esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 2 Testing of Key Mechanical Properties of Igneous Rocks
### Test Method  
The hydration of drilling fluids causes inter-particle swelling of clay minerals [14]. The significant presence of clay minerals in weak interlayer igneous necessitates the collection of drilling fluids used in the field for hydration tests. These tests aim to determine the effect of rock soaking time on the strength parameters of rocks containing weak layers [15]. Figure 2 presents igneous cores obtained from the core bank at a depth of 2963-2964 m, with a regular cylindrical rock sample measuring 25 mm \(\times\) 50 mm. Figure 2 depicts the drilling fluid immersion test, utilizing a modified PEC drilling fluid.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multicolumn{6}{c}{**Mineral Content (\%)**} \\ \cline{2-7}  & **Quartz** & **Plagioclase** & **Dolomite** & **Stone Salt** & **Barite** & **Anhydrite** & **Clay** \\ \hline BZ-1 & 3.5 & 64.2 & 4.1 & 2.1 & 4.7 & 4.0 & 17.4 \\ \hline BZ-2 & 3.0 & 71.1 & 5.3 & 0 & 0 & 4.7 & 15.9 \\ \hline BZ-3 & 3.1 & 65.9 & 4.1 & 2.0 & 4.8 & 3.6 & 16.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of X-ray diffraction quantitative analysis of igneous rock.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multicolumn{6}{c}{**Relative Content of Clay Minerals (\%)**} \\ \cline{2-7}  & **S** & **I/S** & **It** & **Kao** & **C** & **C/S** \\ \hline BZ-1 & 80 & 20 & 0 & 0 & 0 & 0 \\ \hline BZ-2 & 86 & 14 & 0 & 0 & 0 & 0 \\ \hline BZ-3 & 89 & 11 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of quantitative analysis of igneous clay minerals by X-ray diffraction.  
Figure 2: Core and immersion experiment: (**a**) cores from the core bank; (**b**) drilling fluid immersion experiments.
# Modeling of Fatigue-Strength Development
## 2 Materials and Methods
### Specimen Preparation  
Based on the design mix presented in Section 2.1, 42 cold-emulsion asphalt slabs measuring 400 mm \(\times\) 300 mm \(\times\) 80 mm were produced in the laboratory following procedures set out in BS EN 12697-33 [64]. This research made use of electro-mechanically operated Dynamoave Rolling Wheel Compactor, supplied by Industrial Process Controls Ltd., Italy to compact the cold-mix asphalt slabs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Material Description** & **Softening Point (°C)** & **Needle Penetration (mm)** & **Percentage in Total Mix (\%)** \\ \hline Bitumen Emulsion & 54.5 & 88.25 & 65 \\ Reclaimed Asphalt Pavement & 63.4 & 16.3 & 4.1 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of Bitumen Recovered from Emulsion and Reclaimed Asphalt Pavement.  
Figure 1: Aggregates Gradation for Reclaimed Asphalt Pavement Mix.  
Upon completion of compaction, slabs were kept in their molds for 24 h at ambient temperatures before being de-molded and cured for a further 24 h at 40 \({}^{\circ}\)C. Since the specimens were still fragile, only the sides of the molds were struck off, while the asphalt slabs went into the curing chambers with the base plates. To minimize aggregate chipping and excessive dust generation during sawing, the slabs were thereafter conditioned at 5 \({}^{\circ}\)C for 24 h in a fridge. At the end of the preliminary curing period, prismatic specimens measuring 400 \(\pm\) 5 mm by 63.5 \(\pm\) 5 mm by 50.8 \(\pm\) 5 mm, were obtained from the slabs by sawing in the direction of compaction with a diamond masonry saw. For dimensional stability, 6 mm were sawn off from the sides as well as the top and bottom faces of the slabs. Each slab produced 4 prismatic asphalt beams.  
The resulting 168 asphalt beams were divided into four sets, of 42 asphalt specimens each, which were variously cured at 5 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C in temperature-controlled chambers, with what was the upper side during compaction facing up. The cure temperatures were chosen to give a range below the softening point temperature of the base bitumen of the emulsion. This was informed by findings of previous studies, that curing of cold-mix asphalts at temperatures below the softening point of the base bitumen results in minimal bitumen aging [28; 32; 65; 66; 67; 68]. Specimens were drawn from each curing chamber at intervals of 1, 3, 5, 7, 14, 21, and 28 days and tested for fatigue strength.
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
###### Abstract  
Distributed space time frequency coding (DSTFC) schemes address problems of performance degradation encountered by cooperative broadband networks operating in highly mobile environments. Channel state information (CSI) acquisition is, however, impractical in such highly mobile environments. Therefore, to address this problem, designers focus on incorporating differential designs with DSTFC for signal recovery in environments where neither the relay nodes nor destination have CSI. Traditionally, unitary matrix-based differential designs have been used to generate the differentially encoded symbols and codeword matrices. Unitary based designs are suitable for cooperative networks that utilize the amplify-and-forward protocol where the relay nodes are typically required to forego differential decoding. In considering other scenarios where relay nodes are compelled to differentially decode and re-transmit information signals, we propose a novel co-efficient vector differential distributed quasi-orthogonal space time frequency coding (DQSTFC) scheme for decode-and-forward cooperative networks. Our proposed space time frequency coding scheme relaxes the need for constant channel gain in the temporal and frequency dimensions over long symbol periods; thus, performance degradation is reduced in frequency-selective and time-selective fading environments. Simulation results illustrate the performance of our proposed co-efficient vector differential DQSTFC scheme under different channel conditions. Through pair-wise error probability analysis, we derive the full diversity design criteria for our code.  
differential distributed space time frequency coding; co-efficient vectors; unitary matrices; quasi-orthogonal codes +
Footnote †: journal:  
0000-0002-323-7540  
00000-0002-323-7540
## 3 The Development of Detection Technology  
With the development of industry and agriculture, there are more and more pollutants in the water. There will be more unknown components to interfere with Cl\({}^{-}\) determination. The accuracy of Cl\({}^{-}\) detection is becoming more and more important. If the traditional methods are not improved, it is more and more difficult to adapt to the detection requirements of the new environment. Therefore, optimized versions of various traditional methods and new detection technologies are constantly emerging.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detection Method** & **Name** & **Concentration Range (mg/L)** & **Applicable Objects** & **Standard Number** \\ \hline \multirow{8}{*}{Volumetric Method} & Mercury nitrate titration & 2.5–500 & Groundwater Surface water & HI/T343 [39] \\  & Ammonium thiocyanate & 5–100 mg & Natural and clean water & GB/T29340 [40] \\  & volumetric method & 10,000–42,000 & Seawater cooling water & GB/T33584.3 [41] \\  & Silver nitrate volumetric method & \textgreater{}1.0 & Domestic drinking water and & GB/T5750.5 [42] \\  & & & source water & GB/T5750.5 [42] \\  & Mercuric nitrate volumetric method & \textgreater{}1.0 & Domestic drinking water and & GB/T5750.5 [42] \\  & Silver nitrate titration & 10–500 & Natural water & GB/T11896 [43] \\ \hline Electrochemical Method & Potentiometric titration & 5–1000 & Industrial circulating cooling water & GB/T15453 [44] \\  & & & and boiler water & GB/T15453 [44] \\ \hline \multirow{4}{*}{Spectrophotometry Method} & Corpecipitation enrichment & \multirow{2}{*}{10–100} & Industrial circulating cooling water & GB/T15453 [44] \\  & photometric method & & and boiler water & GB/T15453 [44] \\  & Ferrie photometric method of & 0.4-6.0 & Meteoric water & GB/T13580.9 [45] \\ \hline \multirow{2}{*}{Chromatop Method} & \multirow{2}{*}{Ion chromatography Method} & 0.1–500 & Clean water & GB/T14642 [46] \\  & & 0.15–2.5 & Domestic drinking water and & GB/T5750.5 [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical detection standards for chloride ion in water in China.
# Transient Friction Analysis of Pressure Waves Propagating in Power-Law Non-Newtonian Fluids
## 3 Results and Discussion
### Transient Friction at the Wall
#### 3.3.1 Influence of Stable Pipe Flow on the Wall Friction  
The velocity fluctuation of the periodic flow is relatively small compared to the velocity of the stable pipe flow. When the periodic flow is superimposed on the stable pipe flow, it is difficult to distinguish the shear stress generated by pressure pulsation by directly observing the distribution of shear stress. Therefore, it is necessary to separate the pulsating shear stress using the following expression:  
\[\tau_{p}=\tau-\tau_{c}, \tag{29}\]  
where \(\tau_{p}\) is the pulsating shear stress, \(\tau_{c}\) is the stable shear stress, and \(\tau\) is the total shear stress.  
For Newtonian fluids, the different ratios of \(P_{c}\) and \(P_{p}\) in Equation (25) will not affect the distribution of the shear stress generated by the periodic pressure, as shown in Figure 10a,b. However, for non-Newtonian fluids, different ratios of \(P_{c}\) and \(P_{p}\) will have different effects on the wall shear stress, as shown in Figure 10c,d. Moreover, even under the same ratio, different values of \(P_{c}\) and \(P_{p}\) can cause different shear stress distributions.  
The unit length pressure drop \(P_{c}\) of the stable pipe flow can be expressed by the flow rate \(Q\) as  
\[P_{c}=\frac{2\tau_{acc}}{R}=\bigg{(}\frac{(3n+1)Q}{n\pi R^{3}}\bigg{)}^{n} \frac{2K}{R}, \tag{30}\]  
\(\tau_{acc}\) is the wall shear stress of the stable pipe flow, which is a power-law relationship with the flow rate.  
Figure 11 shows the variation curves of pulsating shear stress at the wall with the flow rate under two power-law indexes and four ratios of \(P_{p}\) to \(P_{c}\). As shown in the figure, the pulsating shear stress increases with the increase in the ratio of \(P_{p}\) to \(P_{c}\), and this pattern remains consistent regardless of whether \(n\) is greater than or less than 1. However, for \(n<1\) or \(n>1\), the variation pattern of pulsating shear stress with flow rate is exactly the opposite, which differs from the variation law of wall shear stress with flow rate in the stable pipe flow given in Equation (30). For \(n<1\), the wall pulsating shear stress nonlinearly decreases with the increase in flow rate; for \(n>1\), the shear stress increases nonlinearly with increasing flow rate. The reasons for these phenomena relate to the shear thinning or thickening of the power-law non-Newtonian fluids. When \(n<1\), the apparent viscosity decreases with the increase in shear rate, resulting in lower viscosity at higher flow rates. Therefore, the friction loss of pulsating pressure decreases with the increase in flow rate for \(n<1\). On the contrary, the friction loss of pulsating pressure increases with the increase in flow rate for \(n>1\).  
Figure 11: Variation of pulsating shear stress at the wall for different flow rate and ratio of \(P_{p}\) to \(P_{c}\). (**a**) \(n=0.5\). (**b**) \(n=1.5\).
## 3 Results and Discussion  
### Create a Dataset  
The experimental dataset was obtained from a clear video recorded in Yuandian No. 1 Mine in Huaibei. A total of 1600 pairs of blurred-clear pictures were generated by averaging the adjacent 7-13 frames of each video. Clear images were compiled into dataset 1, and some clear images are shown in Figure 8a-c. Blurred images were compiled into dataset 2, and some blurred images are shown in Figure 8d-f. DeblurGANv2 was used to deblur the blur images in dataset 2, and the processed images were compiled into dataset 3. Some images are shown in Figure 8g-i.  
Figure 7: Improved network structure.
## 4 Results and Discussion
### Radial Dynamic Mechanical Characteristics of the Air Spring
#### 4.2.1 Influence of Amplitude Excitation on Radial Load Characteristics  
In this experiment, an initial air pressure of 0.3 MPa was applied to the air spring, and a vibration frequency of 1.0 Hz was set for the reference point. The relationship between the axial load of the air spring and time was observed and recorded under excitation amplitudes of 0.25, 0.5, 1.0, and 2.0 mm. The results are displayed in Figure 11.  
As depicted in Figure 11, the radial load of the air spring exhibited four distinct stages in response to varying excitation amplitudes: a stationary period, an ascending period, another stationary period, and a descending period. The peak value of the radial load generally increased with increasing amplitude; however, a deviation from this trend was observed when the excitation amplitude as set to 0.25 mm, as the peak radial load value surpassed that at an amplitude of 1.0 mm. This suggests that excitation amplitudes that are too low can have a negative impact on the radial load of the air spring.  
To investigate the load characteristics of the air spring under varying excitation amplitudes, the reference point of the air spring was set to a vibration frequency of 1.0 Hz, and an initial inflation pressure of 0.3 MPa was applied. Figure 12 illustrates the relationship between the radial load of the air spring and vibration displacement for different amplitudes.  
As demonstrated in Figure 12, under varying excitation amplitudes, the radial load of the air spring exhibited a similar pattern in the first half of each vibration cycle, characterized by fluctuations within a certain load amplitude. The excitation amplitude had a direct impact on the radial load peak, with higher amplitudes leading to greater peaks. The second half of each vibration cycle exhibited a regular and approximately symmetrical change, with peak loads that were relatively higher than those in the first half cycle. After a complete vibration cycle, the radial load tended to decrease steadily.
# Wireless Remote Control for Underwater Vehicles
## 3 Requirements and Definition of Working Modes for ROV Control
### Mid-Range Low-Capacity Wireless Mode  
In this mode, called mid-range low-capacity wireless mode (MRM), a real-time ROV remote control is no longer supported. Conversely from SRM, in MRM, position control is based on the transmission of way-points and not on a joystick-based trajectory control. A way-point-based remote control consists of sending not only the next position that the vehicle needs to reach but also the speed and rotation angles to the underwater vehicle, the latter used for setting the orientation of the vehicle while proceeding to the next position. The size of each component of position (x, y, and depth), speed (u, v, and w), and rotation angles (roll, pitch, and yaw) in a way-point packet is set equal to 4 bytes; therefore, together with a sequence number used as unique identifier of the way-point, the total size of a way-point packet is 40 bytes. In this mode, we assume that the minimum time lapse between two consecutive way-points is 2 s; thus, the maximum data rate generated by the control traffic is 160 bps.  
The operator can observe the progress of the ongoing mission through a very low-quality non-real-time video feedback, streamed with a high-frequency acoustic modem. The video considered in this mode is a 200 \(\times\) 96 px 5 fps VP9 video that requires an average bit rate of 13.7 kbps and a peak bit rate of 42 kbps. In [61], we managed to stream the same video through the EvoLogics S2 CM HS acoustic modem: although the maximum bit rate is higher than the maximum throughput achievable by the modem in that tested conditions (20 kbps), the video was not blocked and the receiver was able to smoothly reproduce the video with few frame losses. Indeed, 14 frames, over a total of 645, were automatically discarded by the application used for streaming to avoid video jitter and additional delay. Due to the codec setup, stream visualization started 10 s after the beginning of transmission.  
MRM can be supported by the simultaneous use of an HF acoustic modem for low-quality video stream plus the use of a long-range LBL acoustic modem for positioning, status monitoring, and sending the control messages up to a distance of a few hundred meters.  
In this mode, although the gripper cannot be maneuvered due to the long latency of the video monitoring, still, it is possible for the ROV to take some high-definition pictures of some interesting areas. These pictures will be stored in the ROV and buffered until the system switches to SRM mode.  
MRM requires traffic up to 160 bps from the control station to the ROV (that includes way-point transmission and control of lights and cameras) and of at least 15 kbps from the ROV to the control station (very low-quality video and status monitoring).
## 3 Results
### In Situ Spectra
#### 3.1.2 Atmospheric Data  
Analysis of in the situ SPM data indicating the optical characteristics of atmospheric aerosol on 12 September 2017 showed increased AOT values over the entire spectral range.  
Figure 1: Satellite images of MODIS (Aqua) for the day of dust transfer on 12 September 2017 (**a**) and 8 September 2017 (clean atmosphere); (**b**) AOT values’ variability (source: SeaDas).  
So, for example, on this day, the average daily AOT values at the wavelengths of 500 and 872 nm (AOT (500) and AOT (872)) were 0.37 and 0.2, respectively, which is 1.8 times more than the average monthly value at a given wavelength. The average daily values of AOT (500) and AOT (872) were 0.07 and 0.04, respectively, on 8 September 2017, indicating a very clean atmosphere.  
To determine the aerosol source on 12 September 2017 and 8 September 2017, the data of the modeling of the air mass transfer AERONET (BTA) and HYSPLIT were analyzed (Figure 2).  
Figure 2 shows that according to the modeling data of both AERONET and HYSPLIT, on 12 September 2017, dust transport from the side of the Sahara Desert was recorded at all studied heights while on 8 September 2017, westerly transport from the Atlantic Ocean was recorded. To estimate the contribution of atmospheric dust aerosol to the atmospheric correction of satellite data over the region under study, the error flag statistics (12_flags) ([[https://oceancolor.gsfc.nasa.gov/atbd/ocl2](https://oceancolor.gsfc.nasa.gov/atbd/ocl2) flags/]([https://oceancolor.gsfc.nasa.gov/atbd/ocl2](https://oceancolor.gsfc.nasa.gov/atbd/ocl2) flags/), accessed on 12 November 2021) were analyzed.
# On the Melting Thresholds of Semiconductors under Nanosecond Pulse Laser Irradiation
## 3 Results and Discussion  
Here we present an analysis of the available literature data used for our model development and discuss the simulation results and general trends in the damage threshold determination. The results of the present simulations are summarized in Table 1 in comparison with the literature data. In addition, for irradiation conditions with a ruby laser (wavelength 694 nm) where the most systematic data are available, we have performed calculations for various laser pulse duration of 15, 30 and 70 ns, beyond the ranges reported in the literature, in order to investigate the effect of pulse duration for specific materials (the obtained results are also presented in Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Material** & \(\lambda\)**, **nm** & \(\tau\)**, **ns** & \(F_{\text{th}}\)**, **mj/cm2** & \(F_{\text{th}}\)**, **mj/cm2** \\  & & & **This Work** & **Literature Data** \\ \hline Si & 532 & 18 & 355 & 395 [6], 330 * [37] \\  & & 30 & 423 & 474 [6] 350 [38] \\  & 694 & 15 & 672 & 725 [6] \\  & & 30 & 752 & 805 [6], 980 [27] \\  & & 70 & 900 & \\ \hline Ge & 694 & 15 & 191 & \\  & & 30 & 255 & 300 [27] \\  & & 70 & 370 & 400 * [10] \\ \hline GaAs & 308 & 30 & 213 & 200, 200 * [8] \\  & 532 & 15 & 184 & \\  & 694 & 15 & 265 & 300 [12] \\  & & 20 & 282 & 250 * [13], 360 * [23], 240 [27] \\  & & 30 & 316 & \\  & & 70 & 415 & \\ \hline \end{tabular}
\end{table}
Table 1: The simulation results for the damage threshold fluence, \(F_{\text{th}}\), of the studied semiconductors in comparison with theoretical and experimental data reported in the literature. The experimental data are marked by asterisks *.  
Note that, in the literature, the \(F_{\text{th}}\) values can be determined differently from the method used in this study. For example, in Ref. [39], the calculated melting threshold for CdTe was set 8% higher than the laser fluence needed for reaching \(T_{\text{m}}\). Time resolved reflectometry (TRR) measurements performed by the authors did not show an increase in reflectivity at the intensity corresponding to reaching the melting point in the calculations. Thus, as the melting threshold, the authors consider the intensity at which the sample surface layer is molten to the depth of laser radiation absorption. Experimentally measured values of the melting threshold fluence typically include a transition interval where localized melting occurs, giving rise to an increase in the reflectivity above the values of solid-state surface reflectivity [13; 29]. In numerical simulations, the determination of the damage threshold strongly depends on the used material properties [40]. Their choice can be considered as the main source of inaccuracy of the modeling results because we use a very accurately verified implicit algorithm for numerical solution (Section 2). Below we have surveyed the literature for the optical and thermophysical parameters of the studied semiconductors. The most relevant parameters are given in Appendix A.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 1 Introduction
\(\mathbb{C}^{M\times N}\) denotes the \(M\times N\) dimensional complex matrix space. \((\cdot)^{H}\), \(\left\|\cdot\right\|_{F}\), \(\left\|\cdot\right\|_{2}\), \(Tr(\cdot)\), and \(\mathbb{E}[\cdot]\) denote the conjugate transpose, the Frobenius norm, the Euclidean norm, the trace operation, and the expectation, respectively. \(\mathbf{I}_{N}\) is the \(N\times N\) identity matrix. \(\mathcal{CN}\left(\mu,\sigma^{2}\right)\) is a complex Guassian vector with mean \(\mu\) and variance \(\sigma^{2}\).