esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optical Flow
Generally, SSH maps have sufficiently rich texture within a patch size of \(5\times 5\) pixels, which we assume in this work (Figure 2a). Any land and out-of-bound pixels falling within \(W\) are discarded, reducing the number of equations in (4). For the grid spacing of \(1/4^{\circ}\) and the patch size of \(5\times 5\) pixels, the spacing between the estimates of \(\mathbf{u}(u,v)\) based on non-overlapping \(W\) is \(1.25^{\circ}\). This is less than the effective spatial resolution of SSH maps everywhere within the domain. Recall that the derivation of (3) assumes small displacements \(\Delta x\) and \(\Delta y\), on the order of one pixel. For larger displacements, which may happen in dynamically active regions such as the ACC and western boundary currents, the Taylor series approximation (2) and the OF constraint Equation (3) are no longer valid. This is resolved by the recursive implementation of the Lucas-Kanade method applied to multi-resolution Gaussian pyramidal representation of SSH maps [44]. The OF is then computed recursively, starting from the lowest-resolution level of the pyramid, and propagating the resultant flow to and updating it at the next higher-resolution level until the original map (with the highest resolution) is reached (Figure 2b). The number of levels in the pyramid is set to achieve a motion of roughly one-pixel at the lowest resolution. In this work, we use three levels, where the original SSH map resolution is reduced by a factor of two relative to the previous level. Unlike the space-time-lagged correlation analysis, the OF technique does not require spatial and temporal constraints to isolate the required scales of ocean variability. Instead, it estimates the motion of individual pixels that may belong to SSH patterns of any spatial scale. The scale separation can be performed at the stage of data preparation by applying spatial and temporal filtering, as was done in this work by removing the global mean SSH, the seasonal cycle, and the low-frequency signals with periods greater than 1.5 years. The residual variability is dominated by transient mesoscale eddies. As mentioned above, because \(u\) and \(v\) are computed between two consecutive images, applying the OF to a series of SSH maps yields the time series of the propagation velocities of SSH patterns.
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
## 3 Description of the Pretreatment Method
### Description of the RBF-NNM  
The RBF-NNM was a three-layer forward network, as shown in Figure 10. Through the radial basis function in each hidden layer, the input signal \(X(n)\) can find the optimal fitting plane in multidimensional space. Therefore, the RBF-NNM provides a good fit in the prediction of a chaotic signal time series.  
The Gaussian kernel function is normally used in the RBF-NNM, as shown in Equation (17); the output \(y(n)\) is shown in Equation (18).  
\[G_{i}(X)=\exp\left[-\frac{\left\|X-C_{i}\right\|^{2}}{2\sigma_{i}^{2}}\right],i=1,2,\cdots,m \tag{17}\]  
\[y(n)=\sum_{i=1}^{m}\omega_{i}G_{i}(X) \tag{18}\]  
where \(X\) is the \(m\)-dimension input matrix, \(C_{i}\) is the center of the \(i\)-th basis function, \(\sigma_{i}\) is the width of the \(i\)-th hidden node, \(m\) is the number of the perceivable unit, \(\omega_{i}\) is the connection weight from the \(i\)-th hidden layer to the output.  
The above parameters can be obtained by using the K-means clustering algorithm, as follows:  
1. Determine the cluster center: first, some clustering centers were determined randomly and defined as \(c_{i}(i=1,2,\cdots,h)\). The training samples \(x_{j}\) were allocated to each cluster set \(v_{j}(j=1,2,\cdots,P)\), based on the principle of minimizing the Euclidean distance between the training samples and the clustering center. The samples average in each set was iterated, and the result was defined as the new cluster center \(c_{i}\). Repeat the calculation cycle until the residual was less than the setting value and use the final \(c_{i}\) as the cluster center of the basis function;
2. Determine the width of the hidden node: the maximum \(c_{i,\text{max}}\) was chosen from the cluster center set, and the width of the hidden node can be solved by Equation (19); \[\sigma_{i}=\frac{c_{i,\text{max}}}{\sqrt{2h}},(i=1,2,\cdots,h)\] (19)
3. Determine the connection weights: the least-square method was used to calculate the weight, as shown in Equation (20).  
Figure 10: Schematic diagram of RBF-NNM.  
\[\omega_{i}=\exp(\frac{h}{c_{i,\text{max}}^{2}}\|x_{j}-c_{i}\|^{2}),(i=1,2,\cdots,h; j=1,2,\cdots,P) \tag{20}\]  
The training epoch \(N_{q}\) and the testing epoch \(N_{p}\) in Section 2.1 were used to build the RBF-NNM. The diffusion coefficient \(s\) of RBF was analyzed, and the optimum value was chosen. Commonly, the fitting radial basis function is smoother when \(s\) is larger, but if it is larger than a critical value, some numerical problem will appear. As seen in Figure 11, \(s\) was optimized in the range from 50 to 500, and when it was 400, the MSE was at its minimum. Therefore, in the following analysis, the diffusion coefficient was set to 400.  
The RBF-NNM was used to predict the trend of the signal sample, as shown in Figure 12. From it, it appeared that the RBF-NNM can also predict the changing trend of the flow field signal.
## 2 Localization Solution
### Mathematical Principles of Hyperbolic Multilateration  
The core of the localization method developed in this work is based on the analysis of isochrone curves generated by time differences (\(\Delta t\)) between signals from pairs of beacons. These isochrones are hyperbolic curves that represent all points in space where the difference in the distances to two beacons is constant.  
While this mathematical model forms the foundation of our localization system, the practical implementation involves considerations beyond the scope of this analysis, particularly regarding optimal beacon placement.  
For a pair of beacons located at \((x_{1},y_{1})\) and \((x_{2},y_{2})\), the equation for the hyperbolic isochrone curve can be expressed as follows:  
\[\sqrt{(x-x_{1})^{2}+(y-y_{1})^{2}}-\sqrt{(x-x_{2})^{2}+(y-y_{2})^{2}}=\Delta t\cdot v \tag{1}\]  
where  
* \((x,y)\) represents any point on the hyperbola;
* \(\Delta t\) is the measured time difference between signal arrivals from the two beacons;
* \(v\) is the speed of sound in the medium (approximately 343 m/s in air at room temperature).  
To determine the robot's position, the intersection of isochrone curves from different pairs of beacons should be found. Consider a system with three beacons located at \((x_{1},y_{1})\), \((x_{2},y_{2})\), and \((x_{3},y_{3})\). Two hyperbolic equations can be formed:  
\[\begin{cases}\sqrt{(x-x_{1})^{2}+(y-y_{1})^{2}}-\sqrt{(x-x_{2})^{2}+(y-y_{2}) ^{2}}=\Delta t_{12}\cdot v\\ \sqrt{(x-x_{2})^{2}+(y-y_{2})^{2}}-\sqrt{(x-x_{3})^{2}+(y-y_{3})^{2}}=\Delta t _{23}\cdot v\end{cases} \tag{2}\]  
Figure 4: Isochrones passing through the robot’s position. The intersection of these isochrones from different beacon pairs indicates the robot’s precise location.  
where \(\Delta t_{12}\) and \(\Delta t_{23}\) are the measured time differences between signals from beacons 1 and 2, and beacons 2 and 3, respectively.  
Solving these nonlinear equations simultaneously provides the coordinates \((x,y)\) of the robot. This solution represents the unique point where all the measured time differences are satisfied, thus pinpointing the robot's position.
## 3 Results
### Correlations Found  
The average of all NRT threshold values was lower in patients that were diagnosed with SNHL than with otosclerosis (\(t\) = 3.069; \(p\) = 0.034) (Figure 2). The electrode insertion depth was negatively correlated with the average of all NRT threshold values (r = \(-\)0.464; \(p\) = 0.026).  
There was no statistically significant correlation between the average of all NRT threshold values and the patient's age (r = 0.262; \(p\) = 0.227); the average of all NRT threshold values and duration of hearing loss before surgery in years (rho = 0.056; \(p\) = 0.788); the average of all NRT threshold values and hearing preservation (r = 0.080; \(p\) = 0.751); slow electrode insertion and postoperative average values of tone audiometry (U = 44.000; \(p\) = 0.300); the average of all NRT threshold values and speech audiometry at 65 dB (rho = -0.070; \(p\) = 0.751); and the percentage of words recognized on speech audiometry with electrode insertion depth (rho = 0.386; \(p\) = 0.069).  
Figure 1: On the left there is an exposed round window membrane prepared for incision. On the right there is a slim straight electrode inserted through the round window in the same patient.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{**Otosclerosis (n = 4)**} & \multicolumn{4}{c}{**SNHL (n = 18)**} \\ \cline{2-9}  & **Mean \(\pm\) SD** & **Median (95\% CI)** & **Min, Max** & **Mean \(\pm\) SD** & **Median (95\% CI)** & **Min, Max** & **Test Value (p)** \\ \hline Age (years) & 67.5 \(\pm\) 7.9 & 66.5 (59, 78) & 59, 78 & 61.2 \(\pm\) 16.0 & 61 (57, 71) & 22, 89 & \(t\) = 0.759 (0.457) \\ \hline Gender & female N (\%) & & 2 (50.0\%) & & 12 (66.7\%) & & \\ \cline{2-9}  & male N (\%) & & 2 (50.0\%) & & 6 (33.3\%) & & (0.602) \({}^{*}\) \\ \hline Electrode insertion depth (degrees) & 372.5 \(\pm\) & 385 (310, 410) & 310, 410 & 423.1 \(\pm\) & 437.5 (405, 460) & 300, 495 & t = \(-\)1.733 (0.099) \\ \hline Average of all NRT thresholds (microvolts) & 213.1 \(\pm\) & 212.9 (195.4, 231.2) & 195.4, 231.2 & 186.3 \(\pm\) & 186.1 (179.0, 15.0) & 158.4, 209.9 & t = 3.069 (0.034) \\ \hline Speech audiometry at & & & & & & & \\ \cline{2-9}  65 dB after surgery (\%) & 78.8 \(\pm\) 19.3 & 87.5 (50, 90) & 50, 90 & 74.7 \(\pm\) 26.4 & 85 (60, 90) & 20, 100 & U = 35.000 (0.931) \\ \hline \end{tabular}  
* Fisher exact test; p: \(p\)-value; t: \(t\)-test value; U: Mann-Whitney U test value; SD: standard deviation; CI: confidence interval.  
\end{table}
Table 3: Comparison of otosclerosis and SNHL groups.
## 3 Phase-Only Synthesis Through Sequential Stages with Different Illuminations
In order to obtain the components \(E_{0x}^{\mathrm{inc}}\) and \(E_{0y}^{\mathrm{inc}}\) of the tangential incident electric field on the reflectarray elements with respect to its coordinate system, a rotation of the resultant electric field \(\mathbf{E}^{\mathrm{F}}\) has to be carried out by matrix multiplication with a matrix whose columns contain the director cosines of \(\{\mathbf{\hat{x}}_{\mathrm{F}},\hat{\mathbf{y}}_{\mathrm{F}},\hat{\mathbf{z}} _{\mathrm{F}}\}\) in the reflectarray coordinate system [2]. Depending on the model of the feeder antenna, variation in the edge illumination can be easy or difficult to implement. According to Equation (19), the advantage of using the \(\cos^{q}(\theta)\) model of the radiated electric field is its ease in changing the level of illumination at the edges of the reflectarray panel by adjusting the power \(q\) (i.e., changing the taper of illumination). For example, let us consider a feeder antenna located at the point \((-325,0,101)\), referring to the reflectarray coordinate system in millimeters. Its radiation pattern has been modeled as a symmetric \(\cos^{q}(\theta)\) pattern [2;30] at 13.5 GHz. Figure 2 illustrates the magnitude of the \(x\) component of the incident electric field (dB), which was normalized relative to the peak illumination (located at the center of the reflectarray) with different values for the power \(q\) on the same reflectarray panel. These results were obtained for a reflectarray \(832\times 806\) mm\({}^{2}\) in size. The total reflectarray cell count was 3124, and they were arranged in a rectangular grid with \(64\times 62\) cells and a cell size of \(13\times 13\) mm\({}^{2}\). The results shown in this figure were obtained by homemade script code programmed in the FORTRAN programming language on an conventional laptop Intel(R) Core(TM) Ultra 7 155H 3.80 GHz processor with 32.0 GB of RAM. In next section, these illumination distributions will be used to obtain convergence results by means of the four algorithms used in [9].
## 2 Methodology
### Flight
#### 2.2.1 Atmospheric Model  
Atmospheric temperature, (\(T_{a}\)), and atmospheric pressure, (\(p_{a}\)), vary with altitude, \(H\). This variation influences the air density (\(\rho_{a}\)), which in turn affects the drag on any vehicle passing through the Troposphere, lower Stratosphere and upper Stratosphere. In this research, the US Standard Atmosphere Model, 1976 [32], is used to calculate the pressure, temperature and density with increasing altitudes as per the following equations.  
For the Troposphere with an altitude below 11,000 m, \(T_{a}\) and \(p_{a}\) are calculated via the following:  
\[T_{a}=15.04-0.00649\times H \tag{30}\]  
\[p_{a}=101.29\times(\frac{T_{a}+273.1}{288.08})^{5.256} \tag{31}\]  
When the LPRE is travelling through the lower Stratosphere where the altitude is between 11,000 m and 25,000 m, the following set of equations are used to obtain \(p_{a}\) and \(T_{a}\):  
\[T_{a}=-56.46 \tag{32}\]  
\[p_{a}=22.65\mathrm{e}^{1.73-(0.000157\times H)} \tag{33}\]  
The upper Stratosphere lies above 25,000 m. For these higher altitudes, (\(T_{a}\)) and (\(p_{a}\)) are determined as per the following equations:  
\[T_{a}=-131.21+0.00299\times H \tag{34}\]  
\[p_{a}=2.488\times(\frac{T+273.1}{216.6})^{-11.388} \tag{35}\]  
The three equations are given above for calculating \(T_{a}\) return values in \({}^{\circ}\)C. Therefore, an additional step is required to add 273.1 to each value to convert to Kelvin. Similarly, the values of \(p_{a}\) are in kPa and must be converted to Pa in order to ensure all units are uniform throughout the framework.  
The air density, \(\rho_{a}\), is a function of \(p_{a}\) and \(T_{a}\) and is calculated via the followin.  
\[\rho_{a}=\frac{p_{a}}{(0.286\times(T_{a}+273.1)} \tag{36}\]
## 2 Related Work
Instead of adjusting based on the intrinsic properties of the data, they typically compute loading and score matrices under specific constraints. 4. Over-Reliance on Assumptions: Some advanced methods heavily depend on specific assumptions about data distribution or external variables, such as employingmultivariate Laplace distributions for robust modeling [24]. If these assumptions do not match reality, the model performance can suffer. * Trade-off Between Interpretability and Predictivity: Improvements like Orthogonal Partial Least Squares (OPLS) have enhanced the interpretability of PLS models but have not directly improved their predictive power [25]. In some cases, enhancing one aspect might come at the expense of the other. * Challenges with Multimodal Data: For data collected from modern industrial processes that exhibit complex, non-Gaussian, and multimodal characteristics, current PLS models and their variants may struggle to adequately capture the intrinsic structure of the data, leading to decreased prediction accuracy. In conclusion, while existing methods provide valuable enhancements to PLS regression, they face significant challenges, especially in handling high-dimensional, noisy, and multimodal data. To address these issues, a novel PLS model that incorporates adaptively learned filters is introduced. This approach dynamically adjusts during the modeling process to better fit the data characteristics, thereby improving both the robustness and predictive performance of the model. The proposed method is summarized in Figure 1.  
The introduction of adaptive filters within PLS models marks a significant advancement in addressing these challenges. By integrating adaptive filtering directly into the PLS framework, this novel approach not only mitigates noise but also dynamically selects features most relevant to the predictive task at hand. Unlike static preprocessing methods where filters are applied prior to modeling, or probabilistic PLS models which assume certain data distributions, the proposed model learns filters adaptively during the model's construction phase. This adaptive learning process optimizes filter parameters concurrently with loading and score matrices, leading to enhanced suppression of noise, effective feature selection, and ultimately superior model performance. The adaptability of the filters allows for a more flexible and efficient handling of complex datasets, ensuring that the model remains robust and accurate even in the presence of non-Gaussian and multimodal data characteristics. Thus, embedding adaptive filters within PLS models represents a crucial step forward in improving both the interpretability and predictivity of PLS models, setting a new standard for handling high-dimensional and noisy data. Figure 1: Flow chart of the proposed method.
## 5 Conclusions and Further Work  
In this paper, we proposed FRBP, a real-time network broadcast protocol for the (\(m\), \(k\))-firm real-time model, that is applicable in WSNs. FRBP operates over the real-time broadcast tree that consists of only some sensor nodes satisfying the real-time condition as intermediate transmission nodes for the real-time constraint. In the broadcast tree, leaf nodes only receive packets, but do not transmit them for energy efficiency. While constructing the broadcast tree, FRBP considers two major inherent characteristics of WSNs, resource-constrained sensor nodes and an unstable network environment. For resource-constrained sensor nodes, FRBP tries to elect as few intermediate nodes as possible in the tree. To overcome the unstable network environment, FRBP performs local real-time failure avoidance and recovery based on DBP by measuring (\(m\), \(k\))-firm real-time conditions, as well as monitoring local states. Finally, simulation results demonstrated that FRBP can satisfy the (\(m\), \(k\))-firm real-time requirement with reasonable energy consumption in various environments. In summary, FRBP consumes on average 26% the energy of flooding, and its deadline missed ratio is on average 58% that of the other three protocols.  
Figure 11: Average energy consumption impacted by the link failure ratio.  
Figure 10: Deadline missed ratio impacted by the link failure ratio.  
The remaining further work will be focusing on extending FRBP to support multicast as group communications. In addition, practical applications such as real-time mobile event tracking and its deployment issues will be studied. Moreover, extensive simulation studies will be conducted by analyzing the impact of multiple (\(m\), \(k\))-firm streams.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A3A01019680). Furthermore, it was supported by the Research Incentive Fund (R17058) by Zayed University, Abu Dhabi, UAE.  
[PERSON], [PERSON] and [PERSON] contributed to the FRBP protocol design and the detailed algorithm. [PERSON] and [PERSON] contributed to the performance evaluation and analysis.  
The authors declare no conflict of interest.
# Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments
## 3 Proposed Scheme
### Initial Authentication Process  
Figure 5 shows Initial Authentication Process and Smart Device Registration. The gateway that controls the home network of a specific area constantly sends advertisement messages. Smart nodes that are newly registered in the network system send join messages in response to these advertisement messages; the gateway verifies smart nodes through CA. When verification is completed, a smart device carries out mutual verification and key exchange as follows [38].  
1. The smart node that received an advertisement message generates \(M_{0}\) and \(H_{0}\) for self-verification and sends them to the gateway.
2. The gateway that received \(M_{0}\) and \(H_{0}\) generates \(M_{1}\) and \(H_{1}\) and sends them to the CA manufacturer.
3. The CA that received information from the gateway obtains two random numbers through decoding and verifies \(H_{0}\) and \(H_{1}\) through searched \(PW\). It generates \(M_{2}\) and \(M_{3}\) and sends them to the gateway.
4. The gateway that received \(M_{2}\) and \(M_{2}\) gains \(R_{1}\) from decoding \(M_{3}\) and checks for any error in the received value through a hash function. It saves 3 \(\times\)\(n\)-bit information generated by two random numbers for verification and then sends \(M_{2}\) to the smart node.  
Figure 5: Initial authentication and device registration process.  
* The smart node that received \(M_{2}\) gains \(R_{2}\) and verifies the sent value through a hash function. If the verification is finished, the node saves \(3\times n\)-bit information, as above.
* The smart node generates random number \(C_{1}\) to perform the verification step and sends it by bit. At this point, the time check for preventing relay attacks begins.
* The gateway that received bits from the smart node sends the \(i\)th bit of \(R_{0}\) if \(C_{1}=0\) in response, and sends the \(i\)th bit of \(R_{1}\) to the smart node if the response is \(1\).
* The smart node generates \(R_{i}^{\mathit{cn}}\) based on the \(c\) sent to the gateway and compares it to \(R_{i}^{\mathit{cn}}\), which is the gathered value of the cluster head's response to verify whether the data was sent from the correct node. After \"time off,\" it guesses the distance through time measurement and stops communication if this is greater than a specified time.
* The smart node that verified the gateway sends the received \(R_{i}^{\mathit{cn}}\) values using the \(f(0)\) function to the gateway.
* The smart node that received a certification value from the gateway generates a certification value in same way and compares it to the value received from the cluster head to verify the smart node.
* The smart node and the gateway generate a session key using the left \(n\) bit and a random number from the \(3\times n\)-bits and ends the verification.
# High-Quality Classroom Dialogue Automatic Analysis System
## 5 Experiments
### Experimental Results  
In this section, we present the results of our dialogue classification task based on the IRE-9 and Gagne frameworks. We evaluated the performance of our model using precision (P), recall (R), and F1-score (F1) as the key metrics, and our experiment's best results are as shown in Table 3. The results show that our model achieved varying levels of metrics for each class. Classes \(I_{1}\), \(I_{2}\), \(R_{2}\), and \(E_{1}\) achieved high precision and recall, resulting in high F1 scores. We observed that the model excelled in correctly identifying samples from Class 1, 3, 5, and 7, as indicated by the high recall values in these classes. However, it struggled with Class \(I_{2}\) and \(I_{3}\), where false negatives were more prevalent. This suggests that our model still needs to improve its semantic understanding of \"open questions\" and \"closed questions\". In terms of macro-averages, the model achieved a macro-average precision of 0.7965, recall of 0.7462, and F1-score of 0.7664 for IRE-9 categories. These results indicate a balanced performance across categories, though certain classes exhibited higher variability. For the Gagne framework, the results followed similar patterns, with a macro-average precision of 0.7719, recall of 0.7639, and F1-score of 0.7653. The weighted averages were slightly higher, indicating strong performance even when accounting for class imbalances. A more detailed analysis of the misclassifications reveals that the model struggled with certain classes, particularly R1 and I3, due to the context-dependent nature of these categories. Incorporating additional contextual features or multimodal data could help address these challenges. Further fine-tuning the model is expected to improve overall performance, particularly by reducing false positives in the more complex dialogue categories.