esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Acquisition and Parallel Design and Implementation of MIR Algorithm
### Design and Implementation of PMIR Algorithm  
The proposed parallelized version of the MIR algorithm is designed and implemented using PySpark. A detailed flowchart is shown in Figure 3. The sensor data is stored on Eddie distributed file system. The master node reads the input data and distributes it to all workers in the cluster with shared memory. This also includes broadcasting locations of antennas in antenna array to each worker node. To make each of the calculation more efficient, resilient distributed data sets (RDDs) are used. The input data received through RF sensors is first converted to an RDD and stored in the distributed memory. An empty matrix is created and broadcasted, which is shared by different worker nodes.  
The map operation from map-reduce programming model is then used to calculate subset of values in parallel using Spark context object. The \"map\" operation computes the pixel value on each worker node in the cluster, and update the empty matrix, in parallel. Finally, reduce operation sums up the different subsets from different computing nodes in the cluster and returns it to the master node, until signal samples are in range. The master node is used to reconstruct the images using simple Python functions. The resultant images are stored back to the Eddie distributed file system.  
The parallel version of the MIR algorithm is implemented using Python programming language. The pseudo-code of the proposed parallelized MIR algorithm is shown in Algorithm 2.  
The above process is repeated for all cases and the number of diagnostics performed.  
Figure 3: The parallelism of the microwave image reconstruction (MIR) algorithm. RF = radio frequency.  
```
0: Radio Frequency Sensors Data
0: Brain Images
1: Read data from Eddie distributed file system
2: Copy the RDD for delay and Antennas location to each worker node
3: Set the variables z, distance, and energy to zero
4: While\(N_{lip}\) < range do
5: Calculate the subset of pixel points on each worker node in parallel based on Equation (2) using Map operation
6: Update the master node concurrently with a subset of pixel values using Reduce operation
7: End While
8: Reconstruct the image from the matrix of pixel values on master node
9: Save the image to the Distributed file system on Eddie
```  
**Algorithm 2** PMIR algorithm
## 4 The Hypothesis Test Based on Mahalanobis Distance for the Bias Check  
The parameter estimator bias can be a potential problem for UWB positioning. To check if the bias in nonlinear least-squares estimator is too large, the hypothesis test based on Mahalanobis distance is proposed.  
\[\hat{\mathbf{X}}_{1}=\widetilde{\mathbf{X}}+\partial M(\widetilde{\mathbf{L}} )(\mathbf{L}-\widetilde{\mathbf{L}}) \tag{13}\]  
\[\hat{\mathbf{X}}_{2}=\widetilde{\mathbf{X}}+\partial M(\widetilde{\mathbf{L}} )(\mathbf{L}-\widetilde{\mathbf{L}})+\frac{1}{2}\partial^{2}M(\widetilde{ \mathbf{L}})(\mathbf{L}-\widetilde{\mathbf{L}})^{2} \tag{14}\]  
\[\begin{array}{rl}\lambda=D_{M}^{2}&=\Big{(}\sqrt{{(\hat{\mathbf{X}}_{2}-\hat{ \mathbf{X}}_{1})}^{\text{T}}\mathbf{Q}_{\widetilde{\mathbf{X}}}^{-1}(\hat{ \mathbf{X}}_{2}-\hat{\mathbf{X}}_{1})}\Big{)}^{2}\\ &=\Big{(}\sqrt{{b_{\hat{\mathbf{X}}}}^{\text{T}}\mathbf{Q}_{\widetilde{\mathbf{X }}}^{-1}b_{\hat{\mathbf{X}}}}\Big{)}^{2}\\ &={b_{\hat{\mathbf{X}}}}^{\text{T}}\mathbf{Q}_{\widetilde{\mathbf{X}}}^{-1}b_{ \hat{\mathbf{X}}}\end{array} \tag{15}\]where \(D_{M}=\sqrt{\left(\hat{\mathbf{X}}_{2}-\hat{\mathbf{X}}_{1}\right)^{T}\mathbf{Q}_{ \widehat{\mathbf{X}}}^{-1}\left(\hat{\mathbf{X}}_{2}-\hat{\mathbf{X}}_{1} \right)}\) denotes the Mahalanobis distance, \(\lambda\) is the square of Mahalanobis distance and is the test statistic term, \(\hat{\mathbf{X}}_{1}\) is the parameter estimator with Gaussian distribution considering only the first-order Taylor expansion, \(\hat{\mathbf{X}}_{2}\) is the parameter solution ignoring the terms of the third order and higher. The test statistic term is the Mahalanobis distance, which can be a measure of significance [38].  
The hypothesis test can be carried out for testing the bias:  
\[P(\lambda>\gamma)=P(D_{M}>\sqrt{\gamma})=\alpha \tag{16}\]  
where \(\gamma\) is predetermined 3.84 by the chosen level \(\alpha\) of significance being 0.05 based on Chi-square distribution table.  
By assuming that the observations obey Gaussian distribution, when the parameter estimator bias \(b_{\hat{\mathbf{X}}}\) is small, the Mahalanobis distance \(D_{M}\) is smaller than the threshold \(\sqrt{\gamma}\) of the test statistic, and \(\lambda\) should be Chi-square distributed with the dimension of the observation vector as the degree of freedom (DOF). If \(D_{M}\) is larger than \(\sqrt{\gamma}\), and the value of test statistic will fall in the right tail-area of the distribution and the null hypothesis can be rejected. We can believe that the model error for UWB positioning does occur, and the linearized model is not sufficiently approximate. The results show that since the large bias due to nonlinearity in UWB position, the estimator needs to be corrected to endure the unbiasedness for UWB positioning.
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 5 Conclusions  
A compact-sized, dual-port, flower-shaped UWB-MIMO antenna with high isolation is presented in this paper. Three elliptically shaped metal patches, located at 60 degrees from each other, were combined to form a flower-shaped radiating element. The adopted ground structure improvements, including two modified inverted L-shaped branches and an I-shaped stub, expand the impedance bandwidth by generating multiple resonance modes to cover the 4.3-15.63 GHz (relative bandwidth 113.4%) range, which is typically used for multi-standard wireless applications, such as 5G N79 (4.4-5 GHz), WLAN (5.15-5.35 GHz/5.72-5.825 GHz), 5G spectrum band (5.9-6.4 GHz), X-band for satellite communication (8-12 GHz), FSS (11.45-11.7 GHz/12.5-12.75 GHz), and Ku band (12-18 GHz). These improvements also effectively reduce the mutual coupling between the antennas by absorbing the current, thereby enhancing the isolation to more than 20 dB. In addition, the measured results suggest that the proposed antenna displays favorable radiation patterns, where the radiation efficiency is between 85% and 93%, while the peak gain ranges from 2.5-5.35 dBi. Moreover, the antenna also possesses acceptable values for ECC (<0.0075), DG (>9.96 dB), and TARC (<-20 dB), demonstrating that the proposed MIMO antenna is highly compatible with the UWB communication systems.  
Conceptualization, Z.W.; methodology, H.L. and M.Y.; software, W.M.; validation, W.M. and C.L.; investigation, W.N.; writing--original draft preparation, W.M.; writing--review and editing, H.L. and Z.W.; supervision, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Anhui Provincial Natural Science Foundation under grant 2108085 MF200, the Natural Science Foundation of Anhui Provincial Education Department under grant KJ2020A0307 and KJ2020A0768, the Academic Funding Project for Distinguished Top Talents of Colleges and Universities in Anhui Province under grant gxbjZD2021088, and the Graduate Innovation Fund of Anhui University of Science and Technology under grant 2021 CX2070.  
Not applicable.  
The simulated and measured data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 4 Active Antiresonance Assignment with No Additional Masses
### State-Feedback Approaches
#### 4.3.3 Assignment for Point-Receptances through Unit-Rank Output Feedback with Time Delay  
Active antiresonance assignment for symmetric LTI systems with time delay has been investigated by [PERSON] et al. in [PERSON] et al. (2018), where output feedback is exploited. The authors refer to their method as \"nodal control\", in contrast to the full-state feedback control. Due to the presence of time delay \(\tau\) in sensor measurements, the characteristic equation of the closed-loop system is transcendental. Therefore, it has an infinite number of roots: \(2N\) roots are the so-called \"primary roots\", while an infinite number of \"secondary roots\" (or \"latent roots\") is due to the presence of the time delay and appears in the controlled system [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). The authors, in [PERSON] et al. (2018), exploited a low-order Taylor series approximation of the time-delay and computed the control gains for the assignment of antiresonance to the point-receptance of a non-conservative system through a single-input and single-output control. The scalar control force is therefore:  
\[u(t-\tau)=-k_{c}x_{N-1}(t-\tau)-c_{c}\dot{x}_{N-1}(t-\tau) \tag{31}\]  
By exploiting the second-order model and some mathematical developments similar to those employed in [PERSON] et al. (2018), a closed-form solution of the position feedback gain \(k_{c}\) and the velocity feedback gain \(c_{c}\) is obtained:  
\[k_{c}=\frac{\omega_{z}^{d}\tau\text{Im}(\phi)-\text{Re}(\phi)}{\tau^{2}\big{(} \omega_{z}^{d}\big{)}^{2}+1},\quad c_{c}=\frac{\text{Im}(\phi)+\omega_{z}^{d }\tau\text{Re}(\phi)}{\omega_{z}^{d}\Big{(}\tau^{2}\big{(}\omega_{z}^{d} \big{)}^{2}+1\Big{)}} \tag{32}\]  
where  
\[\phi=-\frac{\text{det}\Big{(}-\Big{(}\omega_{z}^{d}\Big{)}^{2}\tilde{\mathbf{ M}}+j\omega_{z}^{d}\tilde{\mathbf{C}}+\tilde{\mathbf{K}}\Big{)}}{\text{det} \Big{(}-\big{(}\omega_{z}^{d}\big{)}^{2}\widetilde{\mathbf{M}}+j\omega_{z} \widetilde{\mathbf{C}}+\widetilde{\mathbf{K}}\Big{)}} \tag{33}\]  
The proposed approximation of the time-delay holds only if the time delay is very small. The limitation of this method is that the stability of the controlled system is not guaranteed, as the authors themselves state in [PERSON] et al. (2018). Additionally, the method is developed to assign undamped antiresonance. A second formulation based on the full Taylor series (i.e., the series with infinite terms) is proposed, too. Again, stability is not always ensured for some delays due to the secondary roots that are not imposed in the control synthesis.
###### Abstract  
A water distribution network (WDN) is a critical infrastructure that must be maintained, ensuring a proper water supply to widespread customers. A WDN consists of various components, such as pipes, valves, pumps, and tanks, and these elements interact with each other to provide adequate system performance. If the elements fail due to internal or external interruptions, this may adversely impact water service to different degrees depending on the failed elements. To determine an appropriate maintenance priority, the critical elements need to be identified and mapped in the network. To identify and prioritize the critical elements (here, we focus on the pipes only) in the WDN, an element-based simulation approach is proposed, in which all the composing pipes of the WDN are reviewed one at a time. The element-based criticality is measured using several criticality indexes that are newly proposed in this study. The proposed criticality indexes are used to quantify the impacts of element failure to water service degradation. Here, four criticality indexes are developed: supply shortage (\(SS\)), economic value loss (\(EVL\)), pressure decline (\(PD\)), and water age degradation (\(WAD\)). Each of these indexes measures different aspects of the consequences, specifically social, economic, hydraulic, and water quality, respectively. The separate values of the indexes from all pipes in a network are then combined into a singular criticality value for assessment. For demonstration, the proposed approach is applied to four real WDNs to identify and prioritize the critical pipes. The proposed element-based simulation approach can be used to identify the critical components and setup maintenance scheduling of WDNs for preparedness of failure events.  
Keywords:critical element; criticality index; system serviceability; water distribution network +
Footnote †: journal: Applied Soft 2019
## 5 Experimental Results
### System Configuration and Real-Time Performance Analysis  
We conducted fall-detection experiments on a high-performance Linux platform, utilizing an NVIDIA DGX Server Version 4.6.0 equipped with a GNU Linux 4.15.0-121-generic x86 operating system (Dell Inc., Round Rock, TX, USA). The deep learning models, including the CNN for fall detection, leveraged an NVIDIA Tesla V100 SXM3 GPU (Santa Clara, CA, USA) with 32 GB of memory. This setup ensures substantial computational efficiency, which is critical for real-time processing.  
The real-time efficiency of our system is primarily attributed to the CNN classification module, which is optimized to utilize the robust capabilities of the NVIDIA Tesla V100 GPU. This configuration allows for the rapid processing of video frames. Each frame undergoes preprocessing, pose estimation via the OpenPose module, and STFT analysis within approximately 50 ms. The CNN module processes each frame in about 16.7 ms. Consequently, the entire sequence of 50 frames is analyzed in about 833 ms, demonstrating the system's ability to operate effectively under real-time constraints. This processing speed is crucial for scenarios requiring a timely fall-incident response, enabling prompt detection and intervention.  
To highlight our system's real-time capabilities, we compared its frame processing speed with other contemporary methods, as summarized in the Table 8. This comparative analysis underscores our system's suitability for real-time applications by demonstrating its processing efficiency relative to other methods in the field.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Frames per Second (GPU)** \\ \hline MoviNet [64] & 12 \\ CNN with Bi-LSTM [65] & 20 \\ AE CNN + SVM [66] & 24 \\ IP-LSTM [67] & 23.2 \\ STDN [68] & 25 \\ DS-GRU [69] & 25 \\ TOF + LSTM [70] & 30 \\ TSN + TSM [71] & 60 \\ Our proposed method & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative real-time performance analysis.  
Our proposed method processes 60 frames per second on the GPU, providing a competitive processing rate in fall detection. This capability demonstrates our system's efficiency and applicability in scenarios where quick response and low latency are critical, such as monitoring elderly individuals to prevent fall-related injuries. This processing speed supports the real-time capability of our system, positioning it as a reliable solution in fall detection for urgent care scenarios.
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal  
The U-Learning model, supported by connective learning and learning experiences for virtual higher education, is a U-CLX Model framed in a ubiquitous learning ecosystem. The two components, Pedagogical and Technological, have four dimensions, and each component has six elements [2].  
The main purpose of the U-CLX Model is to measure ubiquitous learning (U-Learning) in virtual higher education institutions. The U-CLX Model measures ubiquitous learning at any time, place, medium, or context. Considering that ubiquitous learning is related to people, we concluded that ubiquity is inherent to people and their learning processes.  
Ubiquitous Learning is the basis of the Pedagogical component, and Ubiquitous Computing is the basis of the Technological component; each component has elements by which the level of ubiquitous learning is measured. The relationship between the components and their elements generates the possibility of measuring the level of ubiquitous learning in a virtual higher education context. The measurement of ubiquitous learning by the U-CLX Model has a mathematical basis, found in the volume equation of a four-dimensional hypersphere. The U-CLX Model has four dimensions, two components, and twelve elements in total [2] (see Figure 3).  
U-Learning is the basis of the Pedagogical Component (A). The elements of this component are Learning Paradigms, Domain Knowledge, Physical Settings, Learning Sceneries, Effects, and Academic Levels. All of these elements are related to ubiquitous learning processes.  
Ubiquitous Computing is the basis of the Technological Component (B). The elements of this component are Learning Experience, Technology, Management, Learning Analytics, Functionality, and Devices.  
Below, we give a specific and detailed view of the U-CLX Model, with the dimensions, components, elements, learning processes, and people. The model's components are composed of elements, which are the definitions and conceptual characteristics needed to measure ubiquitous learning. The model is based on U-Learning taxonomy and patterns [1]. Within the ecosystem, people and learning processes will measure the level of ubiquitous learning in the four dimensions of the U-CLX Model (see Figure 4).  
Figure 3: U-CLX Model Overview [2].
## 3 The Proposed Tool
### Qualitative Comparison with the Discussed Works  
An analysis of the related works discussed in Section 2 reveals that each of the reviewed papers faces at least one limitation in applying ML to insider threat detection. Common challenges include lack of instantaneous data usage, lack of real-time analysis, lack of real-time classification, non-interactivity, non-continuity, and absence of adjustability. Table 1 highlights these shortcomings and provides a qualitative comparison between the proposed method and existing approaches in the literature.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Instantaneous Data** & **Real-Time Analysis** & **Real-Time User Classification** & **Non-Interactive** & **Continuous** & **Adjustability** & **Detection Time** & **Classification Time** \\ \hline
[29] & N/D & ✓ & ✗ & ✓ & ✗ & ✗ & ✗ \\ \hline
[30] & ✗ & ✗ & ✗ & ✓ & ✓ & ✗ & ✓ & ✗ \\ \hline
[31] & ✓ & ✓ & ✗ & N/D & ✓ & ✓ & ✗ & ✗ \\ \hline
[3
## 2 Dual-Process Reinterpretation of LRM Behavior
### The Disengagement Pattern as Rational Resource Allocation  
The counterintuitive reduction in reasoning tokens at high complexity represents the most compelling evidence for my reinterpretation. Rather than indicating system failure, this pattern suggests sophisticated resource management analogous to human cognitive disengagement patterns documented extensively in the psychology literature.  
One of the first manifestations of this behavior was documented by [PERSON] and [PERSON] in 1964 [19], who observed that pupil diameter increased with arithmetic problem difficulty but showed limits when tasks became overwhelming. This was followed by [PERSON] and [PERSON] [19], who measured pupil dilation during digit span tasks and discovered that physiological effort markers initially increased with memory load but declined when  
Figure 1: Performance patterns in Large Reasoning Models across problem complexity. Left panel shows accuracy collapse beyond critical thresholds; middle panel demonstrates counterintuitive reduction in reasoning effort (tokens) at high complexity; right panel reveals correct solutions emerging later in reasoning traces for moderate-complexity problems. Adapted (cropped) from [PERSON] et al. (2025) [1] under the CC-BY 4.0 license. For color codes, markers, and methods, see the original paper.  
cognitive capacity was exceeded. Figure 2 depicts these measured pupil dilation patterns as a proxy for cognitive effort allocation during working memory tasks.  
Similar findings have been demonstrated repeatedly by numerous researchers. Later on, [PERSON] synthesized decades of research establishing pupillometry as a reliable measure of processing load with characteristic plateau effects at capacity limits [20]. In 1993, [PERSON] and [PERSON] showed that complex sentence processing triggered greater pupillary responses until comprehension limits were reached [21]. More recently, [PERSON] et al. found that both pupil size and EEG theta power exhibit a plateau followed by decline when exceeding memory limits, suggesting physiological disengagement when cognitive systems are overwhelmed [22].  
The parallel between Figures 1 and 2 is particularly striking. Both demonstrate the characteristic inverted-U relationship that defines bounded rational systems: effort markers initially scale with demand until capacity thresholds are reached, then decline as systems adaptively withdraw resources from intractable challenges. In [PERSON] and [PERSON]'s study, pupil dilation peaks around the 6-7 digit range (the boundary of typical working memory capacity) then declines for higher loads. Similarly, LRM thinking tokens increase with problem complexity until reaching model-specific thresholds (around N = 7-8 for the Tower of Hanoi), then decrease despite maintained task demands.  
This convergence across six decades of research, from human physiological responses to modern AI computational patterns, provides strong empirical support for interpreting LRM behavior through established cognitive frameworks rather than dismissing it as illusion.
## 3 Results and Discussion
### Physical Models
The laminar model was closest to the experimental data for \(Re=1000\) and \(2000\), followed by the LES and \(k\)-\(\varepsilon\). Slightly different results were obtained at the highest \(Re\), where the LES outperformed the laminar model, but still overestimated the velocity by 5%. This results in relatively high MAPE values of 6% for LES a laminar simulation. A huge disparity is found in the \(SCA\) results, where the value of the experimental \(SCA\) is more than 20% larger than the predicted one. The experimental \(SCA\) discussed here is measured directly after discharge at the apex angle of the cone, which covers the liquid sheet, while the \(SCA\) from the CFD is based on the maximum in the liquid fraction inside the discharge liquid sheet. Nevertheless, both approaches should return very similar values of \(SCA\). The \(SCA\) grows significantly in the experiment with \(Re\) as \(SCA\propto Re^{0.12}\), respectively with \(p_{l}\) as \(SCA\propto p_{l}^{0.06}\), while it increases in transient laminar simulation as \(SCA\propto Re^{0.06}\) or \(SCA\propto p_{l}^{0.03}\) and it is constant for the LES simulation. The reason for these disparities is not clearly known, but may be partially related to the flow of the surrounding air, which may slightly affect the liquid sheet formation as described in [35] and was not captured in CFD due to a relatively small outflow area. Furthermore, manufacturing inaccuracies and different measuring methods may introduce some errors. A widely used empirical correlation published by [PERSON] and [PERSON] [36] predicted a much higher influence of the inlet pressure as \(SCA\propto p^{0.11}\). However, this correlation was derived for liquids with higher viscosity and well captured the trends using higher viscous liquid in our previous study [25] using almost identical geometry as used here. Similarly, the experimental values of \(SCA\) were higher than expected. The viscous liquids typically exhibit a larger influence of \(p_{l}\) on \(SCA\) since [PERSON] et al. [33] found \(SCA\propto p^{0.39}\) for heating oil. The values of \(SCA\) based on the [PERSON] predictions are 46, 54 and 63\({}^{\circ}\) for \(Re=1000\), 2000 and 4000, respectively. These values are well below the measured values but are much closer to the CFD prediction. Since the CFD correctly predicts other parameters of the internal flow, the differences in \(SCA\) might be linked to a different measurement technique, complex airflow near the liquid sheet, or wettability of the atomizer PMMA body.