esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers
## 2 The Related Tuning Methods
### Setting PID Controllers Parameters
According to our experience, the reasonable selecting region of \(m\) is generally \((0.2,2)\). One other thing to note is that a greater \(m\) is needed when the model is imprecise. Combine Equation (2) with Equation (5) and do a Laplace transform, and the following equation is obtained:  
\[\hat{f}=\frac{k}{s+k}f. \tag{17}\]  
Its derivation is shown in Appendix B. As shown in Equation (17), the response rate of the disturbance observer speeds up with \(k\) increasing. So, the work point should be chosen nearby the \(q\)-axis in order to make \(k\) as large as possible because \(k=q/p\) in the \((q,p)\) plane. The work point is chosen as follows. First, substitute \(q=0\) into Equation (14), and the following equation is obtained. \[\left\{ \begin{array}{l}(h_{0}-2m\omega h_{1})\\ =-\frac{m\sin(\varphi_{0}(m,\omega))+\cos(\varphi_{0}(m,\omega))}{M_{0}(\varphi _{0}(m,\omega))}\\ \omega h_{1}p=\frac{\sin(\varphi_{0}(m,\omega))}{M_{0}(\varphi_{0}(m,\omega) )}\end{array} \right.. \tag{18}\]  
Solve Equation (18), and \(p_{q=0}\) and \(\omega_{q=0}\) can be obtained. Then, define  
\[p^{*}=p_{q^{*}q^{*}}/(10\,\sim 50) \tag{19}\]  
and substitute \(p^{*}\) into Equation (14), so \(q^{*}\) and \(\omega^{*}\) can be obtained. The point \((q^{*},p^{*})\) is the work point in the \((q,p)\) plane. Finally, substitute \(p^{*}\) and \(q^{*}\) into Equation (13), and the controller parameters are obtained. 1. Obtain the system model and choose proper \(h_{0}\), \(h_{1}\) and \(m\) values according to the requirements of system;
2. Solve Equation (18), and then get \(p_{q=0}\) and \(\omega_{q=0}\);
3. Get \(p^{*}\) from Equation (19), substitute \(p^{*}\) into Equation (14), and then get \(q^{*}\) and \(\omega^{*}\);
4. Substitute \(p^{*}\) and \(q^{*}\) into Equation (13) to calculate the controller parameters. If there are closed-loop poles outside of the sector area, decrease both \(h_{0}\) and \(h_{1}\) simultaneously or only \(m\), and then turn to step (2); otherwise, continue;
5. Do the step response and calculate the settling time and overshooting. If the settling time is not met, then increase both \(h_{0}\) and \(h_{1}\) or decrease \(m\), and turn to step (2); if the overshooting is too great, then decrease both \(h_{0}\) and \(h_{1}\) or increase \(m\), and turn to step (2); otherwise, continue;
6. End.
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications  
[PERSON]  
1 The Intelligent Manufacturing Electronics R&D Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (P.  
overtone crystal oscillators are not widely used, they can offer higher frequencies by operating the crystal oscillator at the overtone rate. The quality factor of crystal oscillators is much higher than that of ordinary LC circuits, generally exceeding 10,000 [17], making them a favorable replacement for LC oscillators. Additionally, the coupling in the crystal to the external circuit must be weak, \(C_{0}\gg C_{q}\) resulting in a small access factor of \(p\approx C_{q}/C_{0}\) in Figure 1, which further enhances the exceptional frequency stability of quartz crystals. Therefore, crystal oscillators are commonly utilized to provide a highly stable and fixed reference frequency or clock.  
In modern electronic information systems technology, phase-locked loops are widely used to generate the required high-frequency clock, and crystal oscillators are used to generate low-noise and low-frequency reference clocks. The XO phase noise can have a considerable impact on the in-band noise of PLL. Therefore, it is essential to have XO circuits that can achieve low phase noise for designing high-performance PLLs and transceivers.  
The keys to achieving low phase noise in crystal oscillators are a reduction in noise due to power supply fluctuations and 1/f flicker noise in amplifiers and resonators [18], and the amplifier amplitude modulation to phase modulation (AM to PM) transition due to the nonlinear effects within the crystal [19; 20]. Paper [21] has discussed various types of oscillators, where the requirement for high Q and adequate suppression of 1/f flicker phase-type noise is emphasized. A number of oscillator topologies are also discussed, including Pierce, Miller, Butler, and bridged T-configurations. In [22], a LPF (low pass filter) was added to the LDO input to reduce the contribution of supply noise to XO noise, but this circuit does not specify a way to prevent the bandgap reference circuit from entering the simplex state and its phase noise is only \(-153.1\) dBc/Hz at 100 kHz offset. In fact, few papers (if any) have shown a 40 MHz crystal oscillator complete design with phase noise near or below \(-160\) dBc/Hz at 100 kHz offset.  
This article will present a low phase noise crystal oscillator circuit that incorporates a fast start-up bandgap reference to meet the requirements of such applications. This paper first describes the background of crystal oscillator circuit research, and then analyzes the bandgap reference with start-up circuit in Section 2.1. In addition, we analyze the noise contribution of the crystal oscillator circuit and explain the design of the crystal oscillator and LDO circuit in Section 2.2. The third chapter is devoted to the analysis and comparison of the simulation and test results of the crystal oscillator. Finally, the last two chapters are the discussion and conclusion of this design.
## Materials and Methods
### Model Simulations  
Simulated canopy reflectance data were generated with the PROSAIL model [24], composed of the PROSPECT-5 [31; 32] leaf optical model and the SAILH [33] canopy reflectance model. PROSPECT-5 simulates the hemispherical reflectance and leaf level transmittance by using Cab, the leaf carotenoid content, leaf dry matter content, leaf water content, leaf brown pigment content, and the leaf mesophyll structure parameter. SAILH additionally requires LAI, MTA, the solar zenith angle, sensor viewing angle, azimuth angle, the fraction of diffuse solar illumination, soil reflectance, and the hot-spot size parameter. We ran PROSAIL 100,000 times with input values drawn from the uniform distributions given by values of field measurements and the literature. Based on field measurements, we varied Cab between 25 and 100 ug cm\({}^{-2}\), LAI between 1 and 5, MTA between 15\({}^{\circ}\) and 70\({}^{\circ}\), and the leaf water content between 0.001 and 0.020 cm. The leaf mesophyll structure parameter was fixed to 1.55, the average value of various crop species [34], and the leaf dry matter content to 0.005 g cm\({}^{-2}\), a value suitable for the six studied species [35; 36; 37; 38]. The leaf carotenoid content was linked to Cab with the ratio 1:5 based on LOPEX93 data [39]. The brown pigment content was set to 0, assuming that the leaves were green during the measurement. The fraction of diffuse radiation was calculated with the 6S atmosphere radiative transfer model [40] using the input data derived from the image itself and the nearby sun photometer measurements. The hot-spot size parameter had a negligible effect on the simulation due to the observation geometry (sufficiently far from backscatter, or the hot spot) and was set to a reasonable value for a vegetation canopy (0.01). The view and illumination geometry parameters in the model were set to coincide with airborne measurement conditions (solar zenith angle 49.4\({}^{\circ}\), sensor zenith angle 9\({}^{\circ}\), and azimuth angle 90\({}^{\circ}\)). The soil reflectance was taken from measurements. A detailed description of the PROSAIL inputs is given by [PERSON] and [PERSON] [12]. The PROSAIL spectral resolution was 1 nm, and it was resampled to correspond to the wavelengths measured by AISA using a Gaussian spectral response function.
# Development and Performance Evaluation of an Integrated Disc Cutter System for TBMs
## 4 Design of the Anti-Looseness Structure of the ICS
### Anti-Loosening Principle of the TCS  
The TCS mainly uses a double nut structure to prevent loosening, which is a frictional loosening prevention method, that is, to prevent loosening by increasing the friction between the threads [26]. A schematic diagram of the anti-loosening structure of the TCS is shown in Figure 9a. First, the main nut is tightened, the bolt pulls the locking block up to lock the cutter shaft, and then tightens the auxiliary nut to prevent the TCS from loosening.
## 3 Model Formulation
### Transition  
The transition probability from one state to another for the POMDP model can be formulated using different methods. When a Markov process defines the states (for a do-nothing action), the degradation process is modeled as either a homogenous or non-homogeneous growth process (pure birth process). [PERSON], [PERSON], & [PERSON] [6] and [PERSON] et al. [19] used the transition intensity to formulate a transition matrix for a continuous time horizon with a non-homogenous growth rate. The method first determined the probability of a degradation state (_Prob(s)_) by computing the ratio of each state to the total number of defects obtained either from ILI or a simulation model such as Monte Carlo (Equation (5)).  
\[Prob(s)=\frac{n_{s}}{N}\;\forall s,\;s\in\;S \tag{5}\]  
where  
\[n_{s}\;=\;\text{the number of defects in state s}\]  
\[N=\text{the total number of corrosion defects detected}\]  
Then, the transition intensity (\(\lambda\)) was calculated by sequentially differentiating [PERSON]'s forward equations for a pure birth process. The equation was solved for a very small time interval (\(\Delta t\)), where a maximum of one state transition can occur. For a pure birth process, the initial transition matrix is defined as the product of transition intensity and time interval (i.e., \(\lambda_{s}\Delta t\)), as shown in Figure 1. This is a square matrix (\(s\) (current state)by \(s^{\prime}\) (succeeding state) where \(s=s^{\prime}\)). An n-step transition matrix (based on the initial transition) determines the transition probability for any inspection time interval t.  
The determination of a small time interval (\(\Delta t\)) value could be subjective. However, a general guideline can be set to create a time interval for only a single state transition. A Poisson distribution defines the number of events (in this case, the transition to another state) for a Markov process (pure birth process). The value of \(\Delta\)t was set to make the probability of having more than one event a sufficiently small value.  
The Markov method can be modified to meet some specific process requirements. For example, [PERSON] et al. (2018) have presented a method for incorporating additional growth-rate factors, such as soil conditions or the surrounding environment for external corrosion (based on empirical data), in the transition probability to describe the degradation level better. As [PERSON] (2018) demonstrated, the Markov method based on the pure birth process can be transposed to create a pure death process and compute the strength of the pipeline instead of the degradation. [PERSON] et al. (2018) proposed an empirical method for formulating the transition probability. This method defined the corrosion states (i.e., deterioration ranges for each state) first, then the transition probability matrix was calculated by categorizing the expected deterioration per year (for the time horizon under consideration) under each state and analyzing these yearly deteriorations under each state independently.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Models and Methods for Sound Transmission Loss Calculation
#### 4.1.7 Hybridization Models  
A combination of two or more models to predict the STL of sandwich or composite DPS can result into a hybridized model. One possible reason for hybridization is to take care of limitations which could arise from a single model adaptation. For example, [PERSON] et al. [119] combined the TMM and the hybrid finite element (FE)-SEA models to give a hybridized TMM-SEA model. In their analysis, two disadvantages of the single TMM were listed. The first is that the TMM model requires high computational time to integrate the transmitted wave over all the possible incident angles. Further, the second is that they are very limited in the computation of the variance of STL. While the hybrid FE-SEA model [120; 121] was able to handle the shortcomings of TMM by computing effectively the mean and variance at low computational cost, they however, are computationally burdensome at higher frequency range. This is because; a very fine mesh is required at high frequency. The limitations of TMM and FE-SEA therefore, led to the authors' [119] hybridized model. Their model enabled the computation of the mean and variance of diffused field STL and also avoided the detailed finite element modelling of the structure. Other hybrid models which have been investigated are the finite element transfer matrix method (FE-TMM) [122; 123] and the patch transfer models [124; 125].
## 3 The Main Parameters and Indices of MOT
### Application Performance Indices
#### 3.4.2 Resonant Frequency  
The trapping process of an MOT is influenced by vibration and noise. Taking a one-dimensional MOT as an example, the variations in the atomic number under different vibration frequencies and laser intensities are illustrated in Figure 6[79].  
Figure 6 clearly shows that the MOT has a resonant frequency of approximately 500 Hz. When the vibration frequency closely matches the resonant frequency, the number of captured atoms is minimized. Conversely, when the vibration frequency deviates from the resonant frequency, the number of captured atoms increases. The resonant frequency is an important indicator for evaluating the impact of vibration and noise on the atomic cooling and trapping process, thus improving the overall device performance. Various methods for measuring the MOT's resonance frequency include transient oscillation, harmonic oscillation, parametric resonance, and cooling laser intensity modulation [79].  
Figure 5: Atomic gravimeter structure diagram.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 5 Analysis of Influencing Factors
### New Tunnel Diameter  
The new tunnel excavation diameters of 2 m, 4 m, 6 m, 8 m, 10 m, and other conditions are chosen for calculation in conjunction with the features of urban corridors, tunnels, and other subterranean projects. The results of the settlement and deformation distribution of existing tunnels are displayed in Figure 13a. The settlement increases as the new tunnel's excavation diameter increases. On the one hand, a larger new tunnel's diameter results in a higher initial ground stress of the stratum in which the tunnel is located, and shield tunneling will destroy the original limit equilibrium state, potentially causing more additional stress. On the other hand, as the diameter increases, the volume of the stratum will decrease exponentially, which further causes additional stress, leading to an increase in the existing tunnel's settlement and deformation.  
The relationship between the diameter of the new tunnel excavation and the maximum settlement deformation of the old tunnel is shown in Figure 13b. The existing tunnel's maximum settlement rises with the new tunnel's diameter, and the two have a nearly positive exponential relationship. The previous tunnel's settling is significantly impacted by the larger tunnel diameter.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 4 The Rainfall Rate Estimation Model
### Derivation of the Rain Height Enhancement Factor  
The values of \(\tau(m)\) were determined by maximizing the agreement between the annual Complementary Cumulative Distribution Function (CCDF) of the rain rate, \(P(R)\), as measured by the disdrometer and as estimated while using the rain rate prediction model; the two curves are reported in Figure 9 using a red and a blue line, respectively. We have obtained a first guess of the coefficients, named \(\hat{\tau}_{0}(m)\), which are listed in the first row of Table 3, as a result of this maximization process. It should be noted that the value of \(\hat{\tau}_{0}(m)\) associated to August (\(m=8\)) is missing, as no propagation data are available due to some issues with the beacon receiver.  
We have investigated the relationship between \(\tau(m)\) and \(\beta(m)\) for the months classified as convective (that is for \(4\leq m\leq 9\)) with the aim of devising a model with a broad applicability. Figure 10 shows that the two quantities follow the same trend, which confirms that the higher the connectivity level of a given month, the higher the increase in the rain height will be. In particular, the product \(f_{p}(m)\) between \(\tau(m)\) and \(\beta(m)\) turns out to be well approximated by the following third order polynomial, which is only function of \(m\):  
\[f_{p}(m)=\tau(m)\beta(m)=-0.0221m^{3}+0.3434m^{2}-1.5079m+2.0982. \tag{13}\]  
As a result, the rain height enhancement coefficient can be calculated starting from the local values of \(\beta(m)\), which can be obtained on a Global basis, as:  
\[\hat{\tau}_{p}(m)=\frac{f_{p}(m)}{\beta(m)}, \tag{14}\]  
The resulting values of \(\hat{\tau}_{p}(m)\) are reported in the second row of Table 3. Figure 9 instead reported the annual \(P(R)\) estimated while using the coefficients \(\hat{\tau}_{p}(m)\) (green line), and compared to the other two curves mentioned above: the agreement between the \(P(R)\)s estimated using \(\hat{\tau}_{0}(m)\) and \(\hat{\tau}_{p}(m)\) is very good. Table 4 also confirms this result, which lists the mean and RMS values of the estimation error \(\epsilon_{r}(P)\), in turn defined as the difference, for each probability level \(P\), between the measured (disdrometer) and estimated (model) rain rate.  
Figure 8: Trend of \(\beta\) with the month \(m\) for Milan.
# Opportunities for Machine Learning in District Heating
### Motivation and Objective  
Our objective was to understand and discuss to what extent the current state-of-the-art ML has addressed the key problems in the DH industry. Knowledge about how advancesin ML have addressed relevant problems in DH is essential to determine any (mis)matches between these two fields. Such knowledge will help establish an anchor for future ML and DH research. By employing a literature review and expert insights, the article aims to serve as a reference to the current state-of-the-art, while identifying gaps, challenges and future opportunities.  
With a market share of 50% in Nordic countries (with limited use of fossil fuel sources), 2-10% in Western European countries (with still a high reliance on fossil fuel sources) or 55% in northern China or Korea and stream systems in the US, it can be expected that adapting old generations of DH systems alongside newer generations will remain a challenge well into the future. The difficulty of adapting generations of DH to meet new demands requires both policy instruments, as well as innovative solutions made available through research. For example, the European Union (EU) is promoting policies related to decarbonisation of heating and thermal processes [13; 14], which forms a key incentive towards utilising alternative fuel sources. ML is mainly driven by the ability to collect and store a vast amount of data and the increasingly available low cost cloud-based computational resources. It has rapidly been adopted in many process industries, but also other areas with easy access to structured data, such as road traffic and health care sectors, and increasingly for processing unstructured datasets, such as images and documents. ML offers several opportunities for problems related to monitoring of operations and optimisation. However, it has become increasingly clear that advances in DH-related ML research have a disproportionate focus on some problem areas, specifically forecasting [15; 16; 17; 18], while missing out on several interesting problems, e.g., substation heat transfer, occupancy comfort, etc.  
By comparing and contrasting knowledge synthesised from the above two fields, we propose a road-map for research in ML and DH, anchored on identified gaps in the current state-of-the-art. Similar recent efforts identified vital underlying factors influencing thermal load pattern forecasting in DH networks, alongside the most efficient ML algorithms applied for District Heating and Cooling (DHC) load/demand forecasting [19]. In the current article, we further look at problems of interest from the perspective of the industry and we propose a road-map for increasing the impact of ML applications in DH.
# Affection Enhanced Relational Graph Attention Network for Sarcasm Detection
## 4 Experiments
### Impact of Stacked Number of RGAT and GCN  
To study the effect of the number of RGAT layers and GCN layers on the performance of the proposed model, we adjust the layer number from 1 to 7 and recode the results on six datasets in Figure 4. We initially set the number of GCN layers to 3 and then adjust the number of RGAT layers from 1 to 7. Note that three RGAT layers perform overall better than other layers, and thus we set the number of RGAT layers as 3. One RGAT layer performs unsatisfactorily on all datasets, which indicates that simple network structure is insufficient to exploit decent sarcastic features. Additionally, when the layer is greater than 3, the performance fluctuates and declines with the increasing number. This implies that increasing the number of RGAT layers is likely to reduce the model's learning capabilities due to the sharp increase of model parameters. Then, we fix the number of RGAT layers as three and vary the number of GCN layers from 1 to 7, and results show that three GCN layers perform the best overall. According to the experimental results, we set the number of RGAT and GCN layers to 3. The depths of RGAT and GCN layers have less impact on large datasets. The accuracy of Ptacek stays in a small range when the number of layers changes. Conversely, it fluctuates heavily in IAC-1 and Riloff datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Reddit (\(\mathbf{v}\)/Movie)**} & \multicolumn{4}{c}{**Reddit (\(\mathbf{v}\)/Technology)**} \\ \cline{2-9}  & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Acc. (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Acc. (\%)** \\ \hline NBOW & 67.33 & 66.56 & 66.82 & 67.52 & 65.45 & 65.62 & 65.52 & 66.55 \\ CNN & 65.97 & 65.97 & 65.97 & 66.24 & 65.88 & 62.90 & 62.85 & 66.80 \\ GRNN & 66.16 & 66.16 & 66.16 & 66.42 & 66.56 & 66.73 & 66.66 & 67.65 \\ CNN-LSTM-DNN & 68.27 & 67.87 & 67.95 & 68.50 & 66.14 & 66.73 & 65.74 & 66.00 \\ \hline ATT-LSTM & 68.11 & 67.87 & 67.94 & 68.37 & 68.20 & 68.78 & 67.44 & 67.22 \\ SIARN & 69.59 & 69.48 & 69.52 & 69.84 & 69.35 & 70.05 & 69.22 & 69.57 \\ MIARN & 69.68 & 69.37 & 69.54 & 69.90 & 68.97 & 69.30 & 69.09 & 69.91 \\ SAWS & 71.79 & 71.77 & 71.76 & 71.77 & 72.50 & 72.45 & 72.48 \\ \hline ADGCN & 74.48 & 74.58 & 74.47 & 74.48 & 75.59 & 75.99 & 75.58 & 75.59 \\ \hline ARGAT (**proposal**) & **75.82** & **75.82** & **75.81** & **75.82** & **76.13** & **76.13** & **76.13** & **76.13** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results on Reddit datasets. The best results are in bold and the results of four basic models are from [6].  
Figure 4: Impact of the number of stacked layers. The left figure fixes the GCN layers to 3 and records the model’s performance with a different number of RGAT layers. The right figure fixes the RGAT layers to 3 and records the results of the model with a different number of GCN layers.