esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### The Recommended Method
#### 3.3.3 Log-Leaky ReLU Activation
```
Input:\(E\) training iterations, \(\eta\) train size, \(b\) batch size, \(x\) each of training set \(N\), \(m\) training sample of \(x\) image, \(\gamma\) thresholding value, \(c\) feature channel of \(x\), \(\gamma_{c}\) thresholding value of \(c_{th}\), \(i\) width, \(j\) height, \(\text{B}_{ij}\)  Benoulli distribution, \(b_{j}\) bias, \(M_{j}\) collection of channels, \(x_{j}\) j-th input features, \(\alpha\) scaling parameter, \(w\) weight, \(\vartheta\) and \(\beta\) trainable parameters Output:\(y_{j}\) j-th output feature map, \(\hat{P}_{j}\) j-th predicted label for\(e=0,\;1,\;2,\dots,\;E-1\)do for\(i=0,\;1,\;2,\dots,\;\left(\frac{\eta}{m}\right)-1\)do \(y_{j}\leftarrow\;\sum_{i\in N_{j}}x_{i}\times\text{B}_{ij}+\vartheta_{j}\;\; \#\) compute feature maps \(\mu\leftarrow\frac{1}{N_{b}}\sum_{j=1}^{N_{b}}x_{j}\) \(\delta^{2}\leftarrow\frac{1}{N_{b}}\sum_{j=1}^{N_{b}}(x_{j}-\mu)^{2}\) \(\hat{x}_{j}\leftarrow\left(x_{j}-\mu\right)/\sqrt{\delta^{2}+\vartheta}\) \(\gamma_{c}\leftarrow\alpha_{c}\times average|x_{i,j,c}|\;\;\#\) compute soft-thresholding \(y_{j}\leftarrow\gamma_{c}\hat{x}_{j}+\beta\;\;\#\) compute output feature \(g_{log}\leftarrow\sum_{j=0}^{N}w_{ij}\times y_{j}\) \(f_{w}(j)\leftarrow\left(2^{j+1}log_{e}2\right)\times f_{w}(2^{j})\;\#\) compute log-domain feature \(\hat{P}_{j}\leftarrow\max\left(\left(\sum_{i=1}^{N_{b}}x_{i}log_{2}e\right),1\right)\;\#\) compute log-domain softmax endfor endfor
```  
**Algorithm 1** Training of Log-DRSN
## 1 Introduction
That study also pointed out the discrepancies in existing engineering calculations in certain scenarios, echoing the need for improved calculation methods or optimized closure strategies. [PERSON] [29] proposed a two-stage valve closure strategy, verifying its effectiveness in mitigating the maximum water hammer pressure for micro-hydropower systems, and further discussed the coordination between the closing angles and durations at each stage, providing a significant reference value for enhancing water hammer control. Currently, a wide variety of water hammer simulation software packages is available. AFT Impulse excels in modeling water hammer and transient flow and is commonly used in complex systems such as nuclear power and petrochemical pipelines. Allevi offers strong computational and visualization capabilities in scenarios involving wave interference, multi-branch networks, and simultaneous valve operation at multiple points. Other software products, such as Flowmaster, WANDA Transient, and TSNet, also demonstrate good modeling and simulation capabilities in specific fields or under particular conditions [30; 31; 32; 33; 34; 35]. Bentley Hammer was selected for this study because of its proven track record in hydraulic infrastructure projects and its alignment with the engineering constraints of our specific case study. This study employed two-dimensional simulation software to perform numerical modeling and water hammer analysis of the water supply pipeline of the New Longkou Hydropower Station and investigated the effects of valve closing time and closure methods on pipeline water hammer pressure. The study examined water hammer pressure variations at different branch-line valves, identified the optimal valve closure procedures under worst-case conditions, and used CFD simulations to analyze the dynamic behavior of water hammer across varying quantities, diameters, and distances of branch pipes. This research work clarified the impact mechanisms of valve closure procedures on water hammer pressures at both ends of the valve under different branch pipe characteristics, providing technical and theoretical support for similar forkpipe water transfer projects and helping to prevent excessive water hammer that could disrupt the normal operation of a project.
## 1 Introduction
The most used OPA, uA741 was studied under neutron and gamma rays, and results showed that the frequency behavior of the IC i.e., the gain-bandwidth product and slew rate, degraded after irradiation [19]. These changes in the gain and slew rate will affect the shape of the output signal; for example, a sinusoidal signal became a sawtooth wave due to these changes. In reference [16], LM124 from three different manufactures was irradiated under 1 MeV neutron radiation at two different fluences of \(1\times 10^{12}\) ncm\({}^{-2}\) and \(5\times 10^{12}\) ncm\({}^{-2}\), respectively. All three ICs showed a decrease in the supply bias current, open-loop gain, and slew rate, except one of the devices which showed an increase in the slew rate after irradiation, due to the increase in the base current of the transistor used in the buffer stage. Most of the ICs mentioned above are designed using bipolar transistors, however, neutrons can also significantly affect the characteristics of the metal oxide semiconductor devices (MOS) [22; 23; 24]. Neutrons are non-ionizing particles which upon interaction with electronic devices causes displacement damage (DD) or non-ionizing energy loss (NIEL) damage [25]. Both forms of damage will affect the properties of bipolar, MOS as well as the passive components [25]. The silicon dioxide layer in Metal Oxide Semiconductor Field Effect Transistor (MOSFET) is most sensitive to neutron radiation. Neutron irradiation creates oxide trapped charges and interface traps which affect the characteristics of MOSFET significantly [22; 26]. These traps resulted in neutron-induced oxide degradation in MOSFET as observed by [PERSON] et al. [27]. Gallium nitride (GaN) is emerging as one of the promising technologies for harsh environments as it is more reliable under radiation as compared to the silicon counterpart [28; 29; 30]. The mean displacement energy of GaN is higher (approximately 21.3 eV) than the silicon (approximately 11.07 eV), and there is no oxide layer beneath the gate electrode of GaN High Electron Mobility Transistor (HEMT), hence it performs better in the radiation environment [31; 32]. Low noise and high-speed operation are other advantages of GaN which makes it a more viable solution for designing a readout circuit for high detection rate radiation detectors. Due to its low noise characteristics, GaN has already been used for designing low noise amplifiers [33; 34; 35]. In view of the aforementioned advantages, we developed an operational amplifier (OPA) using GaN transistors and used it to design a readout circuit for prompt gamma signals. Such a GaN-based OPA is the first of its kind reported, to the best knowledge of the authors.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
###### Abstract  
Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (\(\text{VRAE}_{t}\)) and re-Scaling Variational Recurrent Auto Encoder plus time (\(\text{S-VRAE}_{t}\)). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the \(\text{S-VRAE}_{t}\) embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the \(F_{1}\) score in the task of classifying exoplanets. Moreover, the \(\text{S-VRAE}_{t}\) model increases all the advantages of \(\text{VRAE}_{t}\), achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel-Agl fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.  
variational auto-encoder; representation learning; transit model; light curve; unsupervised learning +
Footnote †: journal: Physics Letters A
## 5 Conclusions  
In this study, we proposed a circular sector-shaped dipole antenna combined with a meandered arm, which is based on the AMC. An AMC unit cell is designed using a high-permeability ferrite in the HF, VHF, and UHF bands. The circular sector-shaped dipole antenna is combined with the AMC to achieve a low-profile characteristic. The designed antenna improved the operating bandwidth in the HF, VHF, and UHF bands by implementing optimized meandered arms. The VSWR frequency bandwidth ratio of 3.5:1 or less of the designed antenna is 25.8:1, and the size is \(0.02\times 0.02\times 0.0007\)\(\lambda^{3}{}_{L}\) (\(\lambda_{L}\) is the wavelength at the lowest operating frequency). The designed antenna was compared with a commercial reference antenna to verify its performance. For these simulation and measurement results, the proposed antenna can be used as a receive antenna that requires low-profile characteristics to be mounted on a small moving object, such as a ground vehicle.  
Conceptualization, D.H.L. and K.C.H.; methodology, D.H.L.; software, D.H.L.; validation, D.H.L., O.H.K. and W.B.P.; formal analysis, D.H.L.; investigation, D.H.L. and O.H.K.; resources, D.H.L. and W.B.P.; data curation, D.H.L.; writing--original draft preparation, D.H.L.; writing--review and editing, D.H.L., O.H.K., W.B.P. and K.C.H.; visualization, D.H.L.; supervision, D.H.L.; project administration, K.C.H.; funding acquisition, K.C.H. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Measured received power of a proposed antenna and reference antenna.  
**Funding:** This work has been supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development (UD190033 ED).  
**Institutional Review Board Statement** Not applicable.  
**Informed Consent Statement** Not applicable.  
**Data Availability Statement** Not applicable.  
**Conflicts of Interest** The authors declare no conflicts of interest.
## 2 Microgrid Topology  
MG has been previously defined as a small controllable power network that is composed of distributed energy resources, load and transmission systems. A basic formulation scheme of an MG is presented in Equations (1)-(3). The sets G, D, and SS, represent generation units, demands, and storage systems, respectively. In G, several generation units can be installed in MG. This includes RES (e.g., Photovoltaic (PV), wind), Diesel Generators (DGs), Main grid source, and other units. A classification of the demand side helps in facilitating the calculation and modeling process.  
\[\text{G}\ \equiv\left\{\text{G}_{\text{pv}},\ \text{G}_{\text{wind}},\ \text{G}_{\text{DG}},\ \right\} \tag{1}\]  
\[\text{D}\ \equiv\left\{\text{D}_{1},\ \text{D}_{2},\ \text{D}_{3},\ldots,\text{D}_{ \text{m}}\right\} \tag{2}\]  
\[\text{SS}\ \equiv\left\{\text{SS}_{1},\ \text{SS}_{2},\ \text{SS}_{3},\ldots,\ \text{SS}_{n}\right\} \tag{3}\]  
Each generation system could be classified into several units based on the installation strategy. This classification can be modeled as follow:  
\[\text{G}_{\text{pv}}\ \equiv\left\{\text{G}_{\text{pv1}},\ \text{G}_{\text{pv2}}, \ \ldots,\ \text{G}_{\text{pvi}}\right\} \tag{4}\]  
\[\text{G}_{\text{wind}}\ \equiv\left\{\text{G}_{\text{wind1}},\ \text{G}_{\text{wind2}}, \ \ldots,\ \text{G}_{\text{windj}}\right\} \tag{5}\]  
\[\text{G}_{\text{DG}}\ \equiv\left\{\text{G}_{\text{DG1}},\ \text{G}_{\text{DG2}}, \ldots,\text{G}_{\text{DGk}}\right\} \tag{6}\]  
The classification of the generation, consumption and the storage stages is considered a useful and valuable step, especially during simulation modeling and calculation processes.  
For the sake of illustration, we consider a pair of MGs with identical buses, generation and load units, but with different topologies. These topologies are: Bus topology and Ring topology. Figure 2 shows the first microgrid, MG\({}_{1}\) with a bus topology, including three loads and two PV systems. Figure 3 illustrates the ring topology of the second microgrid, MG\({}_{2}\). Both of the topologies have the same parameters of loads, PV, cables and transformers. The rated voltage of the feeder from the main grid is 11 kV. An 11/0.4 kV transformer is installed between the main grid side and low voltage side. Loads L1, L2, and L3 are connected to buses 4, 5, and 6, respectively. PV1 and PV2 have the rated power of 245 kW for each. The Circuits Breakers (CBs) are utilized in order to meet the fault recovery purpose.  
Figure 2: Bus topology of MG\({}_{1}\).
# Deep Q-Network Approach for Train Timetable Rescheduling Based on Alternative Graph
## 1 Introduction
However, this representation of states lacks the entry/exit timing and order of trains at each block, which are important in deciding a good schedule, especially for a railway network with junctions. The current literature defines a policy as the decision rule of a local dispatcher who is responsible for determining the schedule of a single or multiple trains over a small area in a railway network. To directly consider the possible propagation of a delay over the entire network, the arrival and departure times of all trains can be considered as a state [13]. However, this approach was only tested on a simple railway network, where one-way trains run in a single line. Overall, all of the above studies have limitations in applying their reinforcement learning to more-realistic railway networks with multi-aspect signals or junctions where two trains may converge or diverge. To overcome these limitations, we propose a reinforcement learning approach in which the alternative graph is used for defining the states and actions. More specifically, we define the state using the current topology of the alternative graph and the action for the selection of an alternative arc on the alternative graph. This enables our model to reflect train movement and conflicts between trains at the microscopic level. Furthermore, the proposed method is also applicable to local areas with high traffic density and complex infrastructures such as signaling systems and junctions. The remainder of this paper is organized as follows. This section continues with a formal statement of the TTR problem based on the alternative graph and the necessary notations. The proposed framework of reinforcement learning is described in Section 2. In Section 3, we demonstrate the numerical results, which verify the performance of the proposed method. Finally, in Section 4, we give some concluding remarks.
## 4 Results
### Visual-Based Ship Position Estimation Experiment  
To validate the accuracy of the ship position estimation method in this study, we also recorded the real-time position and attitude data of the host ship and the target ship in real time during the video data collection and then uploaded them to the server for storage. The actual GPS data uploaded by the target ship serves as a ground truth for comparison. To facilitate the comparison between the real value of longitude and latitude and the measured value extracted by vision, systematic sampling is performed according to the GPS frequency of the target ship to ensure that the collected position information can be matched frame by frame. To verify the robustness and accuracy of the position estimation method, this paper selects videos recorded in different navigation states for position estimation experiments. In addition, the mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and mean relative position error (MRPE) are also introduced in the experiment to comprehensively evaluate the position estimation method proposed in this paper.  
Figure 16: Tracking performance comparison of four state-of-the-art object trackers in Scene-4.  
Figure 17: Tracking performance comparison of four state-of-the-art object trackers in Scene-6.  
compared to other scenarios. Nevertheless, our framework demonstrated strong robustness in Scene-3, achieving an MAE of 5.6403 \(\times\) 10\({}^{-6}\) and MSE of 4.0986 \(\times\) 10\({}^{-11}\)\(\circ\) and keeping the MRPE within 8.76%. These results indicate that the proposed framework can achieve high-precision target position estimation even in complex scenarios, providing stable and reliable trajectory features for subsequent multi-source data association.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video** & **Feature** & **MAE (\({}^{\circ}\))** & **MSE (\({}^{\circ}\))** & **MAPE (\%)** & **MRPE (\%)** \\ \hline Scene-1 & Coordinates & \(1.68\times 10^{-6}\) & \(4.25\times 10^{-12}\) & \(3.37\times 10^{-8}\) & 4.44 \\ \hline Scene-2 & Coordinates & \(2.39\times 10^{-6}\) & \(8.16\times 10^{-12}\) & \(3.91\times 10^{-8}\) & 4.4 \\ \hline Scene-3 & Coordinates & \(5.64\times 10^{-6}\) & \(4.1\times 10^{-11}\) & \(8.2\times 10^{-8}\) & 8.76 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The MAE, MSE, MAPE, and MRPE values for target ship position estimates in multiple scenes.  
Figure 19: The visual position estimation result of the ‘Baymax’ MASS in scene-2.  
Figure 20: The visual position estimation results of the ‘Baymax’ MASS in Scene-3.
## 2 Testing Apparatus and Methods
### Experiment Program  
#### 2.2.1 Soil Specimen  
The soil sample adopted in this test is from Pingkan transportation tunnel, Guangxi Province, China, the location of Pingkan tunnel is shown in Figure 4a. Pingkan tunnel, with the length of 3276.5 m, a maximum burial depth of approximately 120 m, is located in the mountainous region, where the karst landforms are extremely developed. The karst fissures and fill-type cavity are widely distributed along the tunnel axis. In addition, the terrain of mountain summit is in favor of water collection and the rainfall is abundant as well. Several large-scale water-sand inrush accidents occurred during construction process, as shown in Figure 4b. This soil sample is widely distributed in southwest China, and qualified with a broad representation of sandy-type soil filled in karst cavities [20], the physical properties are listed in Table 1 and Figure 5.  
Figure 3: Principal part of the seepage test apparatus.  
Figure 2: Schematic diagram of the seepage test apparatus.  
Figure 4: Event introduction of Pingkan tunnel. (**a**) Location of Pingkan tunnel [21]; (**b**) Water–sand inrush disasters in Pingkan tunnel.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sand Grains Density \(\rho_{\text{s}}\)(kg/m\({}^{3}\))** & **Maximum Void Ratio \(\mathbf{e_{\max}}\)** & **Minimum Void Ratio \(\mathbf{e_{\min}}\)** & **Moisture Content \(w\)(\%)** & **Uniformity Coefficient \(\mathbf{C_{u}}\)** \\ \hline
2670.0 & 1.15 & 0.40 & 16.5 & 6.02 \\ \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the soil sample.  
Figure 5: Grading curve of the soil sample
## 3 Application Examples
### Evaluation of Fracture Effectiveness  
The fracture effectiveness index, which was constructed using fracture parameters from both single and multi-well data, was compared with other multi-scale logging data to assess the suitability of using it for reservoir evaluation.  
According to Equation (1), the calculation result is dimensionless. The reservoir is considered more effective when the index is larger. Using Well A as an example, the effective interval of formation lies between 4093-4110 m, and the gas logging shows unusually high levels of hydrocarbon gas. The deep and shallow resistivity values are low, with an apparent positive difference in amplitude. For array acoustic waves, transverse waves and Stoneley waves have low wave amplitude, but high attenuation. In the context of electric imaging for fracture development, the static-static diagram shows high fracture parameters with a fracture effectiveness index of 0.73, indicating good formation effectiveness. Conversely, the 4050-4093 m section exhibits a fracture effectiveness index of 0.45, indicating a relatively poor reservoir (Figure 6).  
The effectiveness index for fractures shows a significant correlation with the array acoustic effectiveness index and other logging data. This not only proves the accuracy of fracture identification but also enables effective formation identification through the effectiveness index in a quantitative manner.  
Multi-well formation effectiveness was evaluated using B, D, and E wells as examples, via exploring the differences between deep and shallow resistivity in conventional logging, fracture density and length in imaging logging, shear wave amplitude and attenuation ratio in array acoustic logging, and the productivity index of test data.  
Based on the Table 2, the multi-well formation's effectiveness is highest in B, D and E wells, as evidenced by their fracture effectiveness ratios: less than 0.4, 0.5-0.7, and more than 0.7 respectively. These ratios correspond with the wells' respective productivity indexes of 219.3, 58.2, and 2.8. Thus, the fracture effectiveness index is a useful tool for quantitatively evaluating multi-well formation.  
Figure 6: Logging Data Processing Results of Well A Buried Hill Section.