esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Technologies of 260 \({}^{\circ}\)C/210 MPa Ultra-Deep Logging Equipment
## 1 Introduction  
As the difficulty of discovering shallow and medium-depth oil and gas resources in China increases, the exploration and development of deep and ultra-deep oil and gas resources have become crucial for China's future growth in oil and gas reserves and production [1, 2]. In recent years, significant discoveries in deep and ultra-deep layers have been made in basins such as the Tarim, Sichuan, Junggar, and Songliao. For example, in the Tarim Basin, resources buried at depths of 6000 to 10,000 m account for 83.2% of its total oil and 63.9% of its total natural gas, with ultra-deep oil and gas resources comprising about 19% of the global total [3, 4, 5]. Since the \"12 th Five-Year Plan\", the number of deep and ultra-deep wells in China's oil industry have gradually increased. The number of deep wells at a depth of 4500 m have increased by more than 20% annually, the number of ultra-deep wells at a depth of 6000 m have increased by more than 15% annually, and the number of ultra-deep wells at a depth of 8000 m have doubled annually. The number of key exploration wells such as Round Exploration 1 and Round Exploration 3 have exceeded 8500 m. In 2023, China National Petroleum Corporation (CNPC) deployed two 10,000 m deep wells, \"Shen Di Chuan Ke 1\" and \"Shen Di Ta Ke 1\", in the Tamin Oilfield and Southwest Oilfield, respectively. These wells are designed to exceed 10,000 m in depth, with the expected bottom-hole temperatures and pressures reaching up to 225 \({}^{\circ}\)C and 175 MPa. This marks the official entry of China's oil and gas exploration into ultra-deep layers. The high-temperature and high-pressure environment, along with the complex wellborestructures in these ultra-deep layers, pose significant challenges and higher requirements for the stability of logging equipment performance and the reliability of process safety.  
This paper introduces the key technologies of ultra-deep measurement logging equipment, including high-strength mechanical structure, pressure-sealing capabilities, low-power high-integration circuit systems, 13,000 m long cable communication, and ultra-high temperature and pressure transducers. It also presents the solutions for developing 260 \({}^{\circ}\)C/210 MPa ultra-deep logging equipment.
## 4 Theoretical Analysis
### Packet Delivery Probability Estimation  
The acoustic channel attenuation over a distance \(d\) for a signal of frequency \(f\) due to Rayleigh fading is expressed as:  
\[A(d,f)=d^{k}a(f)^{d}, \tag{3}\]  
where \(a(f)\) is the absorption coefficient and \(k\) is the spreading factor which defines the geometry of spreading: \(k=2\) for spherical spreading, \(k=1\) for cylindrical spreading and \(k=1.5\) for practical spreading [3]. The absorption coefficient \(a(f)\) (in dB/km) for \(f\) (in kHz) is calculated using Thorps formula [32] given by:  
\[10\log a(f)=\frac{0.11\times f^{2}}{1+f^{2}}+\frac{44\times f^{2}}{4100+f}+2.75 \times 10^{-4}f^{2}+0.003. \tag{4}\]  
The average signal to noise ratio (SNR) over \(d\) is given as follows:  
\[\Gamma(d)=\frac{E_{b}/A(d,f)}{N_{0}}=\frac{E_{b}}{N_{0}d^{k}a(f)^{d}}, \tag{5}\]  
where \(E_{b}\) represents energy per bit and \(N_{0}\) represents noise power spectral density in an additive white gaussian noise (AWGN) channel. we use binary phase shift keying (BPSK), for which the probability of error over \(d\) is calculated as follows [33]:  
Figure 3: The Re-intar: flow chart.  
\[p_{e}(d)=\frac{1}{2}\left(1-\sqrt{\frac{\Gamma(d)}{1+\Gamma(d)}}\right). \tag{6}\]  
The delivery probability of a packet of \(m\) bits over distance \(d\) between any pair of nodes is given as follows:  
\[p(d,m)=(1-p_{e}(d))^{m}. \tag{7}\]  
Throughout in this paper, we shortly abbreviate the above notation as \(p\left(h_{(j)}\right)\).
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Effect of Wind Power Generation on Transient Stability
#### 4.1.2. For System with Two Wind Farms  
In this scenario, two wind farms are added to this system, one is at bus 1 and the other is at bus 4. As can be seen in generator speed responses in Figure 7, the first peak during the disturbance almost approaches 1.008 p.u. and settles down in 15 s still with some disturbances. Now, if we look at the theta angle response in Figure 8, though the positive and negative first peaks at generator 2 and generator 4 occur at value i.e., +0.5 radian and \(-\)0.9 radian respectively, all the generator angles are fluctuating even at 20 s.  
According to the observation in Figures 5-8, it is obvious that the stability of the system deteriorates dramatically by integrating more wind farms into the existing power grid. This is because with more integration of wind farms, the inertia of the original simultaneous system is reduced and therefore the system is more sensitive to the external disturbance.  
Figure 6: Generator angles for WF at bus 1.  
Figure 7: Generator speeds for WFs at bus 1 and bus 4.
## 3 Calculation Results and Analysis  
As shown in Figures 5 and 6, the oil started to divert after reaching the cone surface of the valve spool due to the deflecting effect of the cone part of the spool [24]. After the mainstream bundle of oil flowed out from the valve orifice, it directly hit the inner wall of the valve body due to its inertia and flowed along the inner wall to the outlet direction. While the oil at the edge of the mainstream bundle flowed out of the valve orifice due to the larger flow-through area, this part of the oil spread around, and the direction of motion changed greatly, forming vortices at the two ends of the mainstream bundle.  
Figure 4: The curve of the maximum erosion rate with the number of particle packets.  
Figure 5: (a) Velocity contours on the axisymmetric plane; (b) velocity pathlines on the axisymmetric plane.  
Due to the small opening of the high-speed on/off valve port, the fluid velocity at the valve port increased rapidly and the maximum velocity reached 44.4 m/s.  
As Figure 7a shows, the erosion morphology at the valve orifice of the high-speed on/off valve is mainly characterized by a \"scale-like\" lip, where the material surface is subjected to the impact of particles, resulting in the accumulation of material at the erosion wear site, and as the erosion wear continued, the material accumulation site tended to develop into a \"scale-like\" lip [25].  
The erosion parts of the spool were mainly concentrated at the valve orifice, while there was little erosion wear in the tip region of the spool taper. This is because the velocity of the particles impacting the tip region and the impact angle were small, and the erosion damage of the particles on the wall surface was negligible. When the particles flowed out of the valve orifice, the direction of motion changed significantly. The impact velocity and impact angle of the particles are also large, causing varying degrees of erosion and wear on the spool cone extending from near the seal line to the end of the spool cone.  
The direct impact of the particles on the valve front area and the impact of swirling centrifugal particles on the valve cavity combined caused more severe erosion wear at the  
Figure 6: Diagram of particle trajectory.  
Figure 7: (a) Distribution of erosion rate on valve port (unit: kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)); (b) distribution of erosion rate on axisymmetric surface (unit: kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)).  
ends of the valve seat corners. In addition, Figure 7b shows that the maximum erosion wear of the valve port occurred on the sharp edge of the valve seat.  
In summary, the high-speed on/off valve orifice erosion was mainly caused by the mainstream beam and the valve cavity vortex centrifugal particle erosion together.
## 3 The Proposed Method  
By learning the shared dictionary \(D_{0}\), the previous hybrid DL algorithms can capture the common features of the data, but they do not concern the correlation among these features, which will reduce the robustness of the learned dictionary. In this section, we firstly utilize the locality information of the atoms in \(D_{0}\) to construct a locality constraint, then impose it on the coefficients \(Z_{0}\), so that the correlation of the common features is captured explicitly and the learned dictionary \(D_{0}\) is very robust concerning commonality representation. Moreover, once the correlation is discarded, the classification of a query sample will be dominated by the class-specific sub-dictionary corresponding to the correct class to reach minimized data fidelity.  
To obtain the discriminative ability of the class-specific dictionary, we further introduce a label-aware constraint as well as group regularization on the distinctive coding coefficients for the particularity representation. Since this constraint integrated with the locality constraint to reconstruct the input data, they will reinforce each other in the learning process, which results in a discriminative hybrid dictionary for image classification.  
Accordingly, the objective function of the proposed LPLC-HDL method can be formulated as follows.  
\[\begin{split}\min_{D_{0},L_{0},Z_{0},D_{p},Z_{p},Z_{p}}\sum_{i=1} ^{\mathbb{C}}&\left\{\|X_{i}-D_{0}Z_{0}^{i}-D_{p}Z_{p}^{i}\|_{F}^ {2}+\lambda\|P_{i}Z_{p}^{i}\|_{F}^{2}\right\}+\gamma\Big{(}\|Z_{p}\|_{F}^{2}+ \\ & Tr\big{(}Z_{p}L_{p}Z_{p}^{\mathrm{T}}\big{)}+\eta\Big{(}\|Z_{0} \|_{F}^{2}+Tr\big{(}Z_{0}^{\mathrm{T}}L_{0}Z_{0}\big{)}\Big{)},\\ & s.t.\ \|d_{0}^{k}\|_{2}^{2}=1,\ (k=1,2,\cdots,k_{0});\ \|d_{p}^{i}\|_{2}^{2}=1,\ (l=1,2, \cdots,K_{p}),\end{split} \tag{4}\]  
where \(\lambda\), \(\gamma\) and \(\eta\) are the regularization parameters, which can adjust the weights of the label-aware constraint, the group regularization and the the locality constraint, respectively. Here we set the Euclidean length of all the atoms in the shared dictionary \(D_{0}\) and the class-specific dictionary \(D_{p}\) to be 1, which can avoid the scaling issue.
# Investigation on Unsteady Flow Characteristics of a SCO\({}_{2}\) Centrifugal Compressor
## 3 Computational Method
### Generation of Mesh  
A high quality three-dimensional computational mesh was necessary for the CFD simulation in this paper. In this case, we adopted hexahedral meshes in all fluid domains.  
As shown in Figure 5, O-type mesh was applied around impeller blades, which greatly improved the mesh quality at the leading edge and the trailing edge of the blades. Additionally, H-type mesh was mainly employed in the flow channels, which also enhanced the mesh quality. It can also be observed that the mesh in the boundary layers of the wall was densely generated, to be exact, the hub wall, the shroud wall, and the blade surface. Hence, the \(y^{+}\) all over the impeller domain was between 20\(\sim\)110, which corresponds to the requirement of the RNG \(k\)-\(\epsilon\) turbulence model. The mesh in this investigation excluded tip clearances since a shrouded rotor was designed.  
As shown in Figure 6, O-type mesh was also applied around diffuser blades and H-type mesh was employed in the flow channels. The mesh in the boundary layers of the wall was densely generated, and the \(y^{+}\) all over the diffuser domain was between 30-80, which also corresponds to the requirement of the RNG \(k\)-\(\epsilon\) turbulence model.  
As explained above, in order to be within the wall function validity limits, the first mesh line was placed at the \(y^{+}\) range of 20-110 in near the wall region. To guarantee the accuracy of the numerical simulation, a mesh independence test was conducted before the numerical analysis of unsteady flow in the designed centrifugal compressor. In the independence test, we used steady simulation for convenience. Since steady result served as the initial value for unsteady computation, it was efficient to examine the mesh validity and obtain the initialization simultaneously. In the steady simulation, the boundary conditions and the rotating speed were the same as unsteady calculation. Additionally, the frozen rotor method in ANSYS-CFX was applied to the interface between the impeller and the diffusers.  
Figure 5: Mesh of impeller.  
Figure 6: Mesh of diffuser.  
Four additional grids were generated to examine mesh convergence criteria and solution dependence on the mesh resolution. The mesh sensitivity was examined in terms of the torque of a single blade. The result of mesh independence test is presented in Table 4.  
It can be concluded that when the element number exceeds approximate 599.6 \(\times\) 10\({}^{4}\), the torque of a single blade is of small variation. Hence, based on the balance of time cost and precision, we ultimately selected sequence number 4 to conduct the unsteady numerical simulation; in other words, the total element number was 599.6 \(\times\) 10\({}^{4}\). Elements in rotor part and stator part were respectively about 300 \(\times\) 10\({}^{4}\).
## 2 GNSS Products  
The International GNSS Service (IGS) [23] has ensured the availability of open-access, high-quality GNSS orbital and clock data products since 1994 [24]. The post-processed products of IGS include Final products, Rapid products, Ultra-Rapid (observed half), and Ultra-Rapid (predicted half) products, with latencies of 12-19 days, 17-41 h, 3-9 h, and real-time, respectively. Due to the long delays, none of the first three can be used for real-time LEO satellite clock determination. Therefore, available real-time GNSS products are limited to the Ultra-Rapid (predicted half), the broadcast ephemeris, and the real-time stream products provided by IGS Real-Time Service (RTS). Taking the GPS satellite clock as an example, the STD of broadcast ephemeris and Ultra-Rapid (predicted half) is about 2.5 and 1.5 ns, respectively, which limits their applications for high-precision real-time LEO satellite clock determination.  
In 2013, IGS RTS was established to meet the demands of various real-time applications. Currently, four IGS-SSR streams are disseminated, named SSRA02 IGS, SSRC02 IGS1, SSRA03 IGS1, and SSRC03 IGS1, which are weighted averages of products of different Analysis Centers (ACs). Besides that, some ACs also provide individual RTS products [17; 25; 26]. For GPS satellites, the clock precision is typically good, ranging from 0.1 to 0.2 ns, and the orbital accuracy is generally within 5 cm in each direction, compared with COM final products, respectively [27; 28]. As such, IGS RTS provides the necessary product assurance for determining real-time LEO satellite products when using GNSS observations.  
In this study, real-time GNSS satellite products from four different analysis centers, CNE, GFZ, GMV, and WHU, were collected using the BKG Ntrip Client (BNC) Version 2.12.18 [29] from Day-Of-Year (DOY) 119-225, 2024. Figure 1 shows the continuity of GPS satellite products during the test period with a sampling interval of 30 s for the clocks and 5 min for the orbits. It can be seen that all products exhibit small data gaps, which could be caused by various reasons, including the internet connection and the server connection status.  
Table 1 displays the continuity and latency of these products during the test period. The latency varies from 5 to 30 s. Most of the ACs adopt their own ultra-rapid orbits or IGS ultra-rapid orbits to generate the RTS orbit corrections, while the satellite clock offsets are estimated by introducing the predicted orbits [17; 19]. Therefore, in the process of generating RTS products, the steps that may cause delays include collecting network  
Figure 1: The continuity of the received real-time GPS satellite clocks from four analysis centers.  
observations, estimating the clocks, and disseminating clocks and orbits over the network. The internet status of the service provider, the transmission caster, and the user could also cause data gaps and delays.  
The continuity or latency of the real-time GNSS product would directly influence the precision and continuity of the LEO satellite clock determination. This will be discussed in the next section.
## 3 Materials and Methods
### Shrinkage
#### 3.2.2 Elastic Net Regression  
Although the lasso regression carries out the variable selection, it has some defects. The first defect is the number of features that the lasso regression selects. When the dataset has high dimensionality, lasso selects features less than N because the sample size has  
Figure 2: The basic structure of random forest.  
been limited by the lasso regression. The second defect is the selection of correlated features. The lasso regression selects only some of the related features, while it is expected to select all correlated features or remove them all. In [30; 31], the elastic-net regression was introduced to overcome these defects. It is a combination of two methods: lasso regression (\(\ell_{1}\)-norm) [27] and ridge regression (\(\ell_{2}\)-norm) [32]. The \(\ell_{2}\)-norm part of the penalty makes a sparse model by shrinking some regression coefficients toward zero. On the other hand, the \(\ell_{1}\)-norm part of the penalty removes some number of the selected variables. In addition, the weight between the two tuning parameters is determined by \(\alpha\) so that the elastic net is converted into ridge regression at \(\alpha=1\) and is changed into lasso regression at \(\alpha=0\). Therefore, the \(\ell_{2}\) and \(\ell_{1}\) penalties create a range of tuning parameters between 0 and 1 (i.e., \(0\leq\alpha\leq 1\)).  
Suppose \(\left(\mathbf{X},Y\right)\) is a dataset so that \(\mathbf{X}=\left(x_{1},\dots,\ x_{p}\right)^{\prime}\) is the prediction variable and \(Y\) is the response variable. Elastic net uses the combination of the \(\ell_{2}\) and \(\ell_{1}\) penalties, which can be defined as follows:  
\[\hat{\beta}^{elastic}=\underset{\beta}{argmin}\bigg{\{}\frac{1}{2}Y-\mathbf{X} \beta^{2}+\lambda\bigg{[}\frac{1}{2}(1-\alpha)\beta_{2}^{2}+\alpha\beta_{1} \bigg{]}\bigg{\}} \tag{6}\]  
where \(\beta_{1}=\sum_{j=1}^{p}\left|\beta_{j}\right|\) and \(\beta_{2}^{2}=\sum_{j=1}^{p}\beta_{j}^{2}\) are the \(\ell_{1}\)-norm and \(\ell_{2}\)-norm of \(\beta\), respectively. \(\lambda\geq 0\) is a tuning parameter that is selected based on cross-validation.
## 2 Materials and Methods
### Methods
#### 2.3.5 Binary Logistic Regression  
In this study, spectral and texture features were used to establish BLR models. Mark infested cotton as 1 and healthy cotton as 0. Limit the range of the dependent variable to [0, 1]. Set the conditional probability of cotton root rot to be \(P\) and \(f(P)\) is assumed to be a functional form of a variable. Logit transformation is performed to build a BLR model [14,36].  
\[f(P)=\ln\frac{P}{1-P} \tag{3}\]  
BLR model takes the form of  
\[f(P)=a_{0}+a_{1}x_{1}+\cdots+a_{i}x_{i} \tag{4}\]  
\(P\) can be expressed as  
\[P=\frac{1}{1+\mathrm{e}^{-(a_{0}+\sum_{i=1}^{n}a_{i}x_{i})}} \tag{5}\]  
In the formula, \(a_{0},a_{1},\cdots a_{i}\) are regression coefficients; \(x_{1}\cdots x_{i}\) are regression independent variables.  
In this study, the regression independent variables were the spectral indices and texture features selected by the RF algorithm and their combinations, and the sample set was divided into the training set and the test set according to a 3:1 ratio by random sampling. A stepwise method was used to establish three BLR models: spectral, texture, and spectral-texture. In the process, the variables entered the equation based on the significance of the score statistics and were removed from the equation based on the probability of the likelihood ratio statistics based on the conditional parameter estimation.  
In order to evaluate the classification accuracy of the BLR models using the sample set, the Precision, Accuracy, Recall, and F-score were selected as the evaluation indicators.  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F-score=2*\frac{Precision*Recall}{Precision+Recall} \tag{9}\]  
where \(TP\) is true positive; \(FP\) is false positive; \(TN\) is true negative; \(FN\) is false negative.
## 3 Methods  
The planning of the logistical support of the operations process, in particular for Stage IV, i.e., the prediction of failures that could potentially occur during the planning year, is a complex process and often subject to a high risk of error. Many theories have been developed in failure-potential analysis, but they are most often associated with thorough mathematical knowledge and dedicated specialized software.  
Using a simple factor to assess mean time to failure (MTTF) reliability will make it possible to make it extremely easy to estimate which typical failures will occur in a planning year by using the damage data from past operations, determining the \(\mathrm{MTTF_{D}}\) (the data come from flight data and failures analysis) for the most common faults, and breaking them down by the \(\mathrm{P/N}\) of the main replaceable parts, e.g., fuel pump, chassis actuator, safety valve.  
The \(\mathrm{MTTF_{D}}\) is calculated from the operational data using the following formula [25]:  
\[MTTF_{D}=\frac{\sum t_{i}}{\sum n_{i}}, \tag{1}\]  
where:  
\(t_{i}\)--operating time of the \(i\)-th aircraft in the considered period (light hours [FH]);  
\(n_{i}\)--number of defects detected on the \(i\)-th aircraft in the considered period.  
Based on observing the changes in the index value, a prediction can be made of the failure frequency of the specified component.  
Determining the MTTF\({}_{\text{D}}\) is within the reach of any engineer or operator, who does not need specific mathematical knowledge or sophisticated computer tools for this purpose but only a calculator, a pen, and a piece of paper. The biggest difficulty of this damage prediction method is to build a damage database with the determination of characteristic parameters about each damage, i.e.,  
* Name of the damaged unit;
* Part Number (P/N) identification number of the part;
* Date of installation on board;
* Date the failure occurred;
* The operating time of the device until the failure occurred--calculated in flight hours, months, hours, days, cycles, etc;
* Brief description of the failure--in particular, at what level of maintenance was it detected, symptoms, impact on other components, impact on the operation of the entire technical facility, impact on the operational work of the technical facility, impact on safety, etc.;
* How the failure was repaired--replacement with a new one, adjustment, or other activity.  
Having all the above information about the set of all failures, the necessary analysis can be easily carried out.