esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress of Metal-Air Batteries--A Mini Review
## 2 Cathodes  
### Components of the Cathodic Electrode  
On the cathode, chemistry reactions are ORR and OER. The oxidant is oxygen from air atmosphere. The catalysts are required to lower overpotential of ORR and OER. For an aqueous electrolyte, water loss should be avoided to keep battery stability. Hence, the practical cathode is composed of a catalyst layer, gas diffusion layer, and current collector, as shown in Figure 5. The current collector can be metal and non-metal. Metal current collectors are a porous foam-like metal, for example, Ni, Cu. Non-metal current collectors are carbon-based material, for example, conductive carbon paper, graphitic fiber or carbon cloth. Gas diffusion layer (GDL) and the catalysts are also extremely crucial for cathode performance.  
(1) **Gas diffusion layer**: In MABs, the GDL has multi-folded functions: supporting of catalyst layer; providing oxygen diffusion channels between air and catalyst layer; preventing water getting into battery and electrolyte getting out of battery. To better serve as a bridge between air and catalyst layer, the GDL should be thin, light, highly porous, and hydrophobic. Figure 4b indicates that the ORR in MABs occurs at the three phase boundaries (oxygen air, liquid electrolyte, and solid catalyst). The GDL can simultaneously provide hydrophilic micro-channels to the liquid electrolyte, and hydrophobic layers to prevent electrolyte leakage and good properties of gaseous oxygen diffusion [20].  
(2) **Catalyst layer**: Since the kinetics for the oxygen reaction is naturally slow, bifunctional catalysis is required to improve ORR and OER to improve electrochemical performances of MABs. Based on previous researches, platinum (Pt) [21], ruthenium (Ru) oxides, and iridium oxides (Ir) [22] showed excellent performance in ORR and OER. Furthermore, nanostructures of the following materials also had good catalytic activity, (a) transition metal oxides, MnO, CoO, NiO, etc. [23]; (b) transition metal hydroxide and sulfide, NiCoFe-LDH (Layered double hydroxides) layered double hydroxides [24]; (c) spinel compounds, such as CuCo\({}_{2}\)O\({}_{4}\)[25]; (d) carbon-based materials, such as nitrogen doping carbon [26]; (e) nanocomposite materials mixing ORR catalyst Fe-N-C and OER catalyst NiFe [27].
# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 1 Introduction
These findings not only validate the effectiveness of the Ad-RuLer method in addressing issues of data imbalance, but also contribute a novel perspective to the methodologies for data imbalance processing. To summarize, the main contributions of this research are as follows:  
* To tackle the challenge of data imbalance, we propose Ad-RuLer, an innovative adaptive rule-based oversampling algorithm. This method extracts intrinsic rules from minority class samples and, based on these rules, synthesizes additional minority class instances. * To ensure that the synthesized data maintains complete balance, a random sampling step is incorporated into the rule-based synthesizer to keep the number of minority class samples generated via rule extraction aligned with the majority class samples in the original dataset. * Ad-RuLer is extensively evaluated using various machine learning models and real-world datasets, demonstrating its effectiveness and good performance in comparison to existing mainstream data resampling methods. Our research findings suggest that Ad-RuLer may serve as an alternative option to conventional oversampling techniques. The remainder of this paper is structured as follows: Section 2 introduces the relevant literature; Section 3 delves into the intricacies of Ad-RuLer; Section 4 presents the chosen datasets and our comparative analysis; and in Section 5, we discuss the experimental results and outline future work, while in Section 6, we draw our conclusions.
## 4 Applications to Field Data
### Data Acquisition  
A total of 50 ALESEIS-1C/LF broadband seismic stations produced by Beijing Zhongke Shenyuan Science and Technology Co., Ltd. (Beijing, China) are used for microtremor data acquisition, with built-in detectors of broadband (1-240 Hz) and high sensitivity. Long-spread linear array is selected, with station spacing of 5 m, exploration point spacing of 5 m, sampling rate of 4 ms, and sampling duration of 30 min. During the field work, 50 stations are deployed at one time along a linear survey line using the roll-along acquisition mode. After 30 min of stable acquisition, the stations continue to roll forward to complete profile measurement (see Figure 2). The layout in this way is more convenient and efficient for microtremor survey in complex urban areas with limited sites.  
During data pre-processing, several station signals are formed into an array with the exploration point as the center according to the depth of the target layer, and the dispersion curve is extracted by the E-SPAC method. The number of stations in the array can be determined according to the principle of H = (3-5) \(\times\) R [21] between the detection depth H and the array radius R. Through an inversion of the dispersion curve of a certain exploration point, the apparent S-velocity structure of the underground medium can be estimated, and, finally, the pseudo-2D profile of the apparent S-wave velocity under the survey lines can be obtained.
## 4 Presentation and Discussion of Findings
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Predictor**} & \multicolumn{8}{c}{**Predicted**} \\ \cline{2-9}  & \multicolumn{8}{c}{**Hidden Layer 1**} & \multicolumn{2}{c}{**Output Layer 2**} \\ \cline{3-8}  & **H(1:1)** & **H(1:2)** & **H(1:3)** & **H(1:4)** & **H(1:5)** & **H(1:6)** & **QS** & **CPM** \\ \hline \multirow{8}{*}{Input Layer} & (Bias) & 0.217 & 0.290 & 0.346 & \(-\)0.143 & 0.535 & 0.025 & \\  & CFLOW & \(-\)0.303 & 0.107 & 0.487 & 0.057 & \(-\)0.243 & 0.370 & \\  & HDATA & 0.030 & \(-\)0.661 & \(-\)0.083 & 0.196 & \(-\)0.558 & \(-\)0.461 & \\  & SIMP & 0.062 & 0.514 & \(-\)0.243 & 0.331 & \(-\)0.001 & \(-\)0.187 & \\  & TCACT & 0.019 & 0.520 & \(-\)0.435 & \(-\)0.102 & 0.144 & \(-\)0.562 & \\  & MONLC & 0.034 & 0.202 & \(-\)0.395 & \(-\)0.115 & 0.197 & \(-\)0.007 & \\  & MONMC & \(-\)0.095 & \(-\)0.100 & \(-\)0.377 & \(-\)0.481 & \(-\)0.504 & 0.486 & \\  & MONEC & 0.201 & \(-\)0.882 & 0.183 & \(-\)0.045 & \(-\)0.521 & \(-\)0.212 & \\  & MONOV & \(-\)0.541 & 0.479 & \(-\)0.805 & \(-\)0.505 & \(-\)0.616 & 0.232 & \\  & VARMAN & 0.653 & \(-\)0.441 & \(-\)0.072 & 0.592 & 0.191 & \(-\)0.245 & \\  & COSTR & \(-\)0.365 & 0.277 & 0.432 & 0.932 & \(-\)0.554 & 0.078 & \\  & INCMILE & \(-\)0.806 & 0.405 & \(-\)0.124 & 0.501 & \(-\)0.568 & \(-\)0.113 & \\  & COMPRUN & \(-\)0.115 & 0.431 & 0.098 & 0.185 & 0.456 & 0.277 & \\  & IDCOV & \(-\)0.152 & \(-\)0.071 & \(-\)0.577 & \(-\)0.840 & 0.391 & 0.596 & \\  & FORCOMP & 0.099 & \(-\)0.353 & \(-\)0.690 & \(-\)0.051 & \(-\)0.335 & \(-\)0.061 & \\  & COSTFOR & \(-\)0.338 & 0.381 & \(-\)0.348 & \(-\)0.239 & \(-\)0.014 & 0.793 & \\  & INTERIMV & 0.706 & \(-\)0.094 & \(-\)0.613 & 0.192 & 0.616 & 0.172 & \\  & WORKBU & 0.601 & 0.166 & \(-\)0.934 & \(-\)0.162 & 0.665 & 0.569 & \\ \hline \multirow{8}{*}{Hidden Layer 1} & (Bias) & \multicolumn{8}{c}{} & & & 0.503 & 0.740 \\  & H(1:1) & \multicolumn{8}{c}{} & & & 1.063 & \(-\)1.063 \\ \cline{1-1}  & H(1:2) & \multicolumn{8}{c}{} & & & 0.565 & \(-\)0.617 \\ \cline{1-1}  & H(1:3) & \multicolumn{8}{c}{} & & 0.594 & \(-\)0.536 \\ \cline{1-1}  & H(1:4) & \multicolumn{8}{c}{} & & \(-\)0.463 & 0.485 \\ \cline{1-1}  & H(1:5) & \multicolumn{8}{c}{} & & \(-\)1.046 & 1.069 \\ \cline{1-1}  & H(1:6) & \multicolumn{8}{c}{} & & 0.421 & \(-\)0.421 \\ \hline \hline \end{tabular}  
* In the level 1, weights above 0.400 have been colour coded as MLP in the input layer synaptics leads INTERIMV; WORKBU; INCMILE; VARMAN; MONOV largely leads mostly to H(1:1) and H(1:5). The aforementioned hidden layers links to the output layers. QS and CPM. (Source: Authors’ compilation, 2020).  
\end{table} 
Table 3: Synaptic parameter estimates for level 1.  
model, the CPM play more role in decision making when the profit and loss account preparation is part of the process. From Table 5, there is a confirmatory finding of who makes the most decisions to adopt a post-contract cost planning technique. The probability and pseudo-probability of who likely makes the most decisions when it comes to the choice of post-contract cost controlling techniques are crucial for overall success in the construction project.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 2 Implementation of a Dual-Band RF Wireless Power Transfer (WPT) System
### Design of the Rx Antennas
#### 2.3.3 Dual-Band (2.4 and 5.8 GHz) Patch  
The dual-band Rx antenna for receiving the dual-band wireless power of 2.4 and 5.8 GHz is described in Figure 12. This antenna consists of a stacked two-layer substrate, a ground plane, a 2.4 GHz RHCP patch, and a 5.8 GHz RHCP patch with a truncated square structure [36]. On the top layer (_Layer_-1), the 5.8 GHz patch is located, and it is directly  
Figure 11: Simulated and measured results of the 5.8 GHz patch antenna: (**a**) reflection coefficients; (**b**) realized gain; (**c**) axial ratio.  
Figure 10: Simulated and measured results of the 2.4 GHz patch antenna: (**a**) reflection coefficients; (**b**) realized gain; (**c**) axial ratio.  
fed through a metal via. The 2.4 GHz patch is on _Layer_-2 and is fed by a coupling feeding method with the metal via. This 2.4 GHz patch is rotated 50\({}^{\circ}\) counterclockwise around the metal via in order to reduce mutual interference with the 5.8 GHz patch and to improve the radiation pattern. Both stacked substrates in this antenna are the RF-35 types (\(\varepsilon_{r}=3.5\), \(\tan\delta=0.0018\)) with a thickness of 1.52 mm. Figure 13 shows the simulated and measured results of the dual-band Rx antenna in Figure 12. The measured reflection coefficients are \(-13.93\) dB at 2.482 GHz and \(-11.48\) dB at 5.73 GHz. The measured results of the AR are 2.5 dB and 0.54 dB at 2.482 GHz and 5.73 GHz, respectively. AR bandwidths less than 3 dB are also achieved as 2.481-2.505 GHz and 5.682-5.788 GHz. The RHCP-realized gain in the \(+z\)-direction is measured and found to be 5.01 dBic at 2.482 GHz and 6.27 dBic at 5.73 GHz.
## 3 AWG Evaluation Tools Short Review
Although it is not directly a compactness metric, it provides valuable insights into the amount of material required for the water harvesting process. Research in the field of desicants is highly active, as the effectiveness of the harvesting process system strongly depends on the properties of the adsorbent material, including water absorption capacity, sorption-desorption kinetics, water release temperatures, and durability [35]. Recent studies focus on advanced MOFs [12] and hydrogels [36], which have shown promising results. However, it is worth noting that desiccant materials can be expensive, particularly in the case of some types of MOFs [37]. Therefore, evaluating the material quantity requirements is of the utmost importance. WHR, also referred to as WGR, is fundamental when the size of the AWG should be selected in function of the water needs; the higher its value, the greater the water production over a given time period. It is worth mentioning that, in real-world machines, it is generally expressed as daily water production, rather than as hourly production [38]. Two additional indicators, designed for AWGs employing desiccant materials, are worthy of note. The first, \(\eta_{\text{WHTC}}\), represents the net water mass that can be collected in relation to the quantity that remains entrapped in the desiccant material. The indicator can be used to assess the effectiveness of an adsorption-desorption cycle and the suitability of a particular material for such an application. A lower value indicates a higher amount of water that remains entrapped in the material rather than being released during the desorption stage. Nevertheless, this indicator alone does not provide a metric to judge the cycle from an energy consumption perspective. For that purpose, \(\eta_{\text{WHTC}}\) was proposed [26]. This tool has the same approach as WET, converting the collected water mass into the corresponding energy, by considering the specific heat of condensation. However, unlike WET, it takes into account only the thermal energy required by the desiccant material to release the water. The tool can be very useful for comparing different adsorption-desorption cycles and materials quantitatively but does not provide information about the temperatures involved in the process. It is worth mentioning that one of the main challenges in using desiccants is achieving desorption at low temperatures, rather than minimising the required thermal energy.
###### Abstract  
The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. However, existing image segmentation algorithms still encounter issues related to accuracy, speed, and robustness, as well as a tendency to misdetect or overlook small fractures when applied to logging image fracture segmentation tasks. To address these challenges comprehensively, this paper proposes an end-to-end fracture segmentation algorithm named SWSDS-Net. This algorithm is built upon the UNet architecture and incorporates the SimAM with slicing (SWS) attention mechanism along with the deformable strip convolution (DSCN) module. The SWS introduces a fully 3D attention mechanism that effectively learns the weights of each neuron in the feature map, enabling better capture of fracture features while ensuring fair attention and enhancement for both large and small objects. Additionally, the deformable properties of DSCN allow for adaptive sampling based on fracture shapes, effectively tackling challenges posed by varying fracture shapes and enhancing segmentation robustness. Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. Moreover, serving as a lightweight network solution enables SWSDS-Net's deployment on mobile devices at remote sites--an advancement that lays a solid foundation for interpreting logging data and promotes deep learning technology application within traditional industrial scenarios.  
logging images; fracture segmentation; UNet network; lightweight; attention mechanism
## 8 Evaluation Metrics
### Latency Prediction Model  
In this report, we present a mathematical model for latency prediction. The model aims to estimate the latency of a system based on various input  
* \(N\): Number of data points in the training set.
* \((X_{1i},X_{2i},\ldots,X_{ni})\): Values of the independent variables for the \(i\)th data point.  
Once the regression coefficients are estimated, the model can be used to predict the latency for new data points by substituting the values of the independent variables into Equation (1).  
The mathematical model presented in this report provides a foundation for latency prediction. By utilizing multiple linear regression, we can estimate the latency of a system based on the provided input parameters. Further analysis and refinement can be conducted based on the specific context and data available.
## 2 Materials and Methods
### Segmentation Evaluation Metrics  
The accuracy of the achieved segmentation results \(\mathcal{S}\) was quantitatively evaluated with respect to the real measurement--i.e., the ground truth \(\mathcal{T}\) obtained manually by an experienced biologist--by using the Dice Similarity Coefficient (DSC):  
\[\text{DSC}=\frac{2\times|\mathcal{S}\cap\mathcal{T}|}{|\mathcal{S}|+| \mathcal{T}|}\times 100\,(\%), \tag{3}\]  
as well as the Intersection over Union (IoU) metrics, also known as Jaccard coefficient:  
\[\text{IoU}=\frac{|\mathcal{S}\cap\mathcal{T}|}{|\mathcal{S}\cup\mathcal{T}|} \times 100\,(\%)=\frac{|\mathcal{S}\cap\mathcal{T}|}{|\mathcal{S}|+| \mathcal{T}|-|\mathcal{S}\cap\mathcal{T}|}\times 100\,(\%)=\frac{\text{DSC}}{2- \text{DSC}}. \tag{4}\]
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
## 3 Results and Discussion
### The Influence of the Flying Height on the Back Radiation in HAMR  
As shown in Figure 7, obviously, the radiative heat transfer is decreased when the flying height is increased, and the peaks appear in the same frequencies, but the peaks become smoother. By integrating over the whole frequencies, the total near-field back radiation heat flux can be calculated. Taking the varying flying height from 1 nm to 10 nm, the graph of heat flux varying with flying height is plotted in Figure 8. Obviously, when the flying height is small enough, the contribution of the far-field heat flux can be ignored, and the contribution of the near-field heat flux plays a major role in the total flux. As shown in Figure 8, the near-field heat flux (the red line) is almost overlapped with the total heat flux (the black line). Even under the extremely small flying height, when the flying height is \(d=1\) nm, the heat flux is almost two orders of magnitude larger than that when the flying height is \(d=10\) nm. So, because the near-field radiative heat transfer is very sensitive to the changes in the flying height, slight fluctuations in the flying height can lead to huge near-field radiative heat transfer changes, resulting in thermal deformation of the head, the failure of the near-field transducer, the instability of the flying status, and so on.  
Figure 8: Graph of heat flux varying with flying height.  
Figure 7: Spectral heat flux for varying flying height and frequency.
## 3 Results and Discussion
### Dissolved Trace Elements
#### 3.2.2 Iron  
The mean dissolved Fe concentration at site 1 was significantly higher than at sites 3-7, with a maximum content of 3.71 mg/L in June. Pre-treatment concentrations of dissolved Fe showed highly diverse results ranging from 9.3 to 438 mg/L [18; 19; 20]. Even the maximum level in our study was significantly lower compared to previously published values. The dissolution of Fe is highly dependent on pH and redox potential, and the metal is easily mobilized in acidic settings [37]. Interestingly, the Fe concentrations correspondingly increased in the same period as Mn, even if the extent of the surge was less pronounced. Differences in the increase of Fe and Mn concentrations could be explained by the chemical behavior in terms of the solubility of both metals with rising pH. Whereas the removal of dissolved Mn from the aqueous phase is sufficient at a pH of around 8, Fe precipitates as Fe(III) in Schwertmannite (Fe\({}_{16}\)\({}^{3+}\)[O\({}_{16}\)(OH)\({}_{10}\)(SO\({}_{4}\))\({}_{3}\)]\(\cdot\)10H\({}_{2}\)O) at pH \(<\) 4 and ferrihydrite (Fe\({}^{3+}\)\({}_{10}\)O\({}_{14}\)(OH)\({}_{2}\)) at pH \(>\) 4 [36; 38]. The temporary increase of Fe and other metals as well as the decrease in pH indicated that the MIW treatment was somehow flawed over a certain period (Figure 2).  
The mean concentrations of dissolved Fe in sampling site 2 were not significantly different from site 1. The same reasoning as explained for Mn could be responsible because the dissolution of both metals is a primary indicator for AMD or acidic rock drainage (ARD). The low solubility of Fe (III) became evident in the further course of the river system: dissolved Fe concentrations were significantly lower in sites 3-7. According to the TWQR in the SA-WQG, a variation of the background iron concentration of more than 10 % is undesired. Regardless of the background, the dissolved concentrations varied greatly during sampling and increased by 995 % at site 1 from May to June. We, therefore, conclude that the TWQR was not reached either at site 1 or at the other sampling sites.
# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 2 Related Research
### Vps  
Camera pose estimation can be approximately divided into three categories in the existing VPSs [14; 29]. The structure-based positioning method uses a local function to match the features of a query image and a point on a 3D model from 2D to 3D or an RGB-D image with a 3D model, and the camera pose is estimated. We concluded that the 3D-based method provides more accurate pose estimation owing to the precise model construction and maintenance [30]. Moreover, a model is trained from a specific image, wherein pose information serves as a learning-based localization method, and a scene can be expressed using the learned model. This learning-based positioning method predicts the match for pose estimation [31; 32; 33; 34] or estimates camera poses, such as PoseNet [35], PoseNet2 [36], VlocNet [37], and VlocNet++ [38]. Table 2 presents the localization error of sub-meter-level accuracy 0.25 m using the image learning-based existing CNN models. In VlocNet [37], a high accuracy of 0.048 m was achieved using the Microsoft 7-Scenes dataset. The 7-Scenes dataset uses a dataset consisting of RGB-D images collected from seven different scenes in an indoor office environment.
## 3 Reactive Power Optimization Method Based on Graph Security Reinforcement Learning
### Dual Variable  
During the optimization process, we introduce dual variables and adjust the policy by computing a dual gradient to satisfy the constraint conditions. The dual gradient reflects the discrepancy between the incurred cost and the predefined constraint limit. If a certain cost exceeds the predefined safety constraint threshold \(d\), correction must be performed by adjusting the corresponding dual variable. To strictly ensure voltage security at each node, we set a threshold of \(d=0\) in this study. To maintain the stability of dual variable updates, we adopt the update mechanism defined by Equations (16)-(19). At each update step, the average deviation between the current cost \(R_{c}\) and the constraint limit \(d\) is computed, and the dual variable is adjusted using the predefined learning rate \(\alpha\). Specifically, we first compute the difference between the cost and the constraint limit as follows:  
\[\bigtriangledown_{\lambda}=R_{c}-d \tag{16}\]  
The computed dual gradients are then clamped with a lower bound of zero. This clamping operation is necessary because when the cost is below the constraint limit (i.e., dual_gradients \(<0\)), no adjustment of the dual variable is needed, as the constraint is already satisfied. This operation ensures that the dual variable is only updated when the cost exceeds the constraint threshold.  
Subsequently, the dual variable is updated using the mean of the dual gradients. The update rule is given by  
\[\bigtriangledown_{\lambda}=\text{max}[\bigtriangledown_{\lambda},0] \tag{17}\]  
where \(\alpha\) denotes the learning rate of the dual variable, which controls the step size of the update. After the update, it is necessary to ensure that the dual variable remains non-negative. This is because the dual variable typically represents a non-negative slack variable, which reflects the degree to which a constraint is violated. Maintaining the non-negativity of the dual variable preserves both its physical interpretation and mathematical validity.  
\[\lambda\leftarrow[\lambda+\alpha\mathbb{E}[\bigtriangledown_{\lambda}]] \tag{18}\]  
\[\lambda=\text{max}[0,\lambda] \tag{19}\]