esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A New Transient Numerical Model
### Governing Equations
#### 2.2.2 Vapor Control Volume  
As mentioned above, a vapor plug is considered to be a control volume. The energy equations for the vapor plug are:  
\[dU=\delta Q-\delta W \tag{4}\]  
\[m_{v}c_{v}dT_{v}+c_{v}T_{v}dm_{v}=\sum_{i}h_{v}A_{i}(T_{w,j}-T_{v})+c_{p}T_{v}dm _{v}-pdV \tag{5}\]  
Introducing the Meyer formula (\(c_{p}-c_{v}=R_{g}\)) into Equation (5) results in:  
\[dT_{v}=\frac{\sum\limits_{i}h_{v}A_{i}(T_{w,j}-T_{v})+R_{g}Tdm_{v}-pdV}{m_{v}c _{v}} \tag{6}\]  
\(h_{v}\) represents the heat transfer coefficient between the pipe wall and the vapor plug, which can be calculated by [14]:  
\[Nu=4.364+0.722\text{RePr}d_{i}/Le_{x} \tag{7}\]  
\[h_{v}=\lambda_{v}Nu\times/d_{i} \tag{8}\]  
If the temperature difference between the wall and the liquid film temperature is higher than the required superheat for the nucleate boiling, the phase change occurs in the liquid film. The nucleate boiling heat transfer coefficient of the liquid film is calculated by the correlation provided by [PERSON] and [PERSON] [25; 26], given by:  
\[h_{nb}=h_{l}\left[1+3000\text{B}\text{o}^{0.86}+\bigg{(}\frac{x_{v}}{1-x_{v}} \bigg{)}^{0.75}\bigg{(}\frac{\rho_{l}}{\rho_{v}}\bigg{)}^{0.41}\right] \tag{9}\]  
The evaporated mass \(dm_{v}\) in the vapor plug is calculated using Equation (10):  
\[dm_{v}=\sum_{i}h_{nb}(\pi d_{i}Le_{v})(T_{w}-T_{v,sat})/h_{fg} \tag{10}\]  
When the temperature between the saturation temperature of the vapor plug and the pipe wall is higher than the required subcooled temperature, condensation occurs in the vapor plug. The condensation coefficient is calculated by Equations (11)-(13):  
\[h_{cond}=Nu_{cond}\times(\lambda_{l}/d_{i}) \tag{11}\]  
where \(Nu_{cond}\) represents the Nusselt number for the condensation, which is calculated by [27; 28]:  
\[Nu_{cond}=0.023\frac{4\overset{\bullet}{m}(1-x)}{\pi d\mu}\text{Pr}^{0.4}(1+ \frac{2.22}{X_{tt}}) \tag{12}\]  
\[X_{tt}=\bigg{(}\frac{1-x}{x}\bigg{)}^{0.9}\bigg{(}\frac{\rho_{v}}{\rho_{v}} \bigg{)}^{0.5}\bigg{(}\frac{\mu_{l}}{\mu_{v}}\bigg{)}^{0.1} \tag{13}\]  
The condensed mass in the vapor plug is calculated by:  
\[dm_{v}=\sum_{i}h_{cond}(\pi d_{i}Le_{v})(T_{w}-T_{v,sat})/h_{fg} \tag{14}\]  
The vapor is assumed to be an ideal gas, therefore:  
\[P_{v}=\big{(}m_{v}R_{g}T\big{)}/V_{v} \tag{15}\]If the vapor pressure calculated by Equation (15) is higher than the saturation pressure for the vapor temperature, the phase change occurs in the PHP and the pressure is equal to the saturation pressure:  
\[\text{if}\quad P_{v}\geq P_{sat}(T_{v})\;\;,\;\;\;\;\;\;P_{v}=P_{sat}(T_{v}) \tag{16}\]
# A 2-V 1.4-dB NF GaAs MMIC LNA for K-Band Applications
## 1 Introduction  
Although SOI technologies achieve remarkable results and are closing the gap with existing III-V processes [1], the latter still show a superior performance and are attracting the attention of many integrated circuit designers. Moreover, monolithic microwave integrated circuit (MMIC) processes provide an efficient solution for implementing discrete components and full radio interfaces in a single chip [2]. In this sense, gallium nitride (GaN)-based circuits provide many advantages, mainly in terms of power management, efficiency, and breakdown voltage [3]. In contrast, gallium arsenide (GaAs) technologies are the preferred option when a lower noise figure (NF) is pursued at greater frequencies [4, 5]. Therefore, GaAs technologies are most appropriate in high-frequency scenarios requiring a very low NF, such as mmWave applications, SATCOMs or 5G networks. These technologies are utilized in high-resolution radar, including short range military arcraft radios and astronomical observations, all of them operating in the K-band. Particularly, the 26 GHz frequency band is of great interest since it has been identified as a pioneer band for the European Union's 5G new radio (NR) networks [6]. There are two mm-Wave bands, designated as n258 and n257 in 3 GPP NR, ranging from 24.25 to 27.5 GHz and 27.5 to 29.5 GHz, respectively. They enable very high data rates and data capacity, making them suitable for hotspot coverage. Similarly, the US identified the 27.5-28.35 GHz band for the same purpose, whereas the 27.5-29.5 GHz and 26.5-29.5 GHz frequency bands are considered for Japan and Korea, respectively [6].  
To implement a very low noise receiver, special attention must be paid to the design of the low-noise amplifier (LNA) since its noise contribution is critical to the NF of the system.  
Thus, the LNA performance affects the overall receiver sensitivity and linearity [2; 7; 8]. Not many works are available in the literature that report LNA implementations achieving a NF below 1.5 dB with a gain above 30 dB and low Input Return Loss (IRL or \(\mid\)S\({}_{11}\) 1) [9; 10; 4; 5; 11; 12; 13].  
In this article, a 2-V 1.4-dB NF four-stage GaAs MMIC LNA from 24.25 to 27.5 GHz is presented. We introduce a design methodology focused on the selection of the active device geometry and DC bias conditions to obtain the lowest NF possible in a common-source (CS) amplifier with source degeneration. The LNA operates in the K-band (from 23 to 29 GHz), achieving a maximum gain of 34 dB at 24.5 GHz, a minimum NF of 1.3 dB at 26.5 GHz, an \(\mid\)S\({}_{11}\) 1 better than 10 dB, a \(P_{1\text{dB}}\) of -18 dBm and an OIP3 of 24.5 dBm. In Section 2, a detailed description of the proposed circuit is presented, and particular aspects regarding the design implementation with the selected Process Design Kit (PDK) are provided in Section 3. Electromagnetic (EM) post-layout simulation results of the four-stage GaAs LNA are shown in Section 4, and a comparison is made with the most relevant proposals available in the literature. Conclusions are drawn in Section 5.
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 4 Machine Learning Algorithms and Performance Measurements  
In this section, we describe the ML algorithms used in our work as well as the measurements used to evaluate their performances.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Devices** & **Descriptions** \\ \hline On Button & Turns on the level control process of the water storage tank. \\ \hline Off Button & Turns off the level control process of the water storage tank. \\ \hline Light Indicator & Indicates whether the system is on or off. \\ \hline Level Sensor 1 (LS1) & Monitors the maximum water level in the tank. When the water reaches the maximum level, the sensor sends a signal to PLC. \\ \hline Level Sensor 2 (LS2) & Monitors the minimum water level in the tank. When the water reaches the minimum level, the sensor sends a signal to PLC. \\ \hline Valve & Controls the water level in the tank. When the water reaches the maximum level, the valve opens, and when the water reaches the minimal level, the valve closes. This logic is implemented in PLC using the ladder language. \\ \hline Water Pump 1 & Fills up the water tank. \\ \hline Water Pump 2 & Draws water from the tank when the valve is open. \\ \hline PLC & Controls the physical process. The logic of the water control system is in PLC, which receives signals from the input devices (buttons, sensors), executes the program, and sends signals to the output devices (water pumps and valve). \\ \hline HMI & Used by the administrator to monitor and control the water storage system in real-time. The administrator can also display the devices’ state and interact with the system through this interface. \\ \hline Data History & Used to store logs and events of the SCADA system. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the devices used in the testbed.
## 5 Experiments and Results
### Evaluation Metrics  
Many evaluation metrics can be used to assess the strengths and weaknesses of a classification model. We decided to focus on four of these metrics; namely, precision, recall, accuracy, and F1-score. Precision (1) is the ratio of true positives to the sum of true positives and false positives, indicating the classifier's ability to avoid false positives. Recall (2) refers to the ratio of true positive predictions to the actual positives, reflecting how well the model captures the correct instances. Accuracy (3) is the ratio of the number of correct predictions to the total number of labeled texts, serving as a measure of the classifier's overall correctness. F1-score (4) is a combined measure that takes the harmonic mean of precision and recall, providing a balanced evaluation of the classifier's performance [45].  
\[Precision=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{1}\]  
\[Recall=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{2}\]  
\[Accuracy=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{TN}+\text{FN}+\text{ FP})} \tag{3}\]  
\[1-score=\frac{(2*\text{Precision}*\text{Recall})}{(\text{Recall}+\text{ Precision})} \tag{4}\]
## 3 Results and Discussion
### Characterization of the Mineral by XRD  
#### 3.3.1 Characterization of Run-of-Mine Mineral Fed into the Input Stream  
The XRD diagrams of the celestine mineral show that the main mineral phases detected in the run-of-mine mineral (sample 70) and mineral after desliming (sample 70D) (Table 1, Figure 5) are celestine, calcite, strontium, quartz, dolomite, and illite. After desliming (70D), the celestine degree increases by a few percentage units (70: 67.45% celestine, 70D: 69.45% celestine) and the quartz content decreases (70: 6.79% quartz, 79_D: 3.54% quartz). The finer fraction of the mineral is enriched in quartz and clays; therefore, their selective removal through the desliming process should increase the percentage of celestine, as shown by [PERSON] et al. (2022).  
Figure 4: Mineral mass recovered in the over and under streams for the different conditions tested.  
Figure 5: X-ray diffraction patterns of 70D (deslimed; (**A**)) and 70 (run-on-mine; (**B**)) celestine mineral samples that were fed into the hydroxyclone system. Main peaks of mineral phases are indicated: illite (llt); celestine (Cel); calcite (Cal); and quartz (Qtz).
## 2 Methods
### Schur-Based Substructuring Methods
#### 2.2.2 Nine Schur Complement Approach  
In this approach, we introduce the substructuring technique in Equation (14). The main difference of this approach is that we do not need to generate the whole matrix to solve the problem, saving memory. We can work only with the unit cell using the hierarchy presented in Section 2.2.1, computing nine Schur complements for a two-dimensional periodicity, as seen in Figure 3, where we can see that the Schur complement for, e.g., cells 5 and 6, is the same. Note that for the one-dimensional periodicity case, we would have only three different Schur complements. Thus, the procedure that we follow for each kind of unit cell is:  
1. Generate the numbering of the unknowns following the hierarchy in Section 2.2.1.
2. Compute the integral terms (including terms related to boundary conditions) from (9) that are applicable to each kind of unit cell. Note that the volume terms (first, third, fourth, and fifth terms in (9)) are common to each cell (and can be computed once for all the cells), whereas the surface terms (second and sixth terms in (9)) depend on the kind of the cell: e.g., the cell number 1 needs to apply on the south face an absorbing boundary condition whereas the cell number 5 does not need to compute any surface integral term.
3. Extract the subset of unknowns that are involved at the interface with other unit cells.
4. Condense the original problem, i.e., compute the Schur complement.  
Once we have the nine different kinds of Schur complements, we assign them to the different cells taking into account its relative position as in Figure 3. Then, we apply the three-step solver detailed in Section 2.2 solving the surface problem and then propagating this surface solution to the interior of each cell. Note that this approach also allows to introduce additional Schur complements when we have defects on the periodic grid or variations in some cells.
## 4 Discussion  
We investigated how the relationship between spectral indices and foliar macro- and micronutrient contents in potato plants was affected by canopy development under the action of different fertilization and water stress treatments, combining observations at the leaf and canopy scales. By doing so, we could compare the performance of different indices and elucidate possible mechanisms that underpin the relationships at the leaf and canopy levels (Figure 2). We measured foliar nutrient contents, pigments, SLA, and FVC as well as leaf and canopy spectral indices from four nutrient treatments and two water stress treatments at two different points in time, representing different states of canopy development and stress conditions. Our hypothesis was that through their more direct coupling to PAR absorption and photoproduction, ChlF and photoproduction-related indices would be more strongly correlated with foliar nutrient contents compared to vegetation indices related to plant greenness or chlorophyll content.  
Results indicated that photoproduction-related indices such as the PRI and CCI, as well as F685 and the fluorescence Fratio, were more strongly correlated to foliar nutrient content dynamics compared to greenness-based indices when observed at the canopy scale and when observations across different stages of canopy development were pooled together (Figure 7). Furthermore, spectral indices, and in particular ChlF and photoproduction indices, had the potential to track variation across a wider range of foliar nutrients beyond the widely characterized N. These relationships were mediated by the dynamics in foliar pigment contents and leaf morphology (when observed at the leaf scale), as well as canopy structure (when observed at the canopy scale). In the following sections, we discuss the possible mechanisms underlying the observed relationships at the leaf and canopy scales, as well as possible steps towards future research.
## 3 Experimental Data
### The Design of Image Damaged Datasets  
In this study, none of the four public datasets used were constructed with image datasets with damaged finger vein information to simulate the problems encountered in practice. The performance of the network in the finger vein image damage inpainting restoration task depends heavily on the design of the training dataset. The designed dataset should be maintained to be as close as possible to the actual application; otherwise, the generalization ability of the network will be reduced. Therefore, in this study, we build the training dataset by using a random mask to simulate the damaged images, which is a more common method of building damaged images [44].  
**Training dataset.** To continue this study, we designed the appropriate method to build a dataset of damaged finger vein information to simulate the actual problem. In real-world application situations, the size of the damaged region of the finger vein often does not cover more than half of the image size; therefore, in this study, the limit of the size of the constructed damaged region is set to half of the image size. Therefore, to make the network model able to cope with different locations and sizes of damage, and to improve the generalization ability of the network to match the real situation more closely, damaged blocks were designed to be used in the finger vein images, where the location of the damaged area and the size of the damaged area were both randomized. Simultaneously, we used the feature extraction method of maximum curvature to produce feature image datasets as vein texture structure features for the first phase of network training. Following the above approach, we built the training data for the simulated defective images on the public four finger vein dataset.  
**Testing dataset.** The test dataset is independent of the training dataset, and, therefore, the test dataset must contain a variety of damage cases to ensure the reliability of the results. To test the ability of the trained network to repair finger vein images, in this study we created the test dataset according to the location of the damaged area of the finger image and the area of the damaged area. The damaged areas were divided into the left, middle and right sides of the finger image, and the damaged areas were divided into 20 \(\times\) 20, 40 \(\times\) 40, 60 \(\times\) 60, 80 \(\times\) 80, 100 \(\times\) 100, and 120 \(\times\) 120.
## 4 Simulation Environment
### Parameters and Network Topology for Simulation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Definition** \\ \hline Area & 100 m \(\times\) 100 m, 100 m \(\times\) 40 m \\ Simulation time & 5000 s \\ \# of router nodes & 6 (linear), 36 (grid, random), 72 (random) \\ Transmission range & 20 m \\ \(d_{0}\) & 16 m \\ Mobility model & Random waypoint model \\ Mobile node speed & 1.25 m/s\(\sim\)2.5 m/s \\ \(e_{elec}\) & 50 nJ/bit \\ \(\varepsilon_{f_{S}}\) & 10 pJ/bit/m\({}^{4}\) \\ \(\varepsilon_{mp}\) & 0.0013 pJ/bit/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. In the last experiment, the energy consumed by the mobile node in selecting a parent node was measured. Since mobile devices in most practical environments operate on batteries, the lifetime of a device is increased by reducing energy consumption. In this paper, the mobile node is also assumed to be a device operating on a battery. Energy consumption was measured based on the number of transmissions and receptions of messages used to select a parent node. The energy model for the measurement of energy consumption was based on Equation (8) in Section 4.2, and the value \(d_{0}\) was set to 80% of the transmission range of the mobile node. Here, the \(d_{0}\) means a distance from mobile node until a location that the signal strength on RSSI is sharply decreased [23].
## 4 Conclusions  
1. The numerical simulation results show a high linear correlation between the strain of the built-in FBG string and the curvature at the bend. The linearity is up to 0.9998, which is consistent with the theoretical results and verifies the theoretical rationality of the sensor design.
2. Based on the curvature sensing principle of FBG, the 3D curvature sensor of FBG was designed and developed, the 3D spatial curve reconstruction algorithm based on the curvature information in the orthogonal direction was derived, and the 3D bending test platform was built for verification according to the actual bending morphological characteristics of the scraper conveyor on the working face. The test results show that, in the case of the three-dimensional curved shape layout of the scraper conveyor, the measured curve is basically consistent with the perceived curve, and the error of each coordinate axis is generally no more than 15 mm, which can meet the detection accuracy requirements of the straightness of the intelligent working face.
3. The three-dimensional curvature sensor of FBG can adapt to the three-dimensional bending shape of different scraper conveyors and shows good stability of repeated tests.  
Figure 9: Comparison curves of measured and perceived trajectories in three-dimensional linear and bending states. (**a**) The three-dimensional linear. (**b**) 3D bending state 1. (**c**) 3D bending state 2.  
Three-dimensional curvature sensor of FBG can be used for the real-time perception of the three-dimensional bending shape of the scraper conveyor.
* The research results can greatly promote the intelligent process of a coal mine and provide a new idea for realizing the perception and control of the straightness of the scraper conveyor of an intelligent working face.  
Conceptualization, G.W. and X.F.; validation, G.W., F.Q., X.F., M.L. and Y.S.; data curation, G.W.; writing--original draft preparation, G.W. and F.Q.; writing--review and editing, G.W., F.Q., X.F., M.L. and Y.S.; project administration, G.W. and X.F.; funding acquisition, G.W. and M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 52104167 and 52004273), the Natural Science Foundation of Jiangsu Province (No. BK20200639), the China Postdoctoral Science Foundation (No. 2019M661992), the Fundamental Research Funds for the Central Universities (No. 2020 ZDPY0209) and the Open Competition Mechanism to Select the Best Candidates Foundation of Shanxi Province (No. 20201101005).  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
### Modified Rel Model and Proposed Piecewise Approach
The \(d_{break}\) distance indicates the point where the signal passes from complex effects such as reflection and diffraction to free-space propagation,and the signal propagation data are divided into two segments according to this distance. In Region1, the Modified REL model, which includes reflection and diffraction effects that we developed within the scope of this study, is used, whereas in Region2, the propagation data are represented more accurately by the log-distance model. The initial distance value of Region2 was taken as the \(d_{0}\) value of the log-distance model, and the first RSSI value corresponding to the \(d_{break}\) point of the Modified REL was taken as the \(PL(d_{0})\) parameter. For Region2, \(n\) was estimated using these \(d_{0}\) and \(PL(d_{0})\). Thus, Modified REL was used for Region1, and the log-distance model was used for Region2. As a result, this approach provides a more accurate representation of complex propagation characteristics and shows that modeling the measurement data as two pieces instead of one yields more successful results. This piecewise modeling method provides higher agreement with the measurement data, especially when factors such as environmental changes, rough surfaces, and atmospheric conditions are considered.
## 3 Improved Algorithm
### Improved Thresholding Function  
Conventional wavelet thresholding functions have obvious shortcomings. The hard thresholding function is discontinuous at the threshold, and the reconstructed signal will generate additional oscillation. The soft thresholding function compresses the wavelet coefficients larger than the threshold, which can easily cause signal distortion. Both of these conventional thresholds lack adaptability [33]. The following hard and soft thresholding function formulas are given as [34].  
\[\omega_{hard}=\left\{\begin{matrix}\omega,\ |\omega|>T\\ 0,\ |\omega|<T\end{matrix}\right. \tag{11}\]  
\[\omega_{soft}=\left\{\begin{matrix}\text{sgn}(\omega)(|\omega|-T),&|\omega|\geq T \\ 0,&|\omega|<T\end{matrix}\right. \tag{12}\]  
In order to overcome the shortcomings of conventional wavelet soft and hard thresholding, we improved the thresholding function as follows:  
\[\overline{\omega}_{j}^{k}=\left\{\begin{array}{cc}\lambda_{j}^{k}\omega_{j} ^{k}+\left(1-\lambda_{j}^{k}\right)\text{sgn}\Big{(}\omega_{j}^{k}\Big{)} \Bigg{(}\left|\omega_{j}^{k}\right|-\frac{T_{j}}{e^{\sqrt{|\omega_{j}^{k2}-T_{j }^{2}|}}}\Bigg{)},&\left|\omega_{j}^{k}\right|\geq T_{j}\\ 0,&\left|\omega_{j}^{k}\right|<T_{j}\end{array}\right. \tag{13}\]  
\[\lambda_{j}^{k}=1-e^{-\frac{\sqrt{\left|\omega_{j}^{k2}-T_{j}^{2}\right|}}{ \alpha}} \tag{14}\]  
where, \(\omega_{j}^{k}\) represents the wavelet coefficient sequences of different frequency bands, \(\overline{\omega}_{j}^{k}\) represents the adjusted wavelet coefficients. \(\lambda_{j}^{k}\) is a scale factor, which can adjust the change trend of the thresholding function between soft and hard thresholds. \(\alpha\) is an adjustment parameter, \(\alpha>0\).