esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents a comprehensive investigation of the fundamental relationship between microindentation results and the stress state of the shale. To achieve this, a series of laboratory microindentation tests were conducted on a shale sample subjected to various confining pressures. Through an in-depth analysis and discussion of the obtained results, the following conclusions can be drawn:  
* The load-displacement curves obtained from the microindentation tests under various confining pressures demonstrate a decreasing trend in ultimate displacements with higher levels of confining stress. This observation indicates that the depth of the proppant embedment decreases when taking reservoir confining pressure into consideration;
* Young's moduli, Brinell hardness, and fracture toughness exhibit a direct linear correlation with increasing confining pressure, suggesting that applying confining pressure enhances the strength and hardness of the shale;
* Considering the effects of confining pressure, the decrease in proppant embedment is proportional to Young's modulus of the shale. Regarding fracture conductivity, for larger-sized proppants (e.g., \(D\) = 2.50 mm), the influence of confining pressure on fracture conductivity is relatively minor. However, when using smaller-sized proppants (e.g., \(D\) = 1.00 mm), especially in scenarios where prolonged shale-hydraulic fracturing fluid interaction results in significant shale debris swelling, there is a noticeable improvement in fracture conductivity when confining pressure is taken into account. Overall, previous computational models have tended to overestimate the depth of proppant embedment while underestimating the assessment of fracture conductivity.  
Conceptualization, J.S. and D.H.; methodology, J.S., Y.L. ([PERSON]), and Y.L. ([PERSON]); software, Y.L. ([PERSON]), J.S., and Y.L. ([PERSON]); investigation, Y.L. ([PERSON]) and Y.L. ([PERSON]); writing--original draft preparation J.S.; writing--review and editing, D.H.; validation, F.Y.; project administration, J.S. and D.H.; funding acquisition, J.S. and D.H. All authors have read and agreed to the published version of the manuscript.  
This work was support by the National Key Research and Development Program of China with Grant (No. 2022 YFE0137200) and OeAD-GmbH Scientific & Technological Cooperation (WTZ) Austria/China program with Grant (No. CN 04/2022); the National Natural Science Foundation of China (Nos. 52179141, 52179114); Basic Research Program of Liaoning Provincial Department of Education (No. IJKMZ20220381).  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries into any progress can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
###### Abstract  
The GNSS-based bistatic SAR (GNSS-BSAR) system has emerged as a hotspot due to its low power consumption, nice concealment, and worldwide reach. However, the weak landing power density of the GNSS signal often necessitates prolonged integration to achieve an adequate signal-to-noise ratio (SNR). In this case, the effects of the receiver's time-frequency errors and atmospheric disturbances are significant and cannot be ignored. Therefore, we propose an ionospheric scintillation compensation-based imaging scheme for dual-channel GNSS-BSAR system. This strategy first extracts the reference phase, which contains the ionospheric phase scintillation and other errors. Subsequently, the azimuth phase of the target is divided into difference phase and reference phase. We apply the two-step phase compensation to eliminate Doppler phase errors, thus achieving precise focusing of SAR images. Three sets of experiments using the GPS L5 signal as the illuminator were conducted, coherently processing a 1.5 km by 0.8 km scene about 300 s. The comparative results show that the proposed method exhibited better focusing performance, avoiding the practical challenges encountered by traditional autofocus algorithms. Additionally, ionospheric phase scintillation extracted at different times of the day suggest diurnal variations, preliminary illustrating the potential of this technology for ionospheric-related studies.  
Global Navigation Satellite System (GNSS); bistatic Synthetic Aperture Radar (SAR); two-step azimuth phase compensation; ionospheric scintillation; modified Back Projection (BP) algorithm +
Footnote †: journal: Remote Sensing  
10 May 2024  
Revised: 19 June 2024  
Accepted: 24 June 2024  
Published: 27 June 2024
## 4 Limit Load Analysis of Straight Pipes with Local Wall-Thinning Defects under Complex Loads
### Finite Element Analysis Results
Within the applicable range of defect size, the upper boundary point (\(a=0.95\), \(b=0.25\), \(c=0.5\)) was selected for finite element calculation; the error between the results of the finite element calculation and that of Equation (13) was 3.79%. Verification of the Load-Bearing Capacity of Straight Pipes with Local Wall-Thinning Defects under the Combination of Internal Pressure and Bending Moment  
[PERSON] [28] discussed the failure mode of \(\mathrm{SPL}_{\mathrm{LWT}}\) under the combination of internal pressure and bending moment, and obtained the limit load curve under the combination of internal pressure and bending moment, while also summarizing the equation of limit load for straight pipes with all local wall-thinning defect sizes, but the equation was without detail division according to the range of defect size. The limit load can be calculated by Equation (14):  
\[(m_{1})^{2}+\ (p_{1})^{2}=1.0 \tag{14}\]  
Figure 8 shows the comparison between the limit load curves of \(\mathrm{SPL}_{\mathrm{LWT}}\) under the combination of internal pressure and bending moment in this paper and the limit load equation in Ref. [28]. Taking the non-dimensional torque \(n_{1}\) and non-dimensional axial force \(f_{1}\) in Equations (10)-(13) as zero, the limit load equation of \(\mathrm{SPL}_{\mathrm{LWT}}\) under the combination of internal pressure and bending moment can be obtained. It can be seen that the limit load curves of Equations (11) and (12) in this paper are in good agreement with the limit load results of Equation (14) in Ref. [28]. But the limit loads of Equations (10) and (13) in this paper are higher than those of Equation (14) in Ref. [28], within \(p_{1}=0.3\)-0.7. This is because the values of Equation (14) are the lowest boundaries of all types of defects. Equation (14) cannot accurately evaluate all defects.
## 5 Numerical Illustration  
Detailed numerical results about the occupancy grid setting were presented in References [10; 34] using, for the first, a fixed order of optimising and executing and, for the latter, a priority rule setting. Without enforcing explicitly the rule to reveal a circular curve, we like to point out one scenario with \(P=4\) non-holonomic robots from Reference [34], where a minimum open-loop costs priority rule is used, i.e., the robot with minimum costs over the prediction horizon goes first. We selected here two scenarios using cell sizes \(c=1.5\) and \(c=2.0\) and with start and target positions of the robots opposite to each other in the edges of the operation space. Hence, they have to circumvent each other in the centre. In Figures 8 and 9, the snapshots of the different time instants are illustrated to show the evolution of time of the trajectories and the behaviour of the robots.  
In the first scenario depicted in Figure 8 with cell size \(c=2.0\), the large cell size demands a large radius due to the collision avoidance constraints. Robot 0 (\"car0\") goes at first and, hence, may start with a straight trajectory due to the fixed order and the initialisation phase in Algorithm 1. As all robots start with their initial prediction that they keep their position in the first time instant over the prediction horizon, i.e., ensuring feasibility, robot 0 has the centre of the operation space available to use this at first. The other robots (\"car1-3\") have to incorporate this trajectory from robot 0 and have to choose detours shown by the coloured predictions, which forms a roundabout in a clockwise manner. The occupied cells, which are reserved by the predictions of the robots, are coloured accordingly to the robots. In later time instants, the trajectories show clearly that, although the order may disadvantage the last robots most, the arrival times are quite close due to the identical behaviour of the sidestepping robots.  
With cell size \(c=1.5\) shown in Figure 9, the robots form accordingly a smaller roundabout due to smaller cells and less necessary detours. Similar to the first scenario, robot 0 has the advantage of being enabled to take the centre of the operation space due to the fixed optimisation order. The trajectory especially of robot 3 \"car3\" shows that the detours are shorter as the robot crosses the centre in a more straight line than in the scenario before. Therefore, without setting implicit conditions on the behaviour of the collision avoidance in both scenarios, the optimiser leads to the pattern of a circular curve, which is also used in street traffic as roundabouts.  
Figure 8: Holonomic example with 4 robots, prediction horizon length \(N=12\), and cell size \(c=2.0\): snapshots at time instants \(n=1\) (**left top**), \(n=9\) (**right top**), \(n=14\) (**left bottom**), and \(n=24\) (**right bottom**):Trajectories are drawn in continuous lines, and the predicted occupied cells are coloured in the same colour as the robot.
## 3 DRFM System Model
### DRFM Model  
The DRFM system has a simple task; it records a digitally sampled pulse to memory and \"replays\" the waveform at another time. At its heart, this function is performed by a two-port Random Access Memory (RAM) that can simultaneously write a signal to memory and read a signal from memory, as long as the read and write addresses are not identical.  
The DRFM systems used in this paper are shown in Figure 6, and implemented simultaneously in amplitude and I-Q forms to facilitate testing, with an 18-bit address space of dual port memory on the FPGA. The resulting system could record up to 5.242 ms of pulse samples at the 20 ns sample period.  
The DRFM sub-system is in many ways the simplest component of the jammer receiver, as its sole function is to record detected pulses to a RAM sub-system. The detection process, outlined in the previous sub-section, creates triggers for the rising and falling edges, which initiates and stops the process, respectively, of recording the pulse to memory. In the case of the I-Q model, shown in Figure 7, two identical DRFM sub-systems are used for each data stream, whereas for the amplitude DRFM, only a single DRFM sub-system is used.  
Figure 6: The top level sub-system model of the DRFM in Simulink, including both the amplitude and I-Q DRFM on the left and right, respectively.  
Figure 7: The FPGA implementation of the DRFM memory for the I-Q DRFM.  
The dual port RAM would normally be used to simultaneously record pulses and play them back to support the generation of ECM during a look-through operation. In this case, as the pulse repeater model was used, it wrote the received waveform to memory and simultaneously retransmitted the results.
## 3 Mudstone Electrochemical Modification Tests Program
### Test Condition Setting  
To explore the specific effects of electrochemical modification on mudstone while ensuring the single-variable principle of controlled experiments, the mudstone was processed into standardized samples of the same specifications. A set of mudstone samples for electrochemical modification were selected. After modification, the physical and mechanical parameters of the original mudstone samples were compared to those of the modified samples to analyze the effects of electrochemical modification. The main influencing factors in the electrochemical modification experiment (electrode material, electrolyte solution type, and concentration) were used as variable conditions for this experiment.  
There are two types of electrode materials: metallic electrodes and non-metallic electrodes. Metal electrodes produce metal cations in the chemical reaction to achieve the cementation and stabilization of the soil particles. Non-metal electrodes do not undergo electrolytic reactions and thus do not produce cementation that blocks drainage channels, which are more conducive to achieving electro-osmotic drainage. To compare how different electrode materials affect electrochemistry, aluminum and iron, which are commonly used as metal electrodes, were chosen for their high electrical conductivity and low costs [29]. In addition, graphite electrodes were chosen as a control for electrodes made of oxidizable metals, which are characterized by a high specific surface area, strong oxidation resistance, corrosion resistance, and high electrical conductivity [30].  
The influence of the electrolyte composition on the electrochemical stabilization effect is similar to that of electrode materials. The reinforcement effect is achieved by introducing cations to produce cement. Depending on the cations introduced, the cement's strength can vary significantly. The concentration of the electrolyte solution affects the total amount of cement produced and the time and drainage consolidation efficiency of the electrochemically modified mudstone. To precisely examine the effect of electrolyte solutions on the electrochemical modification of mudstone, variable test conditions were regulated, and only graphite electrodes were used for comparative electrolyte composition and concentration tests. Mudstone conditions in the laboratory usually change after 20 h, so the electrolyte time factor was not examined separately in the experiments.
## 3 Results
### Simulation Experiment Setting  
In the test scenario, mission request is generated by a random uniform distribution in worldwide. For each request of emergency mission, the following attributes are included: observation duration, geographical location, imaging mission level, observation image type and type of mission. In this paper, 25 initial observation point targets were randomly generated, as shown in Figure 2, and the generated mission types are shown in Table 5. Using the same method, we generated another five as the newly arrived missions to test the dynamic scheduling algorithm (Table 6). Figure 2: Point target distribution. The time horizon of scheduling was from 2018/08/24 00:00 to 2018/08/24 14:00 (universal time coordinated), and the imaging duration was varied from 60 to 240 s. For satellite resources, three Earth observation satellites were set up in this experiment. These satellites were taken from the satellite database of STK software developed by AGI of the United States. The orbit parameters are shown in Table 7 and were generated calling the orbital generation tool of STK according to the general orbit type of Earth observation satellite. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mission ID**} & **Observation** & **Geographical** & **Imaging** & **Observation** & **Type of Mission** \\  & **Duration (sec)** & **Position** & **Mission Level** & **Image Type** & **Type of Mission** \\ \hline T26 & 80 & (65\({}^{\circ}\) E, 4\({}^{\circ}\) N) & SII & infrared & maritime static target \\ T27 & 170 & (69\({}^{\circ}\) W, 4\({}^{\circ}\) N) & HI & visible light & land static target \\ T28 & 110 & (64\({}^{\circ}\) E, 51\({}^{\circ}\) N) & NIV & microwave & land static target \\ T29 & 150 & (15\({}^{\circ}\) W, 14\({}^{\circ}\) N) & SI & visible light & land moving target \\ T30 & 100 & (73\({}^{\circ}\) E, 12\({}^{\circ}\) S) & AIII & visible light & maritime moving target \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Newly arrived mission parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Orbit Parameters**} & \multicolumn{3}{c}{**Satellite**} \\ \cline{2-5}  & **S1** & **S2** & **S3** \\ \hline Eccentricity \(e\) & 0.2030 & 0.24008 & 0.1353 \\ Inclination \(i\) (\({}^{\circ}\)) & 97.9072 & 97.7823 & 98.1956 \\ Right ascension of the ascending node \(\Omega\) (\({}^{\circ}\)) & 324.7362 & 321.0446 & 322.0663 \\ True anomaly \(\omega\) (\({}^{\circ}\)) & 49.6914 & 0.8425 & 69.8332 \\ Mean anomaly \(M\) (\({}^{\circ}\)) & 310.4478 & 359.2829 & 290.3028 \\ Number of circles per day & 14.84971693 & 14.79898507 & 14.58551423 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Orbit parameters of satellite.
###### Abstract  
With the rapid development of remote sensing (RS) observation technology over recent years, the high-level semantic association-based cross-modal retrieval of RS images has drawn some attention. However, few existing studies on cross-modal retrieval of RS images have addressed the issue of mutual interference between semantic features of images caused by \"multi-scene semantics\". Therefore, we proposed a novel cross-attention (CA) model, called CABIR, based on regional-level semantic features of RS images for cross-modal text-image retrieval. This technique utilizes the CA mechanism to implement cross-modal information interaction and guides the network with textual semantics to allocate weights and filter redundant features for image regions, reducing the effect of irrelevant scene semantics on retrieval. Furthermore, we proposed BERT plus Bi-GRU, a new approach to generating statement-level textual features, and designed an effective temperature control function to steer the CA network toward smooth running. Our experiment suggested that CABIR not only outperforms other state-of-the-art cross-modal image retrieval methods but also demonstrates high generalization ability and stability, with an average recall rate of up to 18.12%, 48.30%, and 55.53% over the datasets RSICD, UCM, and Sydney, respectively. The model proposed in this paper will be able to provide a possible solution to the problem of mutual interference of RS images with \"multi-scene semantics\" due to complex terrain objects.  
Keywords:cross-attention; remote sensing; cross-modal retrieval +
Footnote †: journal: Applied
## 3 Optimization Problem Statement
\right\} \tag{21}\]  
In this case, the vector of parameters  
\[\left(c_{L_{2}},\Delta r_{L_{2}}\right)^{\mathrm{T}} \tag{22}\]  
(specifying the neighborhood of the libration points) is selected manually during the process of solving an auxiliary problem. More details about the choice of the values of the latter will be discussed in the course of further presentation. By remembering that reaching a given neighborhood of the libration point \(L_{2}\) by SC is not a complete solution to the problem of designing low-energy trajectory (with access to a given low circular selenocentric orbit), now we proceed to the formulation and further formalization of the trajectory optimization problem of the second stage:  
It is required to design a low-energy trajectory of the SC, starting from a low circular orbit of the Earth and up to reaching a given selenocentric orbit. As an optimality criterion, the radius of the perigee of the transfer orbit is still considered (should be maximized). It is assumed that the vehicle's trajectory still passes through the vicinity of the libration point \(L_{2}\) (considered at the previous stage of solving the main problem), while the coefficients of the form (22) can be slightly corrected. Taking into account the assumptions have made, the problem of the second stage can be represented as:  
\[\left. \begin{array}{l}\underset{\mathbf{y}\in Q}{\min}J_{1}( \mathbf{y})\\ Q=\left\{\begin{array}{l}\mathbf{y}\in\mathbf{R}^{4}:\sqrt{r^{2}+{r_{L}}_{2} ^{2}-2\;r\;r_{L}\cos(\beta-\alpha_{M})}\Big{|}_{t_{1}}-\Delta r_{L_{2}}\leq 0,\\ \sqrt{1+r^{2}-2\;r\cos(\beta-\alpha_{M})}\Big{|}_{t_{1}+\Delta t_{f}}-(R_{ Moon}+h_{Moon})=0\end{array}\right\}\\ \mathbf{y}=\left(\beta_{0},r_{\alpha_{0}},\alpha_{M_{0}},t_{1},\Delta t_{f} \right)^{\text{T}}\text{, }\mathbf{y}\in\mathbf{R}^{5}\end{array} \right\} \tag{23}\]  
In the above expressions (23), the second condition of the equality type (among the conditions that form the set \(Q\)) meets the requirement that the SC reach a given position (\(R_{Moon}+h_{Moon}\)) relative to the Moon at time \(t_{1}+\Delta t_{f}\). Thus, an equality type constraint of the form (16) has been added to the optimization problem of the second stage; this is the main difference between the latter and problem (21). At the same time, the dimension of the vector \(\mathbf{y}\) also increased in a proper way: the time increment \(\Delta t_{\text{f}}\) was added to its components; during \(\Delta t_{\text{f}}\) the SC performs a passive motion from a certain point in the vicinity of \(L_{2}\) (reached at time \(t_{1}\)) to a circular selenacentric orbit. Before we continue with the third stage problem, it should be mentioned here that the final state vector components of the SC (relative to the Moon) and the corresponding value of the braking impulse \(\Delta V_{f}\) within the considered model of motion could be determined as follows:  
\[\left.
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 6 Case Studies
### Simulation Setup  
We consider a simple power grid that is made up of a power generator rated at 300 MW and 77 power distribution systems. The electric loads and the electric networks of all the power distribution systems are set according to the IEEE 33-bus power system. The aforementioned power generator supplies energy to all the 77 power distribution systems. Since the rated power consumption of each power distribution system is 3.9 MW, a total number of 77 power distribution systems are incorporated in the simulations to draw power from the generator at its rated power output; i.e., \(77\times 3.9\cong 300\). For reducing the computation burden in the simulations, all the power distribution systems are configured identically. For instance, the locations of compromised loads in all the power distribution systems are the buses 11, 12, 13, 14, 16, 18, 20, 21, and 24. Also, the locations of flexible loads in all the power distribution systems are 5, 10, 19, 32, 7, 33, 17, 27, and 15. Furthermore, the power generator is equipped with automatic generation control and provides reserve generation to the power distribution systems. The parameters of the generator and the automatic generation control are as follows: \(R=0.05\), \(Ki=1\), \(M=10\), \(D=0.9\), \(Tg=0.25\), \(Tt=0.6\), \(Kp=1\), \(Kg=1\), and \(Kt=1\); see Figure 1 and [41].