esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Channel Statistics
### Definition of Angular Spread Quantifiers
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & **Quantifiers and Measurements Details** & **Angular Spread**[10] & **True Standard Deviation**[16; 17] & **Angular Construction**[10] & **Direction of Maximum Fading**[10] \\ \cline{3-8}  & **Mathematical Expression:** & \(\Lambda_{\theta}=\sqrt{1-|R_{1}|^{2}}\) & \(\sigma_{\theta}=\sqrt{-2\ln(|R_{1}|)}\) & \(\gamma_{\theta}=\frac{|R_{2}-R_{1}^{2}|}{1-|R_{1}^{2}|}\) & \(\theta_{\text{MF}}=\frac{1}{2}\text{phase}\{R_{2}-R_{1}^{2}\}\) \\ \cline{2-8}  & Range & of Quantifier: & 0\(\sim\)1. & 0\(\sim\)2\(\pi\) (\(\theta^{*}\)\(\sim\)360\({}^{\prime}\)) & 0\(\sim\)1 & \(-\pi\sim\pi\) (\(-180^{\prime}\)\(\sim\)\(+180^{\circ}\)) \\ \cline{3-8}  & & & Denotes concentration of multipaths around a single direction. 0 indicates the concentration of energy around each one path, while 1 indicates no bias. & Denotes measure of energy dispersion in angular domain. & Denotes measure of multipaths about two directions. 1 indicates the concentration of energy about exactly two paths, while 0 indicates no bias. & Denotes the physical direction of maximum fading in radians. \\ \hline  & Tx-Rx separation & \(<\) 150 m & 0.9829 & 105.37\({}^{\circ}\) & 0.163 & 17.59\({}^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(>\) 150 m & 0.955 & 89.6\({}^{\circ}\) & 0.0941 & \(-1.99^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(>\) 250 m & 0.891 & 72\({}^{\circ}\) & 0.0698 & \(-13.72^{\circ}\) \\ \hline  & Tx-Rx separation & \(>\) 250 m & 0.8463 & 64.30\({}^{\circ}\) & 0.7297 & \(-32.33^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(0.8789\) & 69.7\({}^{\circ}\) & 0.74 & \(-28.5^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(0.9077\) & 75.50\({}^{\circ}\) & 0.6605 & \(-28.65^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(0.767\) & 54\({}^{\circ}\) & 0.705 & \(-33.9^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(0.969\) & 96.04\({}^{\circ}\) & 0.322 & 88.8\({}^{\circ}\) \\ \cline{3-8}  & Tx-Rx separation & \(0.619\) & 39.82\({}^{\circ}\) & 0.852 & 32.1\({}^{\circ}\) \\ \hline  & LoS Scenario (Figure 8b of [6]) & 0.9049 & 74.89\({}^{\circ}\) & 0.6834 & \(-3.27^{\circ}\) \\ \cline{3-8}  & NLoS Scenario 1 (Figure 8e of [6]) & 0.7906 & 56.75\({}^{\circ}\) & 0.3881 & 77\({}^{\circ}\) \\ \cline{3-8}  & NLoS Scenario 2 (Figure 8h of [6]) & 0.4125 & 24.74\({}^{\circ}\) & 0.9212 & \(-72^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantification of Angular Spread for mmWave outdoor and indoor radio propagation channels.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.5 Flexible Fog Computing-Based Sensor Node  
The node based on flexible Fog computing [36] offers flexibility since it can be used in several IoT applications such as smart buildings, smart factories and smart ports. Regarding the processor and memory specifications, the node utilizes an Arm(r) Cortex(r)-M4 MCU(CC3220 MODASF12) at 80 MHz. It contains 256 kB of RAM and 1 MB flash memory for accommodating data and configuration parameters in the mote.  
The LoRa transceiver (RN2483) and Wi-Fi module (CC3220 MODASF12 MONR) are utilized to provide the wireless link for the nodes. The Wi-Fi module operating at 2.4 GHz is chosen for short-range (up to 100 m) communication while the LoRa transceiver is selected for long-range coverage of the order of kilometers. In addition, the node consists of an on-board three-axis accelerometer (ADXL355), a pressure/humidity/temperature sensor (BME280) and an inductance sensor (LDC1000) for detecting corrosion levels.  
The node relies on a 3.6 V and 5800 mAh battery for power supply. Concerning the power consumption, at 3.3 V, the LoRa module consumes 420 mA while the system-on-module (SOM) draws 800 mA. With a battery capacity of 5800 mA/h, an approximately 1.7 year lifetime of the sensor node is reported.
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 3 Uncertainty Analysis of Spherical Joint Space Rotation Measurement System
### Uncertainty Caused by Ball Head Eccentricity  
The eccentricity error is caused by the non-coincidence between the center of the rotation of the measuring system and the center of the ball-hinge prototype. The eccentricity error would cause the deviation of the actual distance between the probe of the eddy current sensor and the surface of the ball head, bringing additional variation to the sensor signal. In Figure 6, let \(\varphi_{1}\) be the rotation angle of the ball head prototype with a theoretical ball center and \(\varphi_{1}\) be the rotation angle of the ball head prototype with an eccentric ball center, with the radius of the ball head r = 50 mm.  
The simplified eccentricity error formula is shown in Equation (5) [24].  
\[\frac{\sin\Delta\varphi}{\sin\varphi_{2}}=\frac{\mathrm{e}}{\mathrm{r}} \tag{9}\]  
It can be written as:  
\[\Delta\varphi=\arcsin(\frac{\mathrm{e}}{\mathrm{r}}\mathrm{sin}\varphi_{2}) \tag{10}\]  
Equation (6) shows that the eccentricity error changes sinusoidally when \(\varphi_{2}=90^{\circ}\) or \(270^{\circ}\). The absolute value reaches its maximum:  
\[\Delta\varphi_{\max}=\arcsin\frac{\mathrm{e}}{\mathrm{r}} \tag{11}\]  
The eccentricity of the three-dimensional rotation of the joint is measured by the dial meter. The rotation deviation of the ball head within the rotation range of the X, Y axes and the ball joint rod is 39 \(\upmu\)m, 38 \(\upmu\)m, and 23 \(\upmu\)m, respectively. The value also includes the roundness error of the ball head, that is, the real eccentricity error is less than the value. However, for the convenience of analysis, the maximum eccentricities around the X, Y axes and the ball joint, which are \(\pm 2^{\prime}41^{\prime\prime}\), \(\pm 2^{\prime}37^{\prime\prime}\), and \(\pm 1^{\prime}35^{\prime\prime}\), respectively, can be directly calculated according to Formula (7). The uncertainty u\({}_{2}\) follows the arcsine distribution, and the interval half-width is the absolute value of the maximum eccentricity.  
\[\mathrm{u}_{2}(\alpha)\mathrm{\sim}\arcsin(-2^{\prime}41^{\prime\prime},2^{ \prime}41^{\prime\prime}) \tag{12}\]  
\[\mathrm{u}_{2}(\beta)\mathrm{\sim}\arcsin(-2^{\prime}37^{\prime\prime},2^{ \prime}37^{\prime\prime}) \tag{13}\]  
\[\mathrm{u}_{2}(\gamma)\mathrm{\sim}\arcsin(-1^{\prime}35^{\prime\prime},1^{ \prime}35^{\prime\prime}) \tag{14}\]
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Comparative Analysis of MAC Layer-Based UWAC Full-Duplex Protocols
#### 3.4.1 Throughput  
In the realm of contention-free protocols, the FD-FH-CDMA technique outperforms the simple FD-CDMA technique because it effectively mitigates multipath fading and frequency selective fading, resulting in high throughput. Thanks to FDMA's ability to isolate the channel and perform error-free simultaneous communication [46], the combination of FDMA and hybrid CSMA also performs better. The BD-FD-MAC [36] protocol is a single hop network with DCF and back-off timer communication features, which helps to reduce unexpected delays and shorten the time it takes for data to be successfully transmitted. The BD-FD-MAC achieves a high throughput curve as the number of UW sensor nodes grows in size. The IBFD-MAC [35] protocol uses receive and forward technique to improve data rate and throughput. However, due to the presence of the SI effect, as the net-work load increases, the throughput gradually decreases. In comparison to FS-MACA [37], its efficiency is similar due to the future scheduling technique, but FC-MACA maintains a consistent high throughput under heavy traffic. In the future, a successful SI cancellation technique can improve IBFD-throughput MAC's even more. To prevent unintended collisions, the FD-DA-CSMA [30] uses two different channels to send and receive control and data packets at the same time, resulting in higher throughput. FDCA-MAC [34] creates a collision-free map that can determine if the current communication channel conditions are suitable for collision-free transmission. Throughput rises by 30% and 60% in single hope and multi-hop network tests, respectively. Two and fold is another advanced technique for the construction of FD communication operation in the TFH-MAC protocol [69] for increasing throughput. In addition, the IBFD communication method has been implemented to remove the self-loop and SI in FD communication, thereby improving throughput gain. The E-FD-MAC [38] protocol is tested on a single hop network that uses Short Inter-Frame Space (SIFS) and back-off timer-based communication capabilities to minimize unexpected delays and speed up data transmission. The E-FD-MAC [38] achieves a high throughput curve as the number of UW sensor nodes grows in size using this method. FD-fixed time stamp hand-shake and time-stamp based dispute resolution are two revolutionary strategies used in random access based FDCP-MAC [39] to improve the protocol's throughput and reliability. Even with a high network load, these techniques maintain efficiency.
## 2 Development of the Pre-Tuning Machine
### Equivalent Circuit Tuning Algorithm  
The equivalent circuit model simplifies complex electromagnetic problems into circuit analyses [18]. It represents the electromagnetic field distribution in the superconducting cavity as interactions between inductance and capacitance. Using this model, the TM010-\(\pi\) mode in multi-cell superconducting cavities can be effectively simulated as a resonant mode. In this approach, the equivalent circuit model can replace the distributed parameter circuit model. Figure 5 illustrates the equivalent circuit model for a six-cell superconducting cavity. The electromagnetic behavior of each unit can be modeled using an \(LC\) equivalent circuit, where \(L\) and \(C\) represent the equivalent inductance and capacitance of each unit, respectively [19; 20]. Energy exchange occurs between adjacent cells through coupling capacitors. The resonant frequency of the cavity, along with its inductance and capacitance, is related by the following equation:  
\[f=\frac{1}{2\pi}\sqrt{\frac{1}{LC}} \tag{5}\]  
For multi-cell cavities, the resonant frequency of each cell depends on its intrinsic inductance and capacitance, as well as coupling effects from neighboring cells. Coupling effects cause the overall cavity frequency to deviate from the independent resonant frequencies of individual cells. This phenomenon can be quantified by introducing a coupling coefficient. Introducing coupling capacitance \(C_{k}\) further corrects the frequency of each cell. The coupling capacitance changes the effective capacitance of each unit in the equivalent circuit, resulting in a change in the resonant frequency. For the resonant frequency of each unit \(\omega_{n}\) and the electric field distribution of each unit \(I_{n}\), \(\omega_{0}\) is the cavity's measured resonant frequency; since the coupling coefficient between middle units differs by less than 1%, the effect on the circuit distribution \(\omega_{n}^{2}\) can be ignored, simplifying the coupling  
Figure 4: Coaxiality testing assembly.  
Figure 5: Six-cell elliptical superconducting cavity equivalent circuit model.  
adjustment is identified, the cavity is moved to the position of the fixed clamps. These fixed clamps are equipped with three lead screws, displacement sensors, and motors arranged in a triangular configuration, as shown in Figure 6. Under motor control, the distance and angle between the two fixed clamps can be adjusted, thereby adjusting the state of the unit cavity to be tuned and completing the pre-tuning process. Once tuning is complete, the clamps disengage and the cavity is repositioned to adjust subsequent units. This process is repeated until all unit cavities are tuned.  
The adjustment of the fixed clamps is driven by stepper motors, which can be automated through programmatic control. Once the pre-tuning is completed, the high-frequency characteristics of the cavity can be measured using the bead-pull method. If the test results do not meet the expected criteria, the pre-tuning process is repeated. This cycle continues until the desired performance metrics of the cavity are achieved.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 2 Preload and Rigidity of Linear Guide
### Rigidity of Linear Guide  
Figure 3 illustrates the relationship between the applied load and vertical deflection of the linear guides. As observed in the figure, the high-preloaded linear guides showed less deflection than those with a lower preload under the same force. In addition, even though the linear guides are rated at the same preload levels, for example, low preload (Z0), the three samples showed little differences in the load-deflection curve. This difference can be ascribed to the fact that the interference between rolling balls and grooves is mainly determined by the oversized rolling balls, which normally have a tolerance in diameters in the range of 0-2 \(\upmu\)m. In addition, the interference can be partly affected by internal clearance, the geometric tolerance of components, and assemblage precision. The rigidity or stiffness against vertical loading can be obtained from the load-deflection relationship. For example, for a high-preloaded linear guide, the force \(F_{\
u}\) can be related to the deflection \(\delta_{\
u}\), and can be expressed as a power-low function by a curve fitting technique:  
\[F_{\
u}\ =\ 0.515515\times\delta_{\
u}^{0.98465} \tag{1}\]  
The rigidity or stiffness \(K_{\
u}\) can be obtained by taking a derivative of Equation (1) with respect to the deflection \(\delta_{\
u}\),  
\[K_{\
u}\ =\ \frac{dF_{\
u}}{d\delta_{\
u}}\ =\ 0.50768\times\delta_{\
u}^{-0.015 35} \tag{2}\]  
\[K_{\
u}\ =\ 0.50238\times F_{\
u}^{-0.01559} \tag{3}\]  
Figure 2: Experimental setup of compression test of linear guide.  
The above equations show that the stiffness of the linear guide is dependent on the force acting on the linear guide, with nonlinear characteristics. At the initial preload state, the rigidity of the linear guide can be calculated according to the experimentally measured stiffness formula, as listed in Table 2. It was found that the rigidity of the linear guide changes with the preload induced in the rolling balls. It can be expected that a slight variation in the interference may affect the rigidity of the linear guide deviating from the initial setting during operation.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Significant Wave Height (SWH) Verification
#### 3.3.2 Comparison of SWH among Multiple Satellites  
This study validated the HY-2B satellite's SWH data quality using Jason-3 satellite data. There were 1052 spatio-temporal matched points. As can be seen in Table 3, the mean deviation, standard deviation, and root-mean-squared error of the cross points were \(-\)0.0028 m, 0.17 m, and 0.17 m, respectively. The SWH measurements in the HY-2B Ku band were slightly higher than those of the Jason-3. However, there was significant agreement between the SWH of the HY-2B and Jason-3 satellites.  
This study used the HY-2B data to verify the quality of the HY-2C and HY-2D SWH data. Table 3 shows the spatial and temporal matching of 1077 and 758 points, respectively, with mean deviation of \(-\)0.06 m and 0.17 m and standard deviation of 0.14 m and 0.25 m, respectively. The densest points were concentrated in the SWH of 1 to 6 m, and the numbers of SWH for the 0.5 to 1 m and 7 to 10 m ranges were minimal. The differences between the SWH became larger as the SWH values increased. There is a significant agreement between the SWH of HY-2B and HY-2C/D satellites. In addition, the quality of the HY-2C satellite's SWH data was higher than that of the HY-2D satellite.
## 2 Materials and Data
### Meteorological Data  
In this study, we collected from TerraClimate datasets a dataset of high spatial resolution (1/24\({}^{\circ}\), \(\sim\)4 km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015 [37]. TerraClimate uses climate-assisted interpolation to combine high spatial resolution climate normals from the WorldClim dataset with coarser resolution time-varying (i.e., monthly) data from other sources to generate monthly datasets of precipitation, maximum and minimum temperatures, wind speed, vapour pressure, and solar radiation. This dataset has been widely used by many scholars to calculate various drought indices to assess the effects of drought on vegetation physiological activity and yield [38; 39; 40; 41]. The main climate variables chosen for this paper include precipitation (pr), maximum temperature (tmmx), downgradient surface shortwave radiation (srad), and the Palmer drought index (Pdsi).
# The Objective Space and the Formulation of Design Requirement in Natural Laminar Flow Optimization
## 2 CFD Solver and Its Validation
### Realization of Algorithm  
Since the purpose of this paper is to study the impact of objective formulation on the result of aerodynamic design, the realization of optimization algorithm in different cases must be kept in similar configuration. The algorithm starts with a randomly populated initial generation of \(NP\)\(D\)-dimensional parameter vectors representing candidates with considerations of aforementioned constraints in the design and objective space, where \(NP=15\) is the number of parents in a population and \(D=4\) is the number of parameters in this paper. After initializing the population, each target vector of candidate in generation undergoes mutation and crossover operation before producing a sample to be tested. The perturbations on the parameters are the factors to be multiplied by the original values (instead of the values to be treated as profile description parameters) thus assuring the uniformity in random seeding. Only when the newly generated candidate is within the various constraint and has better aerodynamic performance measured by conventional/improved optimization criterion  
Figure 8: An improved optimization process with the new formulation of design requirement applied in the optimization.  
(i.e., design requirement) than its parent, then the old candidate is updated with the new one. In this paper, surrogate models are not used to avoid inaccuracy in description of design and objective space.  
The two algorithms with different design requirement are realized in two cases. As is shown in Algorithm 1, \(x\) represents candidate, \(v\) represents guided possible marching step, \(r_{i},i=1,2,3\) represent random candidate in current generation; candidates, constant \(F\) represents scaling factor in amplifying differential steps.  
```
Input: Relevant CST parameters \(A_{0},A_{1},A_{2},A_{3}\) Output: History of candidate's evolution in aerodynamic performance
1 forg=0:Generationsdo
2 fori=0:Candidatesdo
3 Mutation: \(\vec{v_{i,g}}\gets x_{r_{1,g}}^{-}+F\times(x_{r_{2,g}}^{-}-x_{r_{3,g}}^{-})\) ;
4 Crossover: elementwise: abandon vs. adopt variated parameters (probability of 3:7);
5 Performance check (where the comparison takes place);
6
7 end for
8
9 end for Return ();
```  
**Algorithm 1**Differential Evolution in this paper.  
The randomness in the evolutionary marching of both cases is minimized to illustrate the comparison of the effect of design requirement, which is the core of this paper. Therefore, the mutation ratio and crossover operation is kept the same for the same serial number of candidates in the same generation in both cases; the selection of samples for obtaining differential vector is the same in terms of the serial number of samples (e.g., Nos. 3, 7, 11 are picked for generating differential vector in both cases, although they represent totally different locations in the objective space.).
## 3 Results
### Effect of Operating Parameters on Specific Energy of R\({}_{\rm VA}\)
#### 3.1.3 Effect of \(u/v\) Ratio  
The speed ratio \(u/v\) had a significant effect on SPE (F\({}_{2,80}\) = 3.72, \(p\) = 0.03065) along with the interaction effect of the \(u/v\) ratio and DO (F\({}_{4,80}\) = 2.65, \(p\) = 0.0429). SPE decreased in the ranges of 35 to 10% when the \(u/v\) ratio increased from 1.7 to 2.2. The variation of SPE  
Figure 7: Effect of depth of operation, cone index and \(u/v\) ratio on specific energy.  
with the \(u/v\) ratio at different DOs and CIs is shown in Figure 7. Increasing the \(u/v\) ratio from 1.7 to 2.2 resulted in SPE requirements between ranges of 0.19 to 0.08 kWh ha\({}^{-1}\), 0.31 to 0.24 kWh ha\({}^{-1}\), and 0.44 to 0.32 kWh ha\({}^{-1}\) at DOs of 20, 40, and 60 mm, respectively, and CI of 300, while when the \(u/v\) ratio changed from 1.7 to 3, SPE decreased from 0.35 to 0.30 kWh ha\({}^{-1}\), 0.55 to 0.49 kWh ha\({}^{-1}\) and 0.73 to 0.69 kWh ha\({}^{-1}\) at selected DO ranges and soil CI of 500 kPa.
## 2 Measurement System and Basic Measurement Principle
\[z_{ball\_B}=z_{slide}+\Delta z_{ball\_B} \tag{4}\]  
where \(y_{ball\_B}\) and \(z_{ball\_B}\) are the \(Y\) coordinate and \(Z\) coordinate of the probing sphere of Probe B, \(y_{slide}\) and \(z_{slide}\) are the \(Y\)-slide position and \(Z\)-slide position obtained from the four-axis motion platform, and \(\Delta z_{ball\_B}\) is the \(Z\)-displacement of the probing sphere obtained as the output of Probe B. It should be noted that the probing sphere of the axial gauge can only move in the \(Z\)-direction, and the displacement in the \(Y\)-direction caused by the elastic deformations is neglected. The relative displacement in the \(Y\)-direction is not involved in Equation (3). Similar to the measurement of the sidewall profile, the inner underside profile of the workpiece can be evaluated based on the scan trajectory of the probe and the information on the probing sphere radius. Finally, a point cloud registration algorithm is implemented to stitch the inner sidewall profile measured by the lateral displacement gauge and the inner underside profile measured by the axial displacement gauge. The proposed measurement method makes it possible to evaluate the entire inner profile of the high aspect ratio aspheric workpiece with two displacement gauges on a four-axis motion platform. However, the measurement results contain various errors, including the miscalibration, misalignment and roundness error of the probing sphere of the two probe gauges, as well as the slide motion error of the four-axis motion platform, as shown in Figure 4. To obtain an accurate measurement result, attention should be paid to evaluating, separating and compensating for the errors in the measurement system.