esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 4 Application of Gliders to Underwater Acoustics
### Underwater Passive Surveillance
#### 4.2.1 Performance of Signal Receiving
Assuming the distribution of the amplitude of the noise is Gaussian and the false alarm rate is set to \(P_{Fa}=10^{-2}\), the detection probability \(P_{D}\) can be calculated based on the following equation [24]:  
\[P_{D}=Q_{X_{N}^{2}}\left(\frac{Q_{X_{N}^{2}}^{-1}(P_{Fa})}{SNR+1}\right) \tag{2}\]  
where the \(Q_{X_{N}^{2}}(\cdot)\) is the chi-square distribution with \(N\) degrees of freedom (\(N=1\) in the simulation), and \(SNR\) is the received signal-to-noise ratio, which can be calculated by the sonar equation. The right-hand panel of Figure 8 shows the simulated detection probability of the glider and the AUV, respectively. As shown in the figure, the detection probability trend of both vehicles decreases with the increase in distance, but in most cases the glider can maintain a detection probability higher than 60% even beyond 5 km. However, the AUV performs poorly when the range is further than 2 km, where most of the detection probability is no more than 20%. This is because the self-noise of AUVs is extremely high, which makes the received SNR much lower compared to the glider under the same conditions. The results of the simulation indicate that the glider performs far better than AUVs with propeller systems in underwater target detection.
## 6 Parallelization Strategy  
Parallelization is crucial to accelerating the solution process. To describe the communication process between partitions, Figure 6a,b will be used, with different shadings relating to the different partitions. To synchronize the partition, a set of ghost elements is defined at each of the partition's interfaces. The ghost element adds a layer of nodes to one partition from elements associated with its neighboring partition. This is portrayed in Figure 6c.  
It should be noted that the ghost elements are updated in the inter-partition synchronization and are used in order to allow the discretization process to be performed for the nodes that lie exactly at the interface, since they require additional information to reconstruct their missing control volumes. The process starts during the partitioning when the ghost nodes are added to all partition interfaces. During discretization, care is taken to ensure that the matrix coefficients are constructed only for the core elements, i.e., the elements that are not ghost elements. Figure 7 shows the communication process taking place among the different nodes on the partitions. A communication process is, in fact, a synchronization process, through which a value of the node which belongs to its owned partition is copied to its shadow node on the neighboring partition. A shadow node can either be a shared node or a ghosted node; a shared node is a node which is shared by multiple partitions while it does not belong to the new layer of nodes added. Ghosted nodes are simply the nodes that are added to the partition. There will eventually be some useless nodes among the ghosted ones, which are those not connected to any of the stencils; these are also indicated in the figure.  
Based on the preceding information, it should be mentioned that synchronization processes are applied at different instants in the solver, some of which are related to fields and others that are related to the algebraic multigrid solver. Flow fields are always synchronized at the beginning of each nonlinear loop so that the assembly process at the nodes associated with the ghost nodes is consistent. For the linear multigrid solver, the parallelization process includes synchronization processes for matrix-vector operations,  
Figure 6: Mesh partitioning process for a sample mesh. (**a**) Sample mesh, (**b**) the corresponding partitions, and (**c**) partitions with ghost elements.  
but there are also multiple requirements that must be satisfied for a fully functioning linear multigrid solver. In this work, the strategy follows the methodology revealed in [36].
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Año** & **Timbes** & **Piura** & **Lambayeque** & **Cajamarca** & **La Libertad** & **Amazonas** & **San Martin** & **Prom** \\ \hline
**2010** & 500.9 & 229.7 & 331.1 & 707.5 & 232.6 & 2018.6 & 1208.9 & **747.0** \\ \hline
**2 \begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Groundwater Scarcity/Water Stress** & **Groundwater Recharge/GWS** \\ \hline
2003 & Piura and Lambayeque with reduced availability. & San Martin and Amazonas with high availability. \\
2004 & Increased scarcity in La Libertad and Cajamarca. & Amazonas continues with good levels. \\
2005 & Crisis in Piura and Lambayeque. & San Martin with good recharge. \\
2006 & Scarcity in La Libertad and Piura. & Amazonas with adequate levels. \\
2007 & Piura and Lambayeque in crisis. & San Martin remains stable. \\
2008 & Severe groundwater reduction in Piura & Amazonas maintains recharge. \\
2009 & Severe crisis in La Libertad and Piura. & San Martin and Amazonas remain stable. \\
2010 & Piura and La Libertad at critical levels. & Amazonas with stable recharge. \\
2011 & Persistent crisis in Lambayeque and Piura. & Slight recovery in San Martin. \\
2012 & Piura and La Libertad still in crisis. & Amazonas remains unchanged. \\
2013 & Critical conditions in Piura and Lambayeque. & San Martin with normal recharge. \\
2014 & Persistent crisis in Piura and Lambayeque. & Amazonas and San Martin remain stable. \\
2015 & Scarcity in Piura, Lambayeque, and La Libertad. & Amazonas and San Martin with good availability. \\
2016 & Increased scarcity in La Libertad and Cajamarca. & Amazonas maintains good levels. \\
2017 & Water crisis in Piura and Lambayeque. & San Martin shows slight recovery. \\
2018 & Low availability in Piura and La Libertad. & San Martin and Amazonas maintain stable levels. \\
2019 & Deficit in Piura, Lambayeque, and Cajamarca. & Amazonas continues with good recharge. \\
2020 & Severe scarcity in Piura, La Libertad, and Lambayeque. & San Martin maintains acceptable levels. \\
2021 & Critical levels in Piura and Lambayeque. & Amazonas and San Martin remain stable. \\
2022 & Severe scarcity in Piura and La Libertad. & Amazonas continues with stable groundwater recharge. \\
2023 & Critical conditions in Piura, La Libertad, and Lambayeque. & San Martin and Amazonas unchanged. \\ \hline \end{tabular}
\end{table} 
Table 4: Annual groundwater variation at Northern Zone (2003–2023).
## 2 Literature Review  
The fPCA technique has been used frequently for analyzing functional data such as magnetic resonance imaging (MRI) data, weather data, and stock exchange trends [9; 10; 11]. Significant research has been undertaken using fPCA, and the method helps to analyze continuous functions and enables the extraction of key features that epitomize dominant aspects of the original dataset [12]. In the field of bioinformatics, fPCA plays a crucial role in the analysis of time-course expression data by reducing the dimensionality of measurements without losing the generality [13]. Moreover, the data analysis enables the prediction of individual smooth trajectories, whereas limited measurements are available with the aid of asymptotic consistency and distribution [14]. [PERSON] and [PERSON] [15] demonstrate several functional data analyses with the help of fPCA, in which different types of functional data are discussed. Roughness penalties are used to estimate the mean and covariance of functions in the analysis of the annual cycle of monthly climate data [16]. Detailed and comprehensive descriptions of functional data analysis, including prediction modeling, were discussed in a published book [15].  
The method of analyzing functional data using fPCA has subsequently evolved significantly due to the varieties and complexities of functional data. In the prediction of multidimensional unbalanced functional data of protein structures, fPCA is used with regularized Gaussian basis functions [17]. The statistical tool was also applied to study the seat-to-stand movement of two separate groups, namely, osteoarthritis patients and healthy subjects. Human movement was studied by retaining all of the information for further analysis and the analysis was an extension of multivariate principal component analysis [18]. A detailed data-driven fPCA model utilizing a local quadratic smoothing function for Li-ion prognostics was used to measure mean functions [19]. The study presented a detailed model of longitudinal battery data through eigenfunctions and estimated variance and covariance functions with a combination of B-splines. This extensive research verified that a more comprehensive conclusion could be provided by fPCA than traditional discrete data analysis methods when datasets are continuous functions [12].  
A distinct research scope can be determined for a data-driven prognostic model of Li-ion batteries based on literature reviews and past studies. Table 1 demonstrates the classification of the research scope.  
As can be seen from Table 1, much of the work regarding Li-ion batteries has used support vector machine (SVM), Bayesian regression, and machine learning (ML) algorithms to develop an empirical model. The current study took a novel approach, adopting fPCA to extract the feature from the dataset existing in the form of a function, and used LASSO regression, a penalized regression model, to fit better models by shrinking the model coefficients. The combination of fPCA and LASSO regression for analyzing functional monitoring data has not been used in previous studies and remains unexplored to date.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
###### Abstract  
The enhanced Loran (eLoran) system, a critical terrestrial backup for the Global Satellite Navigation System (GNSS), traditionally utilizes a Reed-Solomon (RS) code for its data communication, which presents limitations in error performance, particularly due to its decoding method. This paper introduces a significant advancement by proposing the replacement of the conventional RS code with a 128-_ary_ polar code, which is designed to maintain compatibility with the established 128-_ary_ Pulse Position Modulation (PPM) scheme integral to eLoran's positioning function. A Soft-Soft (SS) demodulation method, based on a correlation receiver, is developed to provide the requisite soft information for the effective Successive Cancellation List (SCL) decoding of the 128-_ary_ polar code. Comprehensive simulations demonstrate that the proposed 128-_ary_ polar code with SS demodulation achieves a substantial error performance improvement, yielding an approximate 9.3 dB gain at the 0.01 FER level over the RS code in eLoran data communication with EPD-MD demodulation. Additionally, the proposed scheme improves data transmission efficiency--either reducing transmission duration by 2/3 or increasing message bit number by 250% for comparable error performance--without impacting the system's primary positioning capabilities.  
enhanced Loran (eLoran) system; polar codes; Pulse Position Modulation (PPM); non-binary polar codes 2025254638
## 4 Descriptive Analysis  
Table 1 displays the top six publication sources. Among the 73 publications, most were published in the journal _Sensors_ (7 publications), followed by _IEEE Internet of Things Journal_ (5 publications).  
Figure 1 shows the distribution of publications by year. Scholars started to discuss the role of IoT in the sustainable development of smart cities in 2008. The number of publications fluctuated upward over the next 12 years, peaking in 2020 with 14 publications. The dataset of articles for 2023 is still incomplete, as it only includes items downloaded until 22 December 2023.  
Among the 73 publications, 35 publications discussed smart communities. Twenty-three publications focused on smart transportation. Seven publications focused on disaster management. Two publications highlighted the privacy and security of the IoT system applied in the sustainable development of smart cities. The remaining six publications are related to some topics, such as smart agriculture (two publications), smart healthcare (two publications), smart education (one publication), and smart tourism (one publication). Figure 2 presents the distribution of publications by topics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Number of Publications** \\ \hline Sensors & 7 \\ IEEE Internet of Things Journal & 5 \\ IEEE Access & 4 \\ Applied Sciences-Basel & 2 \\ International Journal of Distributed Sensor & 2 \\ Networks & \\ International Journal of Environmental & 2 \\ Research and Public Health & \\ Expert Systems with Applications & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top six distribution of publication by sources.
## 3 Results
### Sensory Evaluation  
#### 3.4.1 TDS  
TDS is an effective tool to characterize the differences between samples during food or beverage consumption, which has been used to distinguish coffee added with different sweeteners [42], brandy with different aging times [17] and chocolate bars with different cocoa contents [43]. In this study, TDS curves showed the evolution of the retronasal aroma dominance rate over time after spitting (Figure 2A). The amount of dominant retronasal aroma attributes (the attribute which has the highest dominance rate) varied between samples. A dominant retronasal aroma attribute was defined as the most noticeable retronasal aroma attribute at a given time. It should be understood mostly as the new aroma popping up. Therefore, the dominant retronasal aroma attribute is not necessarily the one with the highest intensity. Regarding the TDS curves of F00, grain, alcoholic, fruity and roasted aromas were considered the dominant retronasal aroma attributes. In F10, alcohol was the first dominant aroma, followed by acidic, fruity, grassy, and fruity aromas. F20 had seven dominant retronasal aroma attributes, which was the most. They were alcoholic, grain, fruity (appeared twice), floral, roasted, grassy and earthy. The dominant retronasal aroma attributes in F30 were alcoholic, acidic, fruity, roasted, grassy and earthy.  
TDS curves revealed some differences in the duration of the dominant retronasal aroma attributes between four kinds of differently graded commercial liquors. Figure 2A showed that the low-grade FJ (F00 and F10) had a short duration of retronasal aroma. Basically, no attributes were beyond the \"significance level\" after 50 s. After 70 s, panelists could hardly feel any retronasal aroma. However, high-grade FJ (F20 and F30) had a long and abundant perception of retronasal aroma. This may be related to the aging time of their base liquor. F00 had the longest dominant time of the grain and roasted aroma, while F30 had the longest one of acidic, fruity, grassy and earthy. TDS parameters, including DR-max (Maximum Dominance rate), T-max (Time for DR-max) and T-90%max (Time interval in which dominance rate is \(\geq\)90% of DR-max), effectively distinguished four kinds of FJ. The PCA score plot (Figure 2B) highlighted the similarity between F20 and F30. F00 had a higher maximum dominant rate of fruity, grain and roasted, while F20 and F30 had a higher maximum dominant rate of grassy, floral and earthy. Thus it could be seen that TDS can well characterize the sensory differences between different grades of LFB during consumption.
## 3 Test Section of Asphalt Concrete for Ballastless Track Substructure
### Performance Evaluation of the Asphalt Concrete  
A variety of laboratory tests according to the \"Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering\" [23] were adopted to evaluate the bearing capacity, waterproofing performance and the durability of the designed asphalt concrete for a ballastless track substructure, including a infiltration test, wheel tracking test, indirect tension test, four-point bending beam test, low temperature bending beam test, and dynamic modulus test. Figure 3 shows some test samples and procedures. Table 3 shows the test results. It can be seen that the performance parameters of the designed asphalt concrete can fully meet the technical requirements. It has a good waterproofing property and an excellent load-bearing performance to satisfy the functional requirements during its service life. Therefore, the designed asphalt concrete is suitable for the real practice.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Functionality**} & \multirow{2}{*}{**Parameter (Unit)**} & \multirow{2}{*}{**Test Result**} & \multicolumn{2}{c}{**Technical Requirement**} & \multirow{2}{*}{**Test Method**} \\  & & & & \\ \hline \multirow{3}{*}{Waterproofing} & Water permeability coefficient & \multirow{3}{*}{0} & \multirow{3}{*}{\(\leq\)60} & T 0730 \\  & (mL/min) & & & \\  & Void ratio (\%) & 1.67 & \(\leq\)4 & T 0711 \\ \hline \multirow{3}{*}{Bearing capacity} & Compressive strength @ 20 \({}^{\circ}\)C & \multirow{3}{*}{5.37} & \multirow{3}{*}{\(\geq\)4} & T 0713 \\  & (MPa) & & & \\  & Dynamic stability @ 60 \({}^{\circ}\)C & \multirow{3}{*}{3060} & \multirow{3}{*}{\(\geq\)2400} & T 0719 \\  & (cycle/mm) & & & \\  & Dynamic modulus @ 10 Hz, 15 \({}^{\circ}\)C (MPa) & & & \\  & (MPa) & & & \\ \hline Low-temperature & Failure stiffness @ \(-\)10 \({}^{\circ}\)C (MPa) & 13.26 & \(\geq\)8 & T 0715 \\ cracking resistance & Failure strain (\(\mu\epsilon\)) & 3356 & \(\geq\)3000 & \\ \hline Water damage & Resilient stability (\%) & 95 & \(\geq\)85 & T 0709 \\ resistance & Tensile strength ratio (\%) & 95 & \(\geq\)80 & T 0729 \\ \hline \multirow{2}{*}{Fatigue resistance} & Fatigue life @ 10 Hz, 500 \(\mu\epsilon\), 15 \({}^{\circ}\)C & \multirow{2}{*}{122.5} & \multirow{2}{*}{\(\geq\)20} & T 0739 \\  & (\(\times\)10\({}^{4}\) cycle) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Laboratory test results and technical requirements of the designed asphalt concrete.  
Figure 3: Some test samples and procedures: (**a**) infiltration test; (**b**) wheel tracking test; (**c**) indirect tensile test; (**d**) fatigue test; (**e**) low temperature bending beam test; and (**f**) dynamic modulus test.
## 3 Results and Discussion
### Parallel Solution
The other simulation utilized the _Parallel_ method, which led to promising results in the previous simulation with a much lower mode shape count. The presented results (Table 3) were obtained by the 2 \(\times\) processor Intel(r) Xeon(r) E5 2640 0 2.5 GHz with twelve virtual cores (six physical cores), and the integrator settings were the same as in the previous simulations. The elapsed time above 78 h of simulation with the _Default_ method was caused by very low CPU usage (maximum of 5.6%). On the contrary, the _Parallel_ method led to the maximum CPU usage of 100%, resulting in a reduction in the elapsed time, by almost ten times, to a value of approximately 8 h. In addition, the _Parallel_ method required fewer iterations to find the converged solution: 9987 versus 11597 iterations. The _Default_ method used perturbation steps, which were periodically updated as per a certain number of iterations. As previously mentioned, the _Parallel_ method used perturbation steps estimated during the first call for the Jacobian unless the MBD solver struggled to converge. In this case, the perturbation steps used by the parallel solution seem to lead to a more accurate Jacobian matrix. However, this cannot be regarded as a general rule, as it is likely to be a characteristic of the current simulation and solver settings. According to several study cases it can be concluded that the _Parallel_ method reduces the elapsed time by an order of magnitude as per the above-specified hardware and the DO loop size around 2000 runs. By analyzing the simulation results, when the piston reached the expansion mid-stroke (an approximately 450\({}^{\circ}\) crank angle), the MBD solver struggled to find the converged solution. Although the simulation with the _Parallel_ method led to a similar number of iterations (Figure 8), the design sensitivity analysis was solved much quicker, as was the simulation (Figure 9). Convergence issues during the lower expansion stroke occur due to the small oil film thickness between the piston and the liner at the anti-thrust side. The solver struggles to balance between the deformation and the pressure distribution due to the boundary or mixed lubrication regime. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Default** & **Parallel** \\ \hline Maximum CPU usage (\%) & 5.6 & 100.0 \\ Elapsed time (MM:SS) & 78:14:57.0 & 7:52:51.0 \\ Simulation time acceleration (-) & 1.00 & 9.93 \\ Iteration count (-) & 11597 & 9987 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational speed of the simulation with the _Default_ and the _Parallel_ method for the full number of mode shapes.
## 2 Multi-Physics Coupling Mathematical Model
### Mathematical Model of Heat Transfer
According to principles of fluid dynamics and heat transfer, the closed cavity is filled with SF\({}_{6}\) gas, which behaves as a compressible fluid. Based on the evaluation of the Grashof number (Gr) and Rayleigh number (Ra) within the order of 10\({}^{9}\), the flow regime is identified as being in the transition region between laminar and turbulent flow, with the dominant flow pattern determining the overall behavior. Among the turbulence models, the Shear Stress Transport (SST) model offers higher accuracy in the near-wall region compared to the k-\(\epsilon\) model, but it requires longer computational time and is more prone to divergence. A comparative analysis reveals that the temperature distribution predicted by the SST model differs by less than 2 \({}^{\circ}\)C from that obtained using the laminar model. Considering the significantly lower memory consumption and computation time of the laminar model, it was ultimately adopted in this study. Radiative heat transfer occurs between solid surfaces and is mediated by electromagnetic waves, which can be mathematically expressed as  
\[q_{r}=\varepsilon_{f}\sigma_{b}\left(T_{1}^{4}-T_{2}^{4}\right) \tag{7}\]  
where \(q_{r}\) is the radiant heat transfer, \(\varepsilon_{f}\) is the surface emissivity, \(\sigma_{b}\) is the Stefan-Boltzmann constant (unit: W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-4}\)), \(T_{1}\) is the high-temperature surface temperature, and \(T_{2}\) is the low-temperature surface temperature. The surface emissivity of the conductor is 0.2, the emissivity of the inner surface of the enclosure is 0.93, the emissivity of the outer surface of the enclosure is 0.9, and the insulator emissivity is taken as 0.932.