esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Soc Estimation for the LiBP Using the SPKF  
To estimate the average SoC of \(N_{s}\) cell modules in LiBP, the quantities, including \(N_{s}\) voltages on the cell modules, the operating temperature of LBP and the current through LiBP pack are measured continuously at sampled-times. Using the sigma point estimation algorithm [19; 20; 21], two steps are performed at each sample time \(k\), as follows:  
**Step 1.** Estimate the average SoC of LiBP and current bias of current through the LiBP; this step is done by algorithm SPKF 1.  
**Step 2.** Estimate SoC difference of each cell module from the average SoC determined in Step 1 by algorithm SPKF 2. Finally, estimate the SoC for all cell modules by the sum of the average SoC of LiBP and the SoC difference corresponding to the cell modules.  
To conduct Step 1, suppose the LiBP as the equivalent cell described by the second order dynamic model. The state variables need to be estimated, including the currents through two dynamic branches \(i_{R_{1}},i_{R_{2}}\), the hysteresis \(h\), SoC of the equivalent cell \(z\) and the bias current of current through the equivalent cell \(b\).  
The input of the estimation model is the current through the LiBP with bias current and measurement noise; the output is the voltage on LiBP. Define the estimated state variable vector as  
\[\underline{x}=\left[\begin{array}{cccc}i_{R_{1,k}}&i_{R_{2}}&h_{k}&z_{k}&b_{ k}\end{array}\right]^{T} \tag{10}\]  
The covariance matrices of state estimation errors, system noises (noise of current, noise of current bias) and voltage noise are \(\sigma_{\overline{x}}\), \(\sigma_{w}\) and \(\sigma_{\zeta}\), respectively.  
_The General Algorithms_  
**SoC Estimation Algorithm for LiBP.**  
Initialize the parameters of cell  
Initialize the parameters of LiBP including:  
\(\mathbf{SOC}_{0}\in R^{(N_{s},N_{s})}\), \(\mathbf{R}_{0}\in R^{(N_{s},N_{s})}\)  
Initialize the covariance matrices  
Calculate the initial SoC of the cell modules in the LiBP \(\overline{z}_{0}^{(i)}\)  
Calculate the internal resistance of the cell modules \(\overline{R}_{0}^{i}\)  
Calculate the capacity of the cell module \(Q^{(i)}\)  
**For sample time k = 1 to so do**  
Measure the current through the LiBP  
Measure the voltages on the cell modules  
Measure the temperature  
Estimate the state \(\underline{x}_{k}^{+}\) for equivalent cell and estimate the bias current by using the algorithm SPKF 1  
**For cell module i = 1 to \(N_{s}\)** do  
Estimate the SoC difference \(\Delta\underline{z}^{(i)}_{k}\) of cell module \(i^{th}\) by using the algorithm SPKF 2  
**End**  
Calculate the SoC for all cell modules \(\underline{z}^{(i)}=\underline{z}_{k}+\Delta\underline{z}^{(i)}_{k},i=1,2...,N_ {s}\)  
**End**  
The algorithm SPKF 1 and SPKF 2 are presented in the Appendix A.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 2 Spherical Harmonic HRTF Interpolation
A brief overview of the concept can be found in Figure 1.  
The main issue caused by SH HRTF interpolation is that the interpolated HRTFs are only accurate up to the spatial aliasing frequency \(f_{lim}\), approximated by  
\[f_{lim}\approx\frac{\mathrm{c}M}{4\pi(M+1)\mathrm{sin}(\pi/(2M+2))}\approx \frac{\mathrm{c}M}{2\pi r} \tag{5}\]  
where \(\mathrm{c}\) is the speed of sound, approximated as 343 m/s at 20 \({}^{\circ}\mathrm{C}\) in air, \(M\) is the spherical harmonics order and \(r\) is the radius of the human head [27]. For 1 st order, the spatial aliasing frequency is around 700 Hz. The spectral distortions will not only affect the timbre, but will also degrade the localisation performance since the important cues for identification of source elevation are changed, as shown in Figure 2.  
Figure 1: An overview of the proposed method wherein a model was trained to reconstruct the distorted high frequency information in the SH-interpolated HRTFs. To combat this, we also employed dual-band Time Alignment (TA) in the encoding of the HRTFs [28; 29]. Given ITD is only effective at low frequencies, we can time-align the high frequencies of the HRTFs when undertaking SH encoding, thereby removing high frequency ITD. By doing this, lower order SH HRTFs are more effective at preserving high frequency information, which improves interpolation results. In this study, the crossover frequency was set at 2.5 kHz, as suggested in [29].
## Materials and Methods
### Theoretical Method
\right)\cdot\sqrt{\frac{s_{T}}{\prod_{i=1}^{N-1}(s_{i})}}\cdot \gamma\cdot e^{-\alpha s_{T}} \tag{2}\]where:  
\(\phi_{0}\) represents the SPL from the source;  
\(s_{T}=\sum\limits_{i=1}^{N}s_{i}\), with \(s_{i}\) representing the slant distances for the links of paths chosen, between each node's geometrical centers;  
\(k\) represents the wavenumber;  
\(N\) represents the quantity of nodes for every path;  
\(D_{n}\) represents the diffraction coefficient and \(R_{n}\) the reflection coefficient; application is dependent on the form of incidence either obstacle or ground;  
\(\gamma\) represents the obstacle coefficient factor. The expression is derived from [44; 45], but includes, in this instance, the \(\gamma\) coefficient factor for two reasons: to adjust the phase, and to be a weight factor ([46; 47]) for every form of obstacle under consideration in this research. \(\alpha\) is the air-absorbent coefficient in Np/m, according to [48]. In turn, this parameter depends on the following input variables, which are related to the source's frequency emission (\(f\)) and the physical properties of the air: static pressure (\(Ps\)), Celsius temperature (\(T\)), and percentage relative humidity (\(H\)). PARDOS (acronym from Spanish 'Perdidas Acusticas por Reflexion y Difraccion de la Onda Sonora', which means 'Acoustic Losses due to Reflection and Diffraction of Sound Waves') is a user-friendly software tool developed ad-hoc with MATLAB in order to be used for the simulations conducted in this work. The authors demonstrated, made comparisons, and validated such a formulation in [43]. Figure 7 summarizes the flow chart of the software tool's core to obtain the sound pressure spectrum:  
Figure 7: ‘Calculate’ subroutine flow diagram. Figure 8 shows a general view of the PARDOS graphical user interface (GUI):  
Two assumptions were made in the present algorithm and tool developed by the authors: first, the far field region was met (which is true for distances from the source clearly greater than a wavelength of sound); and second, no contribution through the barriers (acoustic isolation) was accounted for (which does not imply any constraint since it is intended to evaluate the performance of the diffracting structures). Therefore, these two approaches did not turn out to have any restrictions on the results obtained.
## 4 Technologies to Support Contextualization
### Comparing the Present Situation with Historical Data
#### 4.3.2 Storing and Retrieving Human Experience  
Another valuable source of historical information operator experience, its storage and retrieval can be supported by _case-based reasoning_ (CBR) [160]. Based on a situation description by the operator, the system searches for similar cases in a database. An advantage of CBR is that it is explainable [161]. Thus, the system can make its selection of cases transparent, justify its sampling strategies, explain the relevance of questions, help users understand the meaning of concepts, and support learning [162].  
Traditional CBR systems rely on a match between specific features. For instance, if operators describe the current situation by stating that a high temperature in the production hall and broken chocolate at the carrier belt are present, the system retrieves cases that share the exact same features. To benefit from knowledge of system relations, CBR can be combined with ontologies [163; 164]. This makes it possible to go beyond specific features and retrieve cases that share an underlying causal principle (i.e., high temperature causes conveyor soiling, conveyor soiling causes displacements of chocolate bars, and displacements cause breaking under high mechanical impact). In this way, cases can be retrieved that differ in terms of their specific features but might still afford the same solution (e.g., increase machine cooling or reduce machine speed). Moreover, ontological modelling enables systems to make such matches between structural relations explicit. In this way, operators can be supported in transferring knowledge to situations that differ in terms of their surface features. However, a major technical challenge is that this requires asystem that can interpret ontologies and make appropriate queries to extract the relevant information.  
CBR can be combined with dynamic process data, and some CBR systems are capable of autonomous information gathering [165; 166]. They retrieve information from databases so that users do not have to describe all features of the situation by themselves. However, enabling systems to retrieve relevant process data requires a combination of CBR with the technologies described above. Based on formal models of the technical system, a CBR system would know where to look for relevant information and, based on OPC UA, it could extract this information from the respective machines. Such automatic extraction of relevant process data would allow for much richer case descriptions, enabling future users to assess whether the context of a previous situation matches the current one.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 4 Laboratory Tests Results
### Metrological Verification
#### 4.2.1 Calibration Procedure  
The first part of the analysis is dedicated to removing biases from the signals acquired from the accelerometers. The calibration procedure is fundamental because each sensor comes from the factory with slightly different characteristic due to production tolerances. For this static analysis, the sensors are positioned with one axis perpendicular to the ground, and acceleration data from each axis are recorded. Signals from the axes parallel to the ground, which are expected to measure zero acceleration, are used to de-bias the accelerometers. Meanwhile, the vertical axis, expected to measure 1 g of acceleration due to gravity, is utilized to determine the gain, allowing for the conversion of raw sensor data into units of g. The calibration involves rotating the sensor to align each axis sequentially with the vertical direction. All obtained values are summarized in Table 2. This calibration procedure significantly enhances the reliability and accuracy of the sensor system, providing a robust foundation for subsequent data collection and analysis.  
Given the previous derivation, if \(\overline{a}_{ij}\) encompasses the acceleration data recorded in LSB from i-th sensor and j-th axis, in order to obtain the value for the acceleration in units of g, the following equation has to be used:  
\[a_{ij}=\frac{\overline{a}_{ij}-Offset_{ij}}{Gain_{ij}} \tag{2}\]
## 3 Results
### Interpolation Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Year** & **Linear Regression Equation** & **MLR** & **MLR** & **MG** & **RG** & **RG** & **IMERG** & **IMERG** & **IMERG** \\  & **MB** & **MP** & **PB** & **MB** & **MP** & **PB** & **MB** & **MP** & **PB** & **MB** & **MP** \\ \hline
2010 & y \(=\) \(-\)134.60 \(\pm\) (0.02 x \(\times\)) + (18.19 x \(\times\)) + (12.90 x \(\times\)) & 1704 & 1668 & 1404 & 883 & 1073 & 1299 & 1595 & 1828 & 1780 \\
2011 & y \(=\) 750.24 \(\pm\) (0.14 x \(\times\)) + (0.64 x \(\times\)) + (-4.24 x \(\times\)) & 1051 & 1033 & 955 & 462 & 554 & 1080 & 792 & 819 & 1101 \\
2012 & y \(=\) 972.98 \(\pm\) (0.15 x \(\times\)) + (1.18 x \(\times\)) + (-3.82 x \(\times\)) & 1324 & 1305 & 1220 & 798 & 931 & 1470 & 1504 & 1500 & 1621 \\
2013 & y \(=\) 1091.93 \(\pm\) (0.24 x \(\times\)) + (5.99 x \(\times\)) + (-5.56 x \(\times\)) & 1811 & 1781 & 1622 & 1015 & 1162 & 1999 & 1806 & 1961 & 1908 \\
2014 & y \(=\) 682.64 \(\pm\) (0.19 x \(\times\)) + (11.88 x \(\times\)) + (0.71 x \(\times\)) & 948 & 969 & 992 & 920 & 922 & 1321 & 1327 & 1324 & 1368 \\
2015 & y \(=\) 1294.94 \(\pm\) (0.14 x \(\times\)) + (-1.48 x \(\times\)) + (-8.64 x \(\times\)) & 1472 & 1451 & 1374 & 1011 & 1095 & 1841 & 1617 & 2073 & 1873 \\
2016 & y \(=\) 73.50 \(\pm\) (0.24 x \(\times\)) + (0.102 x \(\times\)) + (1.94 x \(\times\)) & 1491 & 1466 & 1305 & 1059 & 1158 & 1176 & 1445 & 1738 & 1435 \\
2017 & y \(=\) 729.75 \(\pm\) (0.13 x \(\times\)) + (6.51 x \(\times\)) + (-0.49 x \(\times\)) & 1344 & 1329 & 1231 & 825 & 857 & 1192 & 1693 & 1648 & 1824 \\
2018 & y \(=\) 1302.64 \(\pm\) (0.01 x \(\times\)) + (-0.37 x \(\times\)) + (-2.25 x \(\times\)) & 1290 & 1279 & 1245 & 1332 & 952 & 1569 & 1860 & 2012 & 1891 \\
2019 & y \(=\) 80.15 \(\pm\) (0.29 x \(\times\)) + (-0.39 x \(\times\)) + (-2.62 x \(\times\)) & 1530 & 1498 & 1341 & 816 & 1192 & 1479 & 1177 & 1163 & 1269 \\
2020 & y \(=\) 407.61 \(\pm\) (0.29 x \(\times\)) + (0.17 x \(\times\)) + (0.51 x \(\times\)) & 1480 & 1449 & 1266 & 776 & 982 & 1561 & 1695 & 1585 & 1734 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cumulative annual precipitation for Monte Bove Sud (MB), Monte Prata (MP) and Pintura di Bolognola (PB), calculated through interpolation (MLR), IMERG data to which was added the real snowfall data (IMERG) and the data measured by the rain gauge (RG); x\({}_{1}\) = altitude, x\({}_{2}\) = distance from the sea, x\({}_{3}\) = distance from the sea
###### Abstract  
Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using _spm_, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.  
Keywords:spatial predictive models; predictive accuracy; model assessment; variable selection; feature selection; model validation; spatial predictions; reproducible research +
Footnote †: journal: Applied Sciences
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 3 Validation  
### Measurement Campaigns  
RF coverage measurements for both 2G and 4G cellular networks were collected in the cities of San Francisco, San Jose, and Atlanta. In San Francisco, several 2G cell sites, at the frequencies of 850 and 1900 MHz were chosen from all over the central part of the city. The cell site characteristics were derived from surveying each target site. BS antenna heights ranged from 6 to 100 m. Effective Radiated Power (ERP), which was found by using a scanner at Line-of-Sight (LOS) locations with each site, was generally between 30 and 45 dBm. Measurements were collected through drive tests, using a [PERSON] scanner placed inside a minivan and a PCTEL OP178H omnidirectional antenna with 3 dBi gain placed on top of the minivan. The antenna height above the ground was approximately 1.8 m and exact Rx locations along routes were tracked using a combination of GPS, inertial devices, and speedometer. Using the scanner, the received signal strength indicator (RSSI) was recorded for the target broadcast control channels (BCCHs) as the  
Figure 3: Simplified scheme and computation flow of the DED-RL algorithm.  
minivan drove the streets around each cell site. From the recorded RSSI measurements, roughly 27,000 small-area average power measurements were extracted.  
Measurements for San Jose and Atlanta (north and south) were collected in a very similar way as for San Francisco, after selecting several 4G Base Stations, and using a Rhode Schwarz TSME 4G scanner. Carrier frequencies were the same as in San Francisco, i.e., 850 and 1900 MHz. Due to the very vast measurement area, which encompasses 437 base station sites, ERP survey for each base station was not possible for San Jose and Atlanta, and since cellular operator ERP info were found unreliable--often lower than the actual figures--mean error statistics cannot be considered valid for San Jose and Atlanta (see next section).
## 3 Roadway Surrounding Rock Deformation Patterns
### The Impact of Surrounding Rock Classification on Rock Deformation  
Taking the example of the total displacement D of the surrounding rock at conditions of P = 2.5 MPa, H = 6 m, and W = 6 m across different rock grades as illustrated in Figure 9, the study investigates the impact of SRC on rock deformation. From Figure 9, it is evident that as the SRC decreases, the deformation trend of the rock mass significantly increases. Specifically, during roadway excavation, Class V surrounding rock displays the steepest slope of displacement deformation curve, indicating that under the same conditions, the displacement growth is most pronounced in lower grade (softer) surrounding rock. The reason for this phenomenon is primarily due to the lower cohesion and shear strength of lower-grade surrounding rock, which poorly adapts to the redistribution of stress caused by excavation. Therefore, during the excavation and support design of roadways, the classification of surrounding rock must be considered. For lower-grade surrounding rocks, more reinforced support measures are necessary, such as using thicker shotcrete, a denser system of rock bolts, and steel arches, to enhance the stability of the roadway and prevent excessive deformation. Additionally, the timing of implementing roadway support should  
Figure 8: Impact of roadway cross-sectional dimensions on surrounding rock deformation. (a) B = 400 m; (b) B = 600 m.  
be advanced as much as possible to minimize the impact of stress release caused by excavation on the surrounding rock [26; 27].
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Network Architecture
#### 3.2.3 Decoding Module  
The decoding network structure is shown in Figure 6. The input feature map first passes through two standard deconvolution layers with a step size of 2. Each standard deconvolution layer is composed of a two-dimensional deconvolution series batch normalization with a convolution kernel of 4. There is a unified layer and ReLU activation function, then the resolution of the feature map is restored to the same size as the input image resolution. Then the feature map goes through a standard convolution operation layer and a two-dimensional convolution to reduce the number of channels to 1, and finally passes through the sigmoid function to output a heat map with a value between 0 and 1. By decoding the heat map, the position coordinates of the small infrared target can be obtained.  
Figure 4: The framework of the feature alignment module.  
Most infrared small target detection methods use datasets with segmentation binary masks as real labels. However, when the size of the target is too small, it is difficult to distinguish clear edges. At this time, it is very difficult to use segmentation binary masks for labeling. Only the center point can be used to mark the target. However, if only a binary mask is used to mark the center, and only the center point is 1 and the rest is 0, this will cause a serious problem of imbalance between positive and negative samples. In order to solve this problem, we use the two-dimensional Gaussian distribution to create the real labels of small infrared targets. Specifically, the real label can be constructed using the following formula:  
\[g_{t}(m,n)=\exp\biggl{(}-\frac{1}{2\delta^{2}}\biggl{[}\biggl{(}m-t_{x}^{i} \biggr{)}^{2}+\biggl{(}n-t_{y}^{i}\biggr{)}^{2}\biggr{]}\biggr{)} \tag{9}\]  
where \(\delta\) is the standard deviation, \(i\) is the index label of the independent target, and \(t_{x}^{i}\) and \(t_{y}^{i}\) represent the \(x\)-coordinate and \(y\)-coordinate of the \(i\)-th target point. Using the  
Figure 5: The framework of time feature fusion module.  
Figure 6: The framework of the decoder module.  
two-dimensional Gaussian distribution to construct the ground truth label expands the non-zero part of the heatmap.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 4 Hierarchical System Architecture  
The complexity of the computing environment calls for systems that can autonomously adapt their behavior in face of changing working conditions. The MAPE control cycle is a well-know architectural pattern that organizes the autonomous control of a software system according to four main components (Monitor, Analyze, Plan, and Execute), which are responsible for the primary functions of self-adaptation [16].  
When the controlled system is distributed across a large number of computing nodes, a single and centralized MAPE loop may represent a system bottleneck that may compromise the system responsiveness. To overcome the limitations of a centralized control solution, we can efficiently decentralize the MAPE components according to popular design patterns, as described in [49]. Among the different decentralization solutions, the hierarchical control pattern represents a suitable approach to manage wide-area distributed systems. The hierarchical control pattern revolves around the idea of a layered architecture, where each layer works at a different level of abstraction. In this pattern, multiple MAPE control loops work with time scales and concerns separation. Lower levels operate on a shorter time scale and deal with local adaptation. Exploiting a broader view on the system, higher levels steer the overall adaptation by providing guidelines to the lower levels.  
To efficiently control the execution of elastic DSP applications in a geo-distributed computing environment, we propose _Multi-Level Elastic and Distributed DSP Framework_ (E2 DF). It includes two management systems that are organized according to a two-layered hierarchical pattern: the Application Control System (ACS), which adapts the DSP operators deployment, and the Infrastructure Control System (ICS), which controls the computing resource elasticity. Figure 2 illustrates the conceptual architecture of E2 DF, highlighting the hierarchy of the multiple MAPE loops and the system components in charge of the MAPE loop phases.  
Figure 2: The E2 DF conceptual architecture: hierarchical MAPE loops.  
The ICS includes a centralized _Infrastructure Manager_ (IM), which cooperates with multiple decentralized Region Managers (RMs). Similarly, the ACS comprises one centralized _Application Manager_ (AM) and multiple decentralized _Operator Managers_ (OMs). These components oversee the application deployment at run-time, which can be conveniently adapted when improvements of the application performance can be achieved. The IM and AM can work independently one another or can cooperate so as to realize cross-level optimizations. In such a way, the infrastructure can be adapted to better address specific application needs (e.g., to provision resources in advance or to prevent node consolidation). In this paper, we do not consider cross-level optimization, but we mainly focus on adaptation policies for the ICS.