esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Atmospheric Flow Structure on Optical Turbulence Characteristics
## 1 Introduction
The structure constant of air refractive index fluctuations, \(C_{n}^{2}\), is associated with turbulent fluctuations in air temperature (or air density). In particular, one study used the WRF model to forecast \(C_{n}^{2}\) in the surface layer [21]. [PERSON] et al. showed that the WRF model reproduces the measured \(C_{n}^{2}\) with good accuracy. The highest deviations between the modeled and measured values were observed under weak winds and in the presence of temperature inversions. The WRF model has also been used to describe optical turbulence in applications to ground-based astronomical telescopes for the Tibetan Plateau [22]. The authors showed that meso-scale models could provide reliable estimations of optical turbulence parameters in rough terrain conditions above an astronomical site on the Tibetan Plateau. The major aim of the present study was to identify possible ways in which large-scale and meso-scale atmospheric structures influence small-scale turbulence and image quality within the observatory region. In order to diagnose and predict small-scale turbulence and solar image quality, the present study was aimed at expanding knowledge about structured turbulent small-scale motions and optical turbulence. In this article, we compare measurements of solar image differential motion with WRF model data. The relevance of using the WRF model to describe the atmospheric states over observatories was determined by:1. The correct accounting of large-scale advections and diurnal transformations of air masses in domains with relatively high horizontal resolution. 2. Reproducibility of meso-scale structures affecting small-scale turbulence. 3. The reconstruction of vertical profiles of air temperatures and wind speeds with high temporal and vertical resolution. Using gradient approaches, these data make it possible to estimate the vertical profiles of optical turbulence, as well as the Fried parameter and seeing. 4. The clarification or creation of special parameterization schemes that relate the characteristics of meso-scale atmospheric disturbances and small-scale turbulence [23; 24]. These schemes can be used for reliable estimations of the wind speed, air temperature and refractive index fluctuations. 5. Clarification of spatial locations with the best astroclimatic parameters. This study focuses on the summer season. In Section 2, we describe the configuration of the Weather Research and Forecasting model, as well as a method to estimate the strength of optical turbulence. Section 3 is devoted to a comparison of the Fried parameter with wind speed characteristics deduced from WRF simulations. In Section 4, we discuss the results of our calculations. Our final conclusions are drawn in Section 5.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 4 Calibration Accuracy Verification
### Equidistance Displacement Error Verification  
In the process of EDE verification, the ECDSs are placed off the measured target with a certain offset, which is defined as the initial position of the verification test. Then, the target is gradually moved away from the sensor, and the output voltages are recorded at intervals of 0.25 mm until reaching the full scale. Afterwards, the errors between the measured values of displacement interval and the true value (0.25 mm) are calculated. Table 4 shows the EDEs before and after adopting the adjusted calibration method proposed in this paper.  
Figure 11 shows the EDE curves before and after adjustment. From Table 4 and Figure 11, it can be seen that the average of EDE absolute values of Sensors 1 to 4 reduced from 1.18 \(\upmu\)m, 1.43 \(\upmu\)m, 1.86 \(\upmu\)m and 2.06 \(\upmu\)m to 0.29 \(\upmu\)m, 0.29 \(\upmu\)m, 0.27 \(\upmu\)m and 0.52 \(\upmu\)m, respectively, and the standard deviations are also reduced from 0.89 \(\upmu\)m, 1.26 \(\upmu\)m, 1.20 \(\upmu\)m and 1.54 \(\upmu\)m to 0.26 \(\upmu\)m, 0.19 \(\upmu\)m, 0.20 \(\upmu\)m and 0.38 \(\upmu\)m, which indicate that the accuracy of the equidistance displacement measurement is significantly improved.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Minimum** & **Maximum** & **Average of EDE** & **Standard Deviation** \\  & & & **Absolute Value** & **of EDE Absolute** \\  & & & **Value** \\ \hline
**Sensor 1** & \(-1.81/-0.87\) & 3.18/0.76 & 1.18/0.29 & 0.89/0.26 \\
**Sensor 2** & \(-1.89/-0.62\) & 4.39/0.69 & 1.43/0.29 & 1.26/0.19 \\
**Sensor 3** & \(-2.33/-0.38\) & 4.44/0.76 & 1.86/0.27 & 1.20/0.20 \\
**Sensor 4** & \(-1.99/-0.82\) & 4.77/1.46 & 2.06/0.52 & 1.54/0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equidistance displacement errors (EDEs) (\(\upmu\)m) before and after adjustment (the value before ’/’ represents the EDE before adjusted and the value after ’/’ represents the EDE after adjusted).  
Figure 10: Verification schematic diagram.
## 4 Electrical Tomography Applications
### Applications of ET in the Pulp Suspensions Domain
When local flow velocities were measured using the tomographic data (equal liquor fluxes to the top and bottom circulation zones) no flow crossed the screen level. Thus, a stagnation point existed at the centre of the vessel near the plane of the extraction screens. However, by forcing liquor to pass through the screen plane (by directing the flow from the top to the bottom of the digester and bypassing the circulation screens, or vice versa) during part of the cook, the spatial dependence of kappa number (related to the degree of cooking) should be reduced. [PERSON] and co-workers [127] studied the effect of non-Newtonian rheology, including the presence of yield stress, characteristic of pulp fibre in mixing operations, where regions of active motion around the impellers can be created with the cavern size affecting the quality of mixing attained. A scaled version of a commercial axial flow impeller was used in a standard side-entering configuration. Measured cavern diameters were compared against model predictions available in the literature. Since the studied pulp suspensions were opaque, cavern size had to be imaged using indirect techniques for which two methods were used, being ERT one of them (the other was ultrasonic Doppler velocimetry, UDV): ERT was the preferred one due to the marked decrease in the time required to acquire data. The shape of the cavern was best approximated as a truncated right-circular cylinder and, as expected, increasing the impeller speed increased the cavern volume. However, the development of the cavern volume with increasing impeller speed was not uniform, which was attributed to the interaction between the cavern and the vessel walls. A model was developed for predicting cavern development in yield stress fluids which included interaction with vessel boundaries. The proposed model predicts well the trend in cavern volume change with increasing impeller speed although the absolute cavern volume is only predicted to within 13%.
# Interference-Robust Transmission in Wireless Sensor Networks
## 2 System Model
The channel occupancy of the interference signal can be defined by:  
\[\rho=\frac{\tau_{\rm busy}}{\tau_{\rm busy}+\tau_{\rm idle}} \tag{4}\]  
where \(\tau_{\rm busy}\) and \(\tau_{\rm idle}\) denote the mean duration of the busy and the idle state, respectively. It can be shown that \(p_{m,c}\) can be represented as:  
\[p_{m,c}=\rho p_{m,c|\rm busy}+\left(1-\rho\right)p_{m,c|\rm idle} \tag{5}\]  
where \(p_{m,c|\rm busy}\) and \(p_{m,c|\rm idle}\) denote the probability of transmission failure due to the packet collision when the packet transmission is initiated in the presence and the absence of the interference signal, respectively. For a given probability of transmission failure, denoted by \(\tilde{p}_{m,c}\left(L,\gamma\right)\), the data throughput can be represented as:  
\[S_{m}\left(L,\gamma\right)=\frac{L\left(1-\tilde{p}_{m,c}\left(L,\gamma\right) \right)}{\tilde{T}_{m}\left(L\right)}\ \left[\rm bit/s\right] \tag{6}\]  
where \(\tilde{T}_{m}\) denotes the data transaction time (i.e., the round trip time). Define the bulk transfer delay \(D_{\rm bulk}\) by the sum of the access delay \(D_{\rm acc}\) and the transmission delay \(D_{\rm tx}\). The access delay is the time difference between the start of an access attempt and the successful access. Then, it can be shown that:  
\[ \begin{array}{lll}D_{\rm acc}&=&\frac{1}{2}T_{\rm period}+\left(1-p_{\rm sync }\right)\left(1-p_{\rm sch}\right)T_{\rm period}\\ &&+\left\{p_{\rm sync}+\left(1-p_{\rm sync}\right)p_{\rm sch}\right\}\left(1 -p_{\rm sync}\right)\left(1-p_{\rm sch}\right)2T_{\rm period}+\cdots\\ &=&\frac{1}{2}T_{\rm period}+\frac{1}{\left(1-p_{\rm sync}\right)\left(1-p_{ \rm sch}\right)}T_{\rm period}\end{array}  \tag{7}\]  
where \(p_{\rm sync}\) and \(p_{\rm sch}\) denote the failure probability of the frame synchronization and the scheduling, respectively. Note that a low-power WSN can maintain the frame synchronization robustly in the presence of co-channel interference [40]. The transmission delay is the time difference between the beginning of data transmissions and the end of all \(L_{\rm bulk}\)-bit data transmissions. Then, it can be shown that:  
\[D_{\rm tx}=\frac{1}{\left(1-p_{\rm sync}\right)}\left[\frac{L_{\rm bulk}}{T_ {\rm comm}}\bar{s}\right]T_{\rm period} \tag{8}\]  
where \(\bar{s}\) denotes the average data throughput. We maximize the average data throughput of low-power WSNs in the presence of co-channel interference by adjusting the transmission rate and the payload size, minimizing the transmission delay.
## 2 Overview of IoV
### Challenges in the IoV System
#### 2.3.4 Incentive-Related Issues  
As the IoV system is improved, new technologies are integrated into the vehicular communication, aiming to provide more efficient functionality and adaptability to the technical challenges in the system [24]. Some of these technologies include SDN, NFV, mobile edge computing (MEC), AI, etc. These additional technologies bring about concerns around cooperation between vehicles, impeding the accessibility of data between vehicles for efficient data transmission and sharing. The need for an efficient incentive mechanism to encourage participating vehicles in the network to cooperate and initiate data transmission and sharing has now ensued.
## 3 Soil Features as a Consequence of Reduced Fertilization
### Soil Chemistry
#### 3.1.4 Nutrient Content  
Reduced N fertilization influences the availability of key nutrients, including O, K, Mg, and sulfur (S) [23]. Lower N inputs can decrease the availability of N in the soil, which is a primary nutrient for plant growth [18]. Without adequate N concentration, plant biomass production may decrease, leading to a reduction in organic inputs in the soil and, consequently, lower nutrient cycling rates [30, 32, 56].  
However, N fertilization can improve the availability of other nutrients. For example, excessive N fertilization can acidify the soil (discussed further in the Section 3.1.5), reducing the availability of nutrients like Mg and K [35]. By minimizing N inputs, soil acidification is mitigated, which helps maintain a more neutral pH where these nutrients are more bioavailable [30]. Additionally, reduced N inputs may encourage the activity of N-fixing bacteria, such as _Rhizobium_, _Bradyrhizobium_, and _Azotobacter_, which can naturally supplement N levels in the soil [32].  
Regarding S, reduced N fertilization may lead to lower S availability, particularly if S-containing fertilizers are also reduced [56]. S is crucial for protein synthesis in plants, and its deficiency can limit plant growth. Since S is often co-applied with N fertilizers, reducing N inputs can inadvertently decrease S levels, which would need to be managed through alternative S supplementation [57, 58].
## 4 Results and Discussions
### Computational Analysis  
To determine the computational complexity of our proposed VGG16 deep learning model, we need to consider the number of operations required to process each frame. Here is an estimation of the computational complexity based on the information provided:  
1. Frame Down sampling: We process one frame after two seconds.
2. Image Size: Each image is converted to a size of 224 \(\times\) 224 pixels before being passed to the model. This means each frame consists of 224 \(\times\) 224 \(\times\) 0.5 (RGB channels) = 25,088 input values.
3. Model Inference: The computational complexity of these layers can be estimated based on the number of operations required for each layer type. However, the exact number of operations can vary depending on the specific architecture and implementation details.  
As a rough estimate, let us consider a simple CNN architecture with a few convolutional layers and fully connected layers. Assuming a total of N operations are required for the model inference per input frame, the computational complexity for each second of the camera stream (0.5 frames) would be approximately 0.5N operations.
# A Novel 1-D CCANet for ECG Classification
## 3 Proposed Method
### Neural Feature Extraction
#### 3.2.2 First Extraction Stage  
The extraction stage follows the same steps as in [20]. First, for each heartbeat, the output of the first convolution is converted to a decimal one-dimensional signal as per \(T=\sum_{l=1}^{L_{1}}2^{l-1}H([x_{1,cat,l},x_{2,cat,l}])\in\mathbb{R}^{2m}\), where \(H\) is the Heaviside step function. Therefore, the range of each component of \(T\) is \([0,2^{L_{1}}-1]\). \(T\) is then divided in \(B\) blocks of size \(u_{1}\). Each block can overlap with its neighbor, according to \(R_{1}\in[0,1]\), an overlapping proportion parameter. For each of these blocks, a histogram with \(2^{L_{1}}\) bins is built. The values of the resulting histogram for each block is embedded in a \(2^{L_{1}}\)-long vector and the vectors provided by each block are then concatenated to obtain \(\mathit{Bhist}(T)\in\mathbb{R}^{2^{L_{1}}B}\). The first feature vector, for the heartbeat, is \(f^{1}=\mathit{Bhist}(T)\).
## 4 Experimental Results
### Results of Subset2  
Table 4 shows the details of the performance results of all the models using the Subset2 dataset. Accordingly, the model with the lowest RMSE and MAPE results is Model1, which is based on the Bi-LSTM architecture. However, it is noticeable that Model2 and Model3 perform very similarly to Model1 according to the performance metrics.  
Figure 11 shows a graph of the one-month-ahead prediction of Model1, Model2, and Model3, respectively, this time with the test data in the Subset2 dataset. Examining this graph, we can see that the models that can best predict the actual load values are Model1 and Model2, and among them, the most ideal model in terms of its closeness to the actual value is Model1. The difference in RMSE and MAPE values between Model1 and Model2 in Table 3 also confirms this.  
Figure 12 shows a visualization of the results of LIME when Model1, Model2, and Model3 use Subset2, respectively. The XAI results for Subset2 in the figure were obtained using the random second sample in the test set for all models. Looking at the results of Model1 here, we can see that the prediction score of Model1 is 0.79 and the parameters that have a positive effect on this score are the lagged values for the 27 th, 28 th and 29 th days of 'ElecPrice' and the lagged values for the 28 th and 29 th days of 'DryBulb'. In addition, recent lagged time values of 'DryBulb', 'ElecPrice' and 'WetBulb' negatively affect the prediction  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subset2** & **RMSE** & **MAPE** \\ \hline Model1 & 0.058 & 0.055 \\ \hline Model2 & 0.061 & 0.065 \\ \hline Model3 & 0.075 & 0.079 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of Subset2.  
Figure 10: LIME results of Model1, Model2 and Model3 by using subset1 dataset.  
of the model. It can be seen that the prediction result for Model2 is 0.78, and while the lagged values for the 1 st and 23 rd day of 'ElecPrice' have a positive effect on the result, the lagged values of the remaining variables colored in blue on the right side of the figure for Model2 have a negative effect. For Model3, the predictive score is 0.77, and all of the variables listed on the right side contribute negatively to the model reaching this minimum predictive score. Summarizing the overall situation for Subset2, where three features were selected from the first five features of the dataset and prepared by taking all the lagged values of these features up to 30 days, it can be inferred that determining the lagging range based on 30 days is the right step because the lagged values of the selected features going back further strengthen the prediction of the models. In addition, 'DryBulb' and 'ElecPrice', which are the most effective features for Subset2, improve the model in real terms, so an effective feature selection can be achieved.  
Figure 11: Forecasting results of the models based on subset2: (**a**) Model1 results; (**b**) Model2 results; (**c**) Model3 results.  
Figure 12: LIME results of Model1, Model2 and Model3 by using subset2 dataset.
## 3 Requirements Analysis
### Software Functional Analysis  
The C-V2X large-scale testing system is primarily designed to investigate communication performance under different regional vehicle density conditions. However, OEMs are concerned about the impact of communication performance on the V2X application response feature. Therefore, the proposed C-V2X LSTS provides communication performance testing and supports scenario-oriented application testing, which are significant challenges in system software design.  
As shown in Figure 1, the C-V2X LSTS includes four functional entities: the scenario generator, rgw scenario injection module, the test control and analysis module, and the C-V2X testbed. The first three are run on the test central server, while the last one is deployed on the roadside.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 3 Navigation Integration Using the State Estimators
The VB approach is an inferential technique that approximates the real posterior distribution of hidden variables using a straightforward distribution, typically presuming that the disguised variables are unrelated to one another. In contrast to sampling methods, the VB has been created for a several models to perform preliminary posterior inference with a minimal computing cost. The typical filters for navigational state estimation and their improved algorithms are summarized in Table 1. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Navigation Fusion**} & \multicolumn{1}{c}{**Features**} \\ \hline Linear Kalman filter algorithm & - & Simple calculation and easy implementation \\ \hline Nonlinear filter algorithms, including the EKF, UKF, and CKF & - & Good processing capacity for nonlinear systems \\ \hline Sequential Monte Carlo (SMC) approaches & - & Particle filter (PF) is the well-known realization of the SM approach \\ \hline Adaptive filter algorithms & - & Adapt to time-varying \\  & - & Innovation-based adaptive estimating (IAE) and \\  & - & multiple-model-based adaptive estimation (MMAE) \\  & - & including the interacting multiple model (IMM) algorithm \\  & - & are the two main approaches. \\  & - & The variational Bayesian (VB) learning algorithm analyzes \\  & - & the real posterior distribution of concealed parameters \\  & - & approximation using a straightforward distribution. \\ \hline Neural-network-assisted filtering algorithm & - & Simple, easy to learn, no need for systematic \\  & - & mathematical models \\  & - & Good network predictability with real-time performance. \\ \hline Deep learning improvement algorithm & - & Improved accuracy \\  & - & Good generalization capability. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The navigation filters and their improved algorithms.
# Heat Capacity of Drained Peat Soils
## 3 Results  
### Heat Capacity Data  
#### 3.1.1 Specific Heat of Investigation Organic Soils  
Figure 2 presents the data set of temperature-dependent specific heat capacity values obtained experimentally for the peat-moorsh soil profiles. The lowest measured values were observed for the lightly decomposed (H1-H2) moss peaks (P17 and P18) according to von Post scale. Conversely, the highly decomposed (H7) adler peaks (P8-P10) had the highest specific heat capacity values. Relatively similar heat capacity data were observed for the group including sedge soil layers (P5, P12, P13 and P16) with moderate soil organic matter decomposition (H2-H5). The \(c_{p}\) data were highly variable in the group comprisingupper soil layers (P1, P4, P7, P11, P14, and P15). The fitting lines at Figure 2 present the average linear relationships between specific heat capacity (\(c_{p}\)) and temperature (\(T\)) for each distinct soil group. For each soil layer, the parameters of the linear Equation (1) were determined and included in Table 2 with basic statistical measures of the quality of the fit. The \(C_{0}\) parameter varied from approximately 1.040 to 1.330 J g\({}^{-1}\) K\({}^{-1}\) with a mean value of 1.202 J g\({}^{-1}\) K\({}^{-1}\). The slope value, \(C_{1}\), representing the change in specific heat capacity for a 1 \({}^{\circ}\)C change in temperature, ranged from 0.0034 to 0.0048 J g\({}^{-1}\) K\({}^{-1}\) with a mean value of 0.0043 J g\({}^{-1}\) K\({}^{-1}\) for the 18 soil layers studied. It should be stressed that both linear parameters (\(C_{0}\) and \(C_{1}\)) were the smallest in the moss peaks (P17, P18). The maximum \(C_{0}\) value was obtained for the alder peat (P10), whereas the highest slope estimator occurred in the sedge-reed peat (P5). Figure 3 illustrates the performance of the linear equations in predicting the measured data. For both cases the \(c_{p}\) values calculated using the linear equation and leave-out-one scheme were similar to the measured data as shown by a comparison line close to unity. Figure 2: Temperature-dependent soil specific heat capacity characteristics for the 18 soil layers analyzed, grouped by organic matter origin. Colors and shapes indicate the different soil layers and sites according to the data presented in Table 1. The equation parameters indicate the intercept (\(C_{0}\)) and slope (\(C_{1}\)). Y represents \(c_{p}\) and X is soil temperature. The data presented in Figure 3 and Table 2 suggest that higher errors in the \(c_{p}(T)\) estimation are determined for moss peaks when compared to the \(c_{p}(T)\) error prediction of the other soils. The results obtained indicate that the \(C_{0}\) and \(C_{1}\) parameter significantly explains the relationship between soil temperature and specific heat capacity data. Hence, the further analysis of the \(c_{p}(T)\) data was focused on establishing statistical equations enabling prediction of the line parameters (\(C_{0}\) and \(C_{1}\)) based on the soil chemical and physical properties.