2021), STEREO [[https://sites.google.com/site/harikanats/](https://sites.google.com/site/harikanats/)]([https://sites.google.com/site/harikanats/](https://sites.google.com/site/harikanats/)) (accessed date: 9 August 2021), and NSSR [[http://see.xidian.edu.cn/faculty/wsdong](http://see.xidian.edu.cn/faculty/wsdong)]([http://see.xidian.edu.cn/faculty/wsdong](http://see.xidian.edu.cn/faculty/wsdong)) (accessed date: 9 August 2021), adjusted the parameters that affect their fusion results to achieve the best fusion results. The best parameters after tuning corresponding to these methods are shown in Tables 1 and 2.