esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 2 Methodology  
### Groundwater Models  
#### 2.1.1 Study Area  
This study was conducted in a region in southeastern South Australia, which is revised by the cases described by [PERSON] et al. [22], measuring 36 km in width from east to west and 42 km in length from north to south. The region primarily features flat terrain, with limestone geology and sandy soil prevalent on the surface. The climate in this area falls within the Mediterranean to temperate climate zone, characterized by hot and dry summers, as well as cool and humid winters. Additionally, there is a gradual north-south gradient of evapotranspiration, with potential evapotranspiration ranging from approximately 1400 mm per year in Mount Gambier to about 1700 mm per year in Keith, located north of the study area.  
In this region, groundwater flows from east to west, with higher elevation in the eastern part (Figure 1). The area features sand dunes and interdunal plains resulting from past marine incursions. Based on the hydrogeological cross-sections in the report of [PERSON] et al. [23], the representative aquifer system at our study site comprises the unconfined Tertiary Limestone Aquifer (TLA) and the confined Tertiary Confined Sand Aquifer (TCSA). The shallow TLA is characterized by karst limestone geology, commonly referred to as the Gambier Limestone Aquifer. This limestone aquifer is extensively utilized for agricultural irrigation, as well as for livestock, domestic, and other purposes.  
Figure 1: Map of the study area. Red line represents the hydrogeologic section of the representative model location. Black lines and blue lines represent the groundwater heads and drains, respectively. Groundwater wells and petroleum wells are indicated by grey dots and orange dots, respectively [2; 22].  
The region has a history of flooding, and intentional lowering of groundwater levels in low-lying areas has been ongoing since 1863 through the implementation of agricultural drainage channels, primarily built between 1949 and 1972. The area is marked by numerous lakes and wetlands, sustained by groundwater inflows, maintaining water levels either throughout the year or seasonally. Primary land uses in this region include dryland grazing, irrigated pastures and vineyards, cork and hardwood forestry, as well as native vegetation.  
Due to the numerous factors influencing groundwater flow in this region, there is a considerable level of uncertainty associated with instantaneous changes in hydraulic head. This study selects this area as the simulation focus to test the capability of the surrogate models in addressing real-word problems and to improve their applicability in predicting transient flow in complex groundwater models.
###### Abstract  
The concentric face gear split-torque transmission system (CFGSTTS) has the advantages of a large reduction ratio and high power density. The CFGSTTS has considerable potential to be applied in helicopter main reducers. As such, in this study, we analyzed the load distribution characteristics of a dual input-dual output concentric face gear split-torque transmission system. A load-dependent time-varying meshing stiffness surrogate model was designed based on a feedforward neural network. The difference in the meshing stiffness between the pinion driving and face gear driving was analyzed. The coupled lumped parameter dynamic model of the bending-torsion-axis-pendulum was developed through [PERSON]'s second law, and the influences of the time-varying meshing stiffness, backlash, comprehensive transmission error, support stiffness, and damping were considered. Finally, the impact of the support stiffness on the load-sharing coefficient was analyzed. An optimization model was constructed with the objective function of minimizing the sum of the load-sharing coefficients and was solved by the marine predator algorithm. In addition, the validity of the optimization results was verified with a finite element model. The results indicate that (1) smaller support stiffnesses of input gears benefit the corresponding load balance; (2) the support stiffnesses of the face gears have different laws of influence on the load-sharing coefficient at the input gear and idler, and the support stiffnesses of the other gears need to be comprehensively considered; (3) the larger supporting stiffnesses of the idler gears and tail gear are beneficial for decreasing the load-sharing coefficient at the input gear; and (4) the optimized load-sharing coefficients at Input Gears 1 and 2 and the idler gear decrease by 23.7%, 24.2%, and 4.6%, respectively.  
oncentric face gear; split torque; surrogate model; load sharing; optimization Article
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 6 Application and Evaluation
### Summary and Discussion  
To apply our approach to performance monitoring in AWS Step Functions, we translate the JSON-style specification of a workflow into a fixed transition system in a suitable level of abstraction, maintain two sets of execution summaries, which are created from the historical and recent workflow execution traces, respectively, and employ DFA to specify trace components, whose time metrics are interesting to monitor. With these three inputs, the building of the SMC models and the computation of the FPT moments in those models are automated and efficient. We can use the FPT moments as time metrics to define the baseline and current performance. Alternatively, we can further infer the FPT quantiles from the FPT moments using the method of moments. Therefore, our approach is a useful monitoring technique for AWS Step Functions, and the evaluation of computation time also demonstrates its efficiency in general.  
It is noteworthy that, although this case study focuses on AWS Step Functions, our approach can be applied to other realistic workflow systems. The process follows the same steps, which is the preparation of the three inputs for model building (c.f., Section 6.4). However, the translation of workflow specifications into transition systems and the collection of execution statistics are platform-dependent. In AWS Step Functions, workflows are specified in the JSON format, and execution statistics can be retrieved by a specific API.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Cnn
The results showed that this method greatly reduced the number of parameters and inference time. [PERSON] et al. [52] proposed a multiscale CNN for constellation-based modulation classification. The network structure was composed of multiple processing modules to fully understand more internal features from images similar to constellations. At the same time, the convolution gray image was developed and the convolution kernel was used to overcome the shortcomings of the existing imaging schemes. The average classification accuracy of the network trained on convolution gray image dataset was about 97.7% at 4 dB SNR. [PERSON] et al. [25] proposed an AMC method based on CNN, which used RT of constellation diagrams as input. For several modulation types, constellation random transform improved the performance and accuracy of the classifier. [PERSON] et al. [53] proposed a dilated CNN for AMR. Firstly, the one-dimensional modulation signal was converted into a two-dimensional asynchronous delay histogram. Then, it is input to CNN based on dilated convolution kernel. Experiment results showed that this AMR method significantly improved the recognition accuracy in low SNR. [PERSON] et al. [54] proposed an AMR method, which includes a multi-scale convolution deep network with attention module and a shallow network for recognizing modulation types that were easy to misclassify. The overall recognition accuracy could reach 98.7%. It performed well in recognizing high-order and analog signals. [PERSON] et al. [56] proposed five CNN models, including ResNet18, SqueezeNet, GoogleNet, MobileNet and RepVGG. The experimental results showed that the SqueezeNet model achieved the highest accuracy of 97.5% when the SNR was +8 dB. Based on the evaluation results of a single model, an ensemble learning method was proposed. Experimental results showed that ensemble learning improved the accuracy of modulation recognition. Weighted ensemble had better performance than the unweighted model.
# Burst Detection in Water Distribution Systems:
## 2 Methodology
### Water Consumption Hydraulic Simulation
The latter is defined adopting the Walski and Pelliccia formulation [43]:  
\[p_{ij}=pd_{ij}+pl_{ij}, \tag{5}\]5  
where \(c_{1}\) and \(c_{2}\) are two correction factors respectively depending on the pipe material and its state (presence of previous breaks), and on the pipe diameter. \(y\) is the current year and \(k\) is the installation year of the pipe, while \(a\) and \(b\) represent the regression coefficients [43] depending on the pipe material. When a burst is generated in a pipe according to Equations (18) and (19), the punctual water losses are lumped at one of the ending nodes of the selected pipe. Thereafter, the amount of water losses is chosen by randomly selecting a pair of geometric parameters \(L_{c}\) and \(A_{0}\) able to generate an outflow ranges between a minimum and maximum flow rate thresholds. Eventually, the burst duration is fixed according to the magnitude of the flow rate loss. The realistic bursts modeling and the randomness introduced by the bursts generators allows for producing suitable time series for the training of anomaly detection algorithms.
## 3 Case Study
### Used Technologies  
The relational database used for the concrete implementation is the ORACLE 19 database [24], standard edition 2. The database was installed on the Linux Enterprise operating system (CentOs 7). Apache http server and Tomcat 9 application server were installed on the same server. Under the Tomcat Application Server, the Oracle REST Data Service (ORDS) was launched to create dynamic web pages from database procedures. On the client side Highcharts 8 was used, a software library written in pure JavaScript for chart creation. For the map, the Google Maps API v3 JavaScript library was used. Some general JavaScript libraries such as jQuery and Backgrid were also used. JSON [25] was used as the data transfer format between server and client.  
In our approach, we used JavaScript libraries both for drawing maps and displaying spatial data and for visualizing data. On the web interface, there is a dynamic data filter to select a data subset for visualization. The data were retrieved via an asynchronous XMLHttpRequest or a newer Fetch API. Only alphanumeric data in JSON format were transferred between server and client.  
We can identify two main components in loading the data of a web page:  
* Loading CSS elements and JavaScript libraries as static content from the web server. The web browser cache can be used to further reduce the amount of data transferred;* Loading a subset of data for visualization in response to specific user requests. The user selects the subset of data using adoptive data filters provided on the page. See Figure 7 for a schematic representation of the page loading process.  
Asynchronous XMLHttpRequest or Fetch API was used to load data in the background. Synchronous data loading was disabled in some browsers. Appropriate event handling is required when loading data asynchronously. Only when the current process of fetching and drawing data is complete, can the user select another dataset or parameter and start a new cycle. The JSON format was used for data transfer. Although the JavaScript method is called \"XMLHttpRequest\", the JSON format was used and not XML. The JSON format allows arrays and complex structures and is more compressed than XML.
## 7 Results and Performance Evaluation
### Evaluation of Base Station-Centric (Centralized) Versus Node-Centric (Decentralized) Approaches  
We will now examine the performance of our proposed base-centric (centralized) approach vis-a-vis the existing approaches, all of them being node-centric (decentralized) from various angles as follows.
## 2 Derivation of Functionality Loss Evaluation Model for Railway Network Components
### Derivation of Functionality Loss Curves for Railway Network Components  
To derive functionality loss curves, the evaluation period (\(t_{f}\)) for assessing the functionality loss of the railway network must first be defined. The degree of functionality loss for railway structures is calculated as the area of functionality loss compared to a fully functional state, which can vary depending on the evaluation period. As railway structures recover their functionality over time, approaching a value of 1, longer evaluation periods tend to yield functionality loss values closer to 1. Thus, selecting an appropriate evaluation period and deriving functionality loss curves for that period is essential. Once the evaluation period for calculating the functionality loss of railway network components is determined, the extent of functionality loss caused by earthquakes during this period can be quantitatively assessed. In this study, as shown in Figure 7, the evaluation period for functionality loss was set to 100 days. Functionality loss values for railway network components over the 100 days following an earthquake were calculated, as expressed in Equation (7). Here, the 100-day functionality loss evaluation period was determined with reference to the expected recovery times for transportation infrastructure facilities such as railway structures [20].  
\[\text{Functionality Loss}=1-\frac{\int_{t_{0}}^{t_{f}}Q(t)dt}{t_{f}-t_{0}} \tag{7}\]  
where \(t_{f}\) is the target time for evaluating functionality loss, and \(t_{0}\) is the time of the earthquake occurrence.  
Repeating this process for various input seismic intensities allows functionality loss curves to be derived. The functionality loss curves for each railway network component are shown in Figure 8. Compared to functionality curves, functionality loss curves provide a more intuitive understanding of how much functionality is lost by structures due to seismic intensity. For example, for railway bridge structures, approximately 25% functionality loss occurs at a seismic intensity of 0.4 g, and around 60% functionality loss occurs at 0.8 g. For embankment structures, functionality loss rates tend to increase as embankment height increases. For tunnel structures, relatively higher functionality loss rates were observed in deeper structures.  
Figure 7: Determination of functionality loss for each seismic load (input PGA = 0.4 g; duration = 100 days).
###### Abstract
[35] proposed a 2D-DOA estimation algorithm based on the symmetric rotation invariable relation of non-circular signal characteristics, which is able to directly estimate the target position by constructing an objective function integrating the information of multiple observation stations without relying on the distribution model. [PERSON] et al. [36] proposed a 2D-DOA estimation algorithm based on a double parallel line array combining rotation invariance and propagation operator method. This algorithm does not rely on the distribution model, which avoids spectral peak search and feature decomposition of the high-dimensional sample covariance matrix required by the traditional algorithm. However, an additional angle-matching process is required, and the array structure is complex. Since the ESPRIT algorithm has the advantage of automatic matching of two-dimensional parameters, a 2D-DOA estimation algorithm that could be accurately estimated in a multipath environment was proposed by [PERSON] et al. [37]. However, in the case of coherent sources [38; 39], the algorithm needs traditional decorrelation processing such as spatial smoothing. In contrast, sparse reconstruction algorithms with better performance are rarely used in 2D-DOA estimation. Inspired by the above analysis, a 2D-DOA estimation method based on total variation (TV-norm)regularization is proposed for spatially extended sources. The spatially extended sources are remodeled by the grouping and correlation features. A planar array two-dimensional manifold matrix and its over-complete representation are constructed to achieve sparse representation of sound sources. The total variation regularization matrix corresponding to the structural characteristics is constructed to facilitate the formation of piecewise constant or linear solutions. Combining with the total variation regularization and LASSO, the 2D-DOA total variation sparse estimation model is constructed, and the accurate orientation estimation is realized by the convex optimization algorithm. This method does not prescribe the eigen-decomposition of the high-dimensional sample covariance matrix and avoids the extra angle matching and the de-coherence step of the conventional method. Meanwhile, it can realize the joint estimation of the pitch angle and azimuth angle of extended sources with high detection probability. The effectiveness of the method is verified by numerical simulation and experiments, and the performance of the method is evaluated and discussed.
## 3 Wireless Sensor Networks
### System Design
#### 3.1.1 Transceiver  
The RF transceiver is the component that requires more energy in the SPWS. It was chosen on the basis of some characteristics, the most important being the reception and transmission current consumptions, the transmission range, the requirements for integration with a microcontroller, and the price. The devices initially considered, presented in Table 1, are provided by different manufacturers and use different approaches, each denoted by a different background colour: radio Integrated Circuits (IC) (pink); System-on-Chip (SOC) with radio and microcontroller (white); integrated modules with a SOC, crystal, and antenna (green), and; integrated modules with an RF IC, crystal and antenna (no microcontroller) (yellow).  
Figure 3 shows the current consumption (values obtained from the datasheets), when the radio frequency transceivers are employed with the maximum power transmission.  
Figure 3: Energy consumption.  
\begin{table}
\begin{tabular}{c c||c|c|c c c|c c} \hline
**Atmel** & **Dresden Elektronik** & **Freescale** & **Jennie** & **Nordic** & **Microchip** & **Murata** & **ST Microelectronics** & **Texas Instruments** \\ \hline
**A1886223** & **MCG1890** & & **NR24101+** & **MR2410 MA** & & STM32W1084 IB & **CC2533** \\ \hline
**A1886223** & & MC1322 & & **NR2411** & **MR24940 MB** & & STM32W108 CS & **CC2530** \\ \hline
**A12546762** & & MC13232 & & & **MR24100 MC** & & **CC2531** \\ \hline \end{tabular}
\end{table}
Table 1: List of RF (Radio-Frequency) devices considered for the prototype. Radio Integrated Circuits (IC) (pink); System-on-Chip (SOC) with radio and microcontroller (white); integrated modules with a SOC, crystal, and antenna (green), and; integrated modules with an RF IC, crystal and antenna (no microcontroller) (yellow).  
Due to their low consumptions, the Nordic (Nordic Semiconductors, Oslo, Norway) devices were considered in a first instance, but they were soon discarded as they were not 802.15.4 compliant. Considering the low-power consumption objective for the SPWS, other devices with current consumption above 28 mA were also excluded. The Microchip MRF24J40 MA device was chosen among the remaining candidates, as it achieved a good compromise between price, current consumption, integration requirements, and transmission range. The current consumption in receiving mode was not deemed a decisive factor for this particular application because the receiver type of node is always connected to a computational system that powers it via USB port.  
The IMBPC HVAC and thermal control product considers the use of repeater nodes to minimize the probability of losing data packets. As such, acknowledgment frames to confirm the reception of packets with sensor data, are not required. Consequently, this enables using the 802.15.4 standard non-beacon mode, which contributes to the preservation of battery charge.  
To a great extent, the selection of the MRF24J40 MA was made by considering the easiness of integration and development with the Microchip XLP microcontroller family. This also helped with narrowing the microcontroller alternatives.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 3 Results
### Time Series of the GPP and NPP  
Similar annual distribution behaviors were apparent when comparing the GPP to the EVI and LST (Figure 6a). The GPP in the control area peaked in July and then dramatically decreased. The GPP was close to zero from October to March of the following year in the study area. In the burned areas, the GPP experienced a downward trend. A maximum difference of 50% occurred in the fire month or subsequent month (i.e., relative month 0 or 1). The GPP was also very low in the reference area during the winter because of seasonal effects; therefore, the GPP was mainly influenced by fires that occurred during the growing season.  
Figure 5: Temporal trajectories of the post-fire forest recovery as defined by the growing season average (May to October) of the EVI, LST and LSWI between the control area and focal area at different burn severity levels. EVI’s annual dynamics in the control and focal pixels (**a**); EVI’s dynamics under different fire intensities (**d**); EVI’s difference between the control and focal pixels (**g**); LST’s annual dynamics in the control and focal pixels (**b**); LST under different fire intensities (**e**); LST’s differences between the control and focal pixels (**h**); LSWI’s annual dynamics in the control and focal pixels (**c**); LSWI under different fire intensities annually (**f**); and LSWI’s differences between the control and focal pixels (**i**).  
The fire disturbances mainly influenced the GPP during the growing season (Figure 6a-c). Therefore, only the statistical GPP and NPP values from May to October were used in this study; the vegetation NPP and GPP decreased as the fire intensity increased. The GPP recovery rate was faster in earlier (1-3) years but became relatively slow in the fourth year of the recovery, and the GPP and NPP did not recover to their original levels during the 17-year study period. However, the GPP and NPP recovery times in the moderately and lightly burned areas were similar. The temporal profiles of the NPP linearly increased with a gradually decreasing slope but did not reach a steady state during the study period. Previous research showed that a steady state is not reached until at least 20 years after a fire, with a longer full-recovery period for the Alberta Boreal Plains eco-zone.