esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effluent Characterization at Optimal Conditions
#### 3.4.2 Chemical Oxygen Demand Fractionation  
Under optimal conditions, in the EC process, the variations in the COD fractions and their representative percentages between the crude leachate and effluent were as follows: CODb, from 870 \(\pm\) 40 mg L\({}^{-1}\) (26%) to 490 \(\pm\) 20 mg L\({}^{-1}\) (39%); CODp, from 1600 \(\pm\) 50 mg L\({}^{-1}\) (48%) to 290 \(\pm\) 10 mg L\({}^{-1}\) (23%); CODsb, from 870 \(\pm\) 40 mg L\({}^{-1}\) (49%) to 490 \(\pm\) 20 mg L\({}^{-1}\) (51%). The CODb fraction increased from 26% to 39%, which was attributed to the elimination of HA (hydrophobic substances) during the EC process, which was precisely what was sought with this treatment [35]. A similar behavior has been reported in other investigations using the EC process with Fe anodes in stabilized leachates. [PERSON] et al. [22] increased the CODb content from 6% to 17%. The CODp decreased from 48% to 23% owing to the removal of colloids [78]. CODsb decreased from 51% to 49%, which could be explained by the fact that the EC process had a marginal effect on the soluble fraction because the destabilization process preferentially affected the particulate fraction [96]. Under optimal conditions, the EO process increased CODb from 39% to 58%, which was expected because of the oxidation of non-biodegradable organic matter by indirect electro-oxidation mediated by \(\bullet\) OH radicals. The CODs fraction decreased from 77% to 67%, owing to oxidation of the hydrophilic fraction during the process. The CODsb fraction increased significantly from 51% to 87%, owing to the action of \(\bullet\)OH radicals that oxidized recalcitrant organic matter (HA and AF). Radical \(\bullet\)OH could react with aromatic and aliphatic components, breaking the C-H bond and resulting in a decrease in high-strength non-soluble organic compounds [62, 97]. The results revealed that EO efficiently decreased the non-soluble COD, which could not be removed during the initial stage of EC.
## 4 Blackout Analysis  
The real process leading to a blackout entails a complicated series of actions. Figure 3 illustrates the four phases of the cascade events until a blackout occurs, according to [74; 75; 76]. The first period before any significant disruption is referred to as (1) preconditions. A series of triggering events that destabilize and unbalance the electricity grid cause the blackout to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Remedial Actions** & **Required Time** & **Period** & **Period** & **Period** & **Period** \\ \hline Predefined automatic action & & & & & \\ (generator output or load & A few sec & X & & & & \\ redistribution in case of outage) & & & & & \\ \hline Topology change via automatic & & & & & & \\ switches & A few min & X & X & X & Within the TSO \\ \hline Topology change via manual & Min or hours: depending on the & & & & & \\ switches & availability of the DSO & & & & \\ \hline Changing the transformers’ tap & & & & & & \\ position & A few min & X & X & X & Within the TSO \\ \hline Enable/disable reactive power & & & & & & \\ compensation & A few min & X & X & X & Within the TSO \\ \hline Load redistribution (TSO/DSO & & & & & \\ cooperation) & A few min & X & X & X & Within the TSO \\ \hline Management of loop power flows & & & & & & \\ (e.g., via DC interconnections) & & Min & & & & \\ \hline Regulation of reactive power & & & & & & \\ (MVar) by the unit generators & A few min & X & X & X & Within the TSO \\ \hline Pumping start & A few min & X & X & X & Within the TSO \\ \hline Internal redistribution unit & & & & & & \\ generators & A few min & X & X & X & Within the TSO \\ \hline Start/shutdown of unit generators & Min or hours: depending on the & & & & \\ unit & & & & & \\ \hline Internal redistribution of the unit & & & & & \\ generators of neighboring TSOs & A few min & X & X & X & Between TSOs \\ \hline Load recovery & & & & & & \\ \hline Limiting the exchange power & & & & & & \\ ability & & & & & \\ \hline Manual load shedding, based on & & & & & & \\ contracts & A few min & X & X & X & Within the TSO \\ \hline Cross-border power exchange & & & & & & \\ regulation & & & & & \\ \hline Automatic/manual off-contract & & & & & & \\ load shedding & A few min & X & X & X & Within the TSO \\ \hline Voltage regulation (5\%) & A few min & & & & & & Within the TSO \\ \hline (Automatic) disconnection of & & & & & & \\ cross-border lines & A few sec & & & & & \\ \hline Blocking OLTC (on-load tap & & & & & & \\ changer) & A few min & & & & & \\ \hline Automatic load cut-off & A few min & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Remedial actions for the power system in an emergency.  
occur. This initial disturbance spreads through a series of actions that might last anywhere from seconds to hours. Typically, the cascade can be split into (2) steady state (slow) and (3) fast. Unserved loads, power generation shedding, and the number of affected persons can be assessed after the ultimate stage of the blackout. From this point on, the system begins (4) restoration, which might take from minutes to many days.
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 2 BVH-Assisted Computation of the Target Multiple Backscatter Field  
The main task of the target backscatter field calculation is to dynamically record the amplitude and phase of the multiple backscattering that occur between the EM wave and the target in real time. We utilize the RD imaging geometry relation and adopt the SARspatial traversal mode to simulate beam scanning within beamwidth. Then, the intersection lattices of the EM waves with the target surface are obtained to complete the effective representation of the time-varying radiation and shadow areas within the SAR beams. In this process, we use the BVH algorithm to reduce the intersection complexity between EM waves and target model facets as a whole. Then, the task of EM wave transmission simulation can be accomplished by transmitting discrete EM waves only to the latticed radiation area at each sampling moment along the azimuth direction. On the basis of EM wave transmission simulation, the ray tracing algorithm is used to dynamically record the amplitude and phase of multiple scattering of EM waves, including various scattering mechanisms and material properties of the target surface.
## 1 Introduction
### Our Contributions  
In this study, we investigate the development of low-resolution quantized precoders for DFRC systems, emphasizing low-resolution phase quantization precoding to enhance performance and operational efficiency. Specifically, our contributions are as follows:  
* We propose a weighted optimization framework to efficiently manage low-resolution quantized DACs and frame precoding in large-scale MU-MIMO DFRC downlink systems. By minimizing the MSE between transmitted signals and their downlink reconstructions, our approach simultaneously enhances radar sensing and communication objectives. The framework ensures close alignment with radar precoding patterns, promoting an improved peak-to-sidelobe ratio (PSLR) for radar sensing and reducing BER for communication.
* To effectively handle the discrete and non-convex characteristics of the optimization problem, we decompose it into three manageable sub-problems. This is achieved by segmenting the variables into distinct blocks and subsequently updating each block in an iterative manner. This decomposition simplifies the overall complexity, allowing for closed-form solutions for each sub-problem. Additionally, we develop an algorithm based on dynamic projection refinement (DPR) within the ADMM framework to address the non-convex transmission matrix design. Furthermore, we propose a fast gradient descent variant of the DPR algorithm to reduce the computational burden, making it suitable for large-scale MU-MIMO DFRC systems.
* Through extensive numerical experiments across various scenarios, we demonstrate that our proposed method outperforms traditional precoding techniques while maintaining robust radar sensing performance. Even under imperfect channel state information (CSI) conditions, our approach ensures reliable communication performance. Additionally, the designed beampatterns in both symmetric and asymmetric scenarios exhibit excellent PSLR values, confirming the effectiveness of our radar sensing waveforms.  
The structure of this paper is as follows. Section 2 describes the models for radar and communication, establishing the framework for the precoding design challenge. Section 3 provides the mathematical formulation of the intended precoding approach. Section 4 presents the DPR method. Section 5 validates the proposed algorithm through numerical simulations. Finally, Section 6 concludes the paper.
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 4 Discussions
### Limitation and Future Work  
The proposed method in this paper does not require clean data. The network learns the features of the noise from the noise model and makes predictions, essentially employing self-supervised learning. However, this does not mean that our method is fully automatic. It is necessary to adjust the network parameters accordingly to different data features due to the complexity of field data. In addition, the time cost on network training is insufficient for industrial applications. Therefore, future work will focus on improving the efficiency. Meanwhile, different DNN structures to replace U-Net to further improve the results can be considered. Combinations with filter-based methods can also be investigated.
## 2 Sites, Instruments, and Data
### Radiosonde  
A radiosonde (GTS1 digital device) is used to measure meteorological parameters, including the relative humidity (RH). The measurement is performed routinely at Zhongsha Islands Meteorological Observation Station, which is close to Sansha. The radiosonde is mounted on a balloon and launched twice a day at 0715 and 1915 LST. The vertical range resolution is about 10 m.
## 3 Methods
### Study Design  
The study was divided into three phases: co-design, development, and evaluation, based on co-design [36] and design science research frameworks [27] (Figure 1). In the observation phase, we employed a co-design approach to gain insight into HCP needs for an application that would allow them to monitor, interpret, and make health decisions based on smartwatch data. In the systems development phase, we developed a web-based application prototype that used machine learning to help visualize long-term smartwatchdata, analyse health trends (anomaly detection), and allow HCPs the ability to communicate areas of concern to clients/patients. Finally, in the experimentation phase, HCPs evaluated the prototype.  
Attributes from [PERSON]'s Diffusion of Innovation theory [28] were applied in the design and evaluation phases of the prototype to determine how potential end-users would respond to the prototype. The attributes used in this study were relative advantage, compatibility, complexity, and trialability. These attributes were applied through the questionnaires used in both the co-design and evaluation phases of the study, as these attributes improved the understanding of how integrating smartwatch data with EMR relates to healthcare professionals' work practices.  
Relative advantage and compatibility were applied in the co-design phase of the study [28]. Relative advantage is the degree to which an innovation is perceived as better than the idea it supersedes. Compatibility is how consistently the innovation is perceived as compatible with existing EMR systems. Complexity and trialability were used in the evaluation phase of the study. Complexity is the extent to which an innovation is perceived as challenging to understand and use. Trialability is whether an innovation may be trialled and modified. Observability is the degree to which the innovation results are visible to others. The observability attribute was not yet applicable in this study as the prototype was not integrated into the EMR for automatic documentation.
# In Situ Annealing Behavior of Cu Thin Films Deposited over Co-W Diffusion Barrier Layers
## 3 Results  
### Cu/Co-W  
#### 3.1.1 10 \({}^{\circ}\)C/min Heating Rate  
The surface of the Cu film starts exhibiting grain growth at 200 \({}^{\circ}\)C when a 10 \({}^{\circ}\)C/min heating rate is applied, as seen in Figure 1b. The onset of dewetting at this heating rate was found to be in the 400 and 450 \({}^{\circ}\)C range. At the end of the isothermal step of the latter temperature, the Cu film was retracting its coverage over the Co-W substrate. The rate at which this front retracts, consuming the Cu film, is very fast. As temperature rises, abnormal grain growth progresses until the stage seen in Figure 1i, where nearly all the Cu film has diffused to form mostly large islands. This type of island geometry has been calculated to present a lower energy state than a thin, continuous film, in line with total energy minimization [17]. The geometry of the large Cu grains present suggests that the dewetting process has not concluded, seeing as the particles are not yet completely round.  
There were no visible indications of Cu dewetting under 400 \({}^{\circ}\)C, with Figure 2 showing dewetting of the Cu layer occurring quickly once conditions for its onset are met, during the 450 \({}^{\circ}\)C isothermal step. This sequence shows how abnormal grain formation and growth are stimulated by lateral diffusion of the Cu atoms disposed throughout the previously compact film, even at large distances from where the atoms are being displaced, as noted in the highlighted areas. This behavior suggests that lateral distance is not the main determining factor affecting Cu-atom diffusion throughout the film, with favorable crystallographic  
Figure 1: Surface of the Cu film deposited over a Co-W layer from 150 \({}^{\circ}\)C (**a**) to 550 \({}^{\circ}\)C (**i**), using a 10 \({}^{\circ}\)C/min heating rate. The figures illustrate the progressive microstructural variation with temperature increases of 50 \({}^{\circ}\)C.  
orientations exerting an important effect in determining which grains grow and which grains are consumed to foster the grain growth [18; 19].  
The evolution of an area of the film after one minute at 450 \({}^{\circ}\)C can be seen in Figure 3. It is observable that even in the initial stages of dewetting, the set of conditions for the formation of very small vestigial Cu grains has already been fulfilled. This observation is opposed to the hypothesis of these smaller grains being formed only during the final stages of the process, when most of the Cu film has already been consumed to feed the growth of the Cu islands at high temperatures.
# Parameter Analysis of Resilient Precast Concrete Beam-Column Joints
## 4 Parametric Analysis of the REDDC
### Influence of the Number of the Openings in the H-Shaped Steel  
In order to investigate the effect of the number of the web openings on the mechanical properties of the device, two different types of web openings, designated as REDDC-N1 and REDDC-N3, were proposed on the basis of specimen REDDC-N1, which had two web openings, as shown in Figure 18. In the REDDC-N1 model, the web consisted of a rectangular opening with a height of 50 mm, whereas the REDDC-N3 specimen incorporated three rectangular openings with chamfered edges. To ensure a valid comparison of the mechanical behavior under identical shear capacity conditions, the opening length and the total width of the openings were set to the same values for all three models.  
In Figure 19a, the hysteresis curves of the three sets of models are compared. The results show that despite the similarity in shape between the three finite element models, the peak load carrying capacity exhibited a gradual decline as the number of openings increased. Specifically, the bearing capacities of REDDC-N1, REDDC-N2 and REDDC-N3 were 59.56 kN, 51.58 kN and 40.36 kN, respectively. Compared to the single-hole case, the bearing capacities of REDDC-N2 and REDDC-N3 decreased by 7.98% and 19.20%, respectively. However, the reduction in the load-bearing capacity was accompanied by an increase in the energy dissipation capacity, which was observed to be 28.48% and 98.17%, respectively (Figure 19b).  
Figure 16: Comparison of the finite element modeling results for the models with different H-shaped steel opening widths.  
Figure 17: Mises stress contours at 40 mm displacement.  
In conclusion, the finite element models REDDC-N1 and REDDC-N2, which employed different types of openings, showed superior mechanical properties. In contrast, the REDDC-N3 model exhibited an enhanced energy dissipation capacity, although its load carrying capacity was comparatively lower.
## 4 Results and Discussion
### Results  
The results of the conducted experiments are summarized in Table 3, where the following terminology is used:  
* \(+\) indicates that a particular output variable increases when values of the selected parameter of domain 1 are higher (l represents linear increase; n--nonlinear increase);
* \(-\) indicates that a particular output variable decreases when values of the selected parameter of domain 1 are higher (l represents linear decrease; n--nonlinear decrease);
* 0 represents no impact of the selected parameter of domain 1 on a particular output variable;
* \(\approx\)0 represents a negligible impact of the selected parameter of domain 1 on a particular output variable. Analysis of Table 3 can lead to the conclusion that there are groups of output variables which react identically to modifications of all investigated parameters of domain 1. For these output variables (e.g., \(E(CSD)_{\text{d1}}\), \(E(CSD)_{\text{f1}}\), \(E(CDD)_{\text{d1}}\) and \(E(CDD)_{\text{f1}}\)), the columns of Table 3 are the same. From the twelve columns of Table 3 corresponding to different types of CPP parameters, only four are unique. Consequently, there are four groups of CPP parameters which are influenced by changes in the parameters of domain 1 in the same way. Of course, within these four groups, there are differences in the values of particular CPP parameters. For example, \(E(CDD)\) times are always lower than \(E(CSD)\) times for the same types of call scenarios (call disengagement process is less complicated than call set-up process), and mean \(CSD\) and \(CDD\) times for inter-operator calls are higher than those for intra-operator calls (the former involve much more communication between elements). In the next part of this section, detailed CPP results (Figures 4-11) are presented for the abovementioned four groups of CPP parameters--as their representatives, \(E(CSD)_{\text{b1}}\), \(E(CSD)_{\text{f1}}\), \(E(CSD)_{\text{b2}}\) and \(E(CSD)_{\text{f2}}\) are chosen. Moreover, changes in all output variables according to the groups of the input variables described in Section 3 are discussed. The registration and call set-up request intensities (**lambdaR**, **lambda1d** and **lambda2d**; Figures 4 and 5) increase the load of network elements and links, resulting in nonlinear growth of the analyzed CPP parameters. As registration and intra-operator calls do not involve elements of multiple domains, modifications of **lambdaR** and **lambda1d** in domain 1 affect only mean \(CSD\) and mean \(CDD\) times for scenarios in which communication is performed in domain 1 (scenarios b1, d1, f1 and f2). When **lambda2d** (call set-up request intensity for inter-operator calls which use both domains 1 and 2) is increased, the CPP
## 3 Experimental Procedure and Equipment
### Calibration of Turbulence Modeling Constants  
The experimental apparatus consists of core components, an auxiliary system, and an observation system, primarily constructed from plexiglass and high-strength stainless steel.  
The core components include a steel frame, tubing frame assembly, tubing components, and plungers with various slot geometries. The entire structure is built on a 7-m-high steel framework, with its top securely anchored to the ceiling and wall to mitigate structural vibrations, while the base is firmly fixed to the ground using four sets of anchor bolts. The tubing frame assembly comprises a blowout preventer, a well-bottom buffering device, and a plexiglass tubing section with an inner diameter of 60 mm and a wellbore length of 6 m.  
Figure 1: Model constant calibration procedure based on experiment data assimilation.  
The auxiliary system consists of an air compressor, a gas storage tank, and adjustable control valves. The observation system includes a gas flow meter positioned at the gas inlet and pressure transmitters installed at the gas inlet, well bottom, and wellhead.  
The primary equipment parameters are presented in Table 1, while the experimental setup for evaluating the sealing performance of the gas-lift plunger is illustrated in Figure 2.  
According to the optimal diameter ratio proposed by [PERSON] et al. (2018), the outer diameter of the plunger was set to 57.6 mm. Based on the findings of [PERSON] et al. (2018), the rectangular slot, which exhibits the poorest sealing performance, and the arc-shaped slot, which demonstrates the best sealing performance, were selected as the sealing slot structures for the plunger's exterior. The rectangular slot has a depth of 6 mm and a width of 12 mm, while the arc-shaped slot has a radius of 6 mm and is designed as a complete semicircle. The experimental plunger device, as illustrated in Figure 3, is constructed from stainless steel, with each plunger weighing 3.5 kg.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Model** & **Technical Specifications** \\ \hline Air Supply Pump & \begin{tabular}{c} W-1/12.5 \\ (Shanghai Rigao Co., Ltd., \\ Shanghai, China) \\ \end{tabular} & \begin{tabular}{c} Rated discharge pressure \\ of 1.25 MPa \\ \end{tabular} \\ \hline Gas Flow Meter & \begin{tabular}{c} LUGB-25 \\ (Anhui Ruling Meter \\ Manufacturing Co., Ltd., \\ Hefei, China) \\ \end{tabular} & \begin{tabular}{c} flow range 8.8–55 m\({}^{3}\)/h, \\ accuracy 1.5\% \\ \end{tabular} \\ \hline Pressure Transmitter & \
\begin{tabular}{c} QDW90A-RG \\ (Ruigan Electronic \\ Technology Co., Ltd., \\ Huaibei, China) \\ \end{tabular} &
\begin{tabular}{c} 0–1.6 MPa, accuracy 0.2\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main equipment parameters.  
Figure 2: Plunger lifting simulation experiment device diagram.