esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Human Location Using Correlated Movements
###### Abstract  
This paper aims at estimating the current location, or predicting the next location, of a person when the recent location sequence of that person is unknown. Inspired by the fact that the behavior of an individual is greatly related to other people, a two-phase framework is proposed, which first finds persons who have highly correlated movements with a person-of-interest, then estimates the person's location based on the position information for selected persons. For the first phase, we propose two methods: community interaction similarity-based (CISB) and behavioral similarity-based (BSB). The CISB method finds persons who have similar encounters with other members in the entire community. In the BSB method, members are selected if they show similar behavioral patterns with a given person, even though there are no direct encounters or evident co-locations between them. For the second phase, a neural network is considered in order to develop the prediction model based on the selected members. Evaluation results show that the proposed prediction model under the BSB scheme outperforms other methods, achieving top-1 accuracy of 71.13% and 69.36% for estimations of current and next locations, respectively, with the MIT dataset and 92.31% and 92.03% in case of the Dartmouth dataset.  
Keywords:mobility prediction; behavioral pattern; tempo-spatial information; cellular network trace +
Footnote †: journal:
## 5 Discussion
In contrast, EfficientViT achieved comparable or better performance without the need for segmentation, and its fast inference speed further supports its suitability for real-time deployment in production environments. Furthermore, the elimination of segmentation dependency offers significant operational advantages. In real-world settings such as slaughterhouses, obtaining accurate segmentation masks in real time can be challenging due to inconsistent lighting, background noise, and varying carcass positions. By directly operating on raw RGB images, the proposed one-stage model avoids the need for costly and error-prone preprocessing, reducing system complexity and potential failure points. This design choice enhances the model's robustness and deployability, particularly in scenarios where high-throughput and low-latency processing are essential. Although the performance gap between EfficientViT and other CNN models may appear numerically marginal (e.g., differences in F1 score of less than 0.02), the consistency of its superiority across different scenarios--including varying loin cross-section ratios--demonstrates its robustness and practical reliability. In industrial contexts, where premium grades such as 1++ are sold at the highest market prices, even a small improvement in classification accuracy can lead to meaningful economic benefits and enhance operational decisions. Therefore, these \"slightly better performances\" translate into substantial value when deployed in high-stakes production environments. Nonetheless, this study is based on a curated dataset from AI Hub, and further validation is needed to assess generalizability under more variable industrial conditions such as differing lighting, background clutter, or carcass positioning. Additionally, while the attention mechanism offers improved global reasoning, its internal decision-making process still poses challenges in terms of interpretability. Future work should explore explainable AI techniques tailored to transformer-based models. Further directions include comparisons with other vision transformers, incorporating data augmentation strategies, and extending the framework to other domains such as different meat species or medical imaging. The balance of efficiency and expressiveness achieved by EfficientViT makes it a strong candidate for edge computing and mobile applications in practical scenarios. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Method** & **Mean (\%)** & **Std (\%)** & **Median (\%)** & **Q1 (\%)** & **Q3 (\%)** \\ \hline EfficientViT & Attention Map & 49.78 & 14.56 & 49.76 & 39.42 & 59.91 \\ DenseNet-121 & Grad-CAM & 13.79 & 4.90 & 13.19 & 10.16 & 16.62 \\ ResNeXt-50 & Grad-CAM & 10.14 & 4.11 & 9.56 & 7.04 & 12.59 \\ VGG-16 & Grad-CAM & 3.35 & 3.44 & 2.25 & 0.98 & 4.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical comparison of high-activation regions (\(\geq\)0.5) across visualization methods. Grad-CAM is used for CNNs, and attention map is used for EfficientViT.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 4 Analysis
### LEO-Enhanced PPP and PPP-AR
#### 4.2.2 LEO-Enhanced PPP and PPP-AR in Different Latitudes  
Considering that the enhancement of performance at different locations may vary, the 258 stations were divided into low-, middle-, and high-latitude zones for analysis. Figure 7a shows the average convergence time of PPP enhanced using the same LEO constellation at different latitudes. The average convergence time of the L constellation-enhanced PPP in low, middle, and high latitudes was 14.1, 7.8, and 3.3 min, respectively. The higher the latitude, the faster the convergence speed. This was because the polar orbital constellation was mostly at higher latitudes, so it provided more visible satellites, as shown in Figure 1a.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Constellations**} & \multicolumn{4}{c}{**Float Solution/cm**} & \multicolumn{4}{c}{**Fixed Solution/cm**} \\ \cline{2-7}  & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline G & 0.3560 & 0.2523 & 0.8695 & 0.  
The average convergence time of the M constellation in the middle-latitude region was 4.6 min, which was slightly faster than the 5.4 min of the low-latitude region and the 7.0 min of the high-latitude region. This was because the M constellation provided the largest number of visible satellites in midlatitudes, as shown in Figure 1b. The average convergence times of the L+M constellation in low, middle, and high latitudes were 2.8, 2.0, and 1.9 min, respectively. The convergence speed became faster with increasing latitude. Compared with the L and M constellations, the L+M constellation presented the least significant latitude difference in the acceleration of convergence time. Figure 7b shows the average TTFF of the PPP-AR enhanced using the same LEO constellation at different latitudes. Unlike ambiguity-float solutions, the TTFF of ambiguity-fixed solution PPP enhanced using the three LEO constellations showed the same latitude difference as that of the non-LEO constellation-enhanced PPP-AR. This was because the degree of enhancement of fixed-solution PPP enhanced using LEO was weaker than that of the float solution. After the introduction of LEO satellites, it did not obviously follow that the more LEO satellites there were, the shorter the TTFF would be, as shown in Section 4.2.1.  
Figure 8a,b compares the convergence performance of the PPP and the PPP-AR enhanced with different LEO constellations at the same latitude. For both the float and the fixed solutions, the L+M mixed constellation was superior to the nonmixed constellation in every latitude region. At low and middle latitudes, M was superior to L. The advantages of the L constellation were shown in the high-latitude region; compared with the float solution, the fixed-solution PPP in any latitude region had no significant enhancement difference with different LEO constellations.  
Figure 7: Comparison of convergence performance of PPP and PPP-AR enhanced using the same LEO constellation in different latitudes: (**a**) PPP; (**b**) PPP-AR.
# Secure Localization in the Presence of Colluders in WSNs
## 7 Conclusions  
The deployment of a wireless sensor network (WSN) under hostile environments may lead to vulnerable scenarios. In particular, many distinct attacks corrupt the estimation of positions, which, in turn, can have a significant negative impact on the behaviour of software relying on such estimations. Consequently, much work has focused on how to correctly compute the latter in the presence of different kinds of attacks. However, how to deal with colluders has not received much attention. To remedy this, here we have focussed on three kinds of such attackers that target the localization process. Most importantly, these attackers are taken to be able to alter the information transmitted between the nodes of the WSN. We described at length a decentralized algorithm that established the coordinates of location-unknown nodes in the presence of such adversaries. Our solution also supports the detection of colluders within the communication range of this sensor. Through extensive simulations we validated two different environments: uniform deployments and random distribution of nodes. With regard to previous work, our new algorithm achieves a higher performance in terms of collusion detection, and at a reasonable cost increase.  
The authors gratefully acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grants No. RGPIN-2015-05390.  
Figure 8: Average number of neighbours per location-unknown node and its 95% confidence intervals.  
Figure 9: Average cost per algorithm and its 95% confidence intervals. (**a**) Uniform deployment; (**b**) Random deployment.  
**Author Contributions:** [PERSON] is the main contributor to this research having created both the algorithm presented here and the original draft of this paper. [PERSON] and [PERSON] have contributed to the editing and improvement of the latter. [PERSON] and [PERSON] have developed and written up all the material pertaining to the simulations.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Molecularly Imprinted Polymer for Selective Extraction of Phenolic Acids from Human Urine
## 3 Results and Discussion
### Validation of the MISPE Procedure  
The MISPE-LC-DAD-MS method developed was validated in terms of linearity, reproducibility, and detection and quantification limits (LOD and LQO), and average recoveries for 4-HPA under the optimized experimental conditions. Those were determined using spiked blank urine samples, prepared as previously has been described. Calibration curves were obtained using ten different concentrations (range from 0.25-40 mg/L). The reproducibility of method, which was evaluated with the relative standard deviation parameter (RSD) in term of reproducibility (inter-day precision and intra-day precision) at three levels of fortification: 2 mg/L, 20 mg/L, and 40 mg/L. The intra-day precision was demonstrated at these three levels and RSD as well. Results are summarized in Table 2. Finally, the recovery values were more than 94% (RSD 1.2%) over the range of concentrations tested.
# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Future Prospects of AI in Agricultural Structures and Beyond
The practical benefits associated with optical and thermal cameras include globally established platforms for fire monitoring, including UraFire, FireVu, FireHawk, ForestWatch, and EYEfi in Europe, North America, and Africa, that integrate light detection and ranging (LIDAR) systems, IR spectrometers, infrared (IR) thermal imaging cameras, and video cameras for heat flux, smoke gases, flame, and smoke particle detection [31]. Even though large farms might benefit from thermal and optical  
Figure 5: Impact of temperature on the accuracy of AI-WSN-based systems for fire prevention/monitoring [32]. imaging provided by independent service providers, the systems are not immune to errors associated with human activities near the target locations and atmospheric turbulence (dust particles, shadows, and clouds, light reflection). This is exacerbated by the detection limits (1.0-10.0 \(\upmu\)m) [95]. Despite the potential limiting factors, the case for optical and thermal sensors is reinforced by the fact that optical detectors have proven more useful compared to ionization chamber smoke detectors in real-life settings due to their higher sensitivity to smoldering fires and tolerance to ambient conditions [95]. The utility of optical and thermal cameras for fire detection could be augmented by AI networks such as DBN, R-LSTM, PNN, GA, and ANFIS, embedded with naive Bayes classifier [30], Haar cascade classifier [11], PCA and SVM classifiers [31], WSNs [32], and probabilistic neural network classifiers [33]. However, this potential requires further investigation. It must be noted that apart from the high accuracy and resolution (but quite expensive) cameras, reduced capabilities modules, such as the thermal arrays described in [96], pose quite a challenging option, as they can be exploited by several AI algorithms and thus seem to widen the current application and cost options.
## 3 Laboratory Test Results
### Elastic Properties of Hardfill  
In designing a hardfill dam, in general, the strength in the elastic region is taken as the strength of the hardfill from the stress-strain behavior, and the hardfill dam is assumed to be a structure that behaves like an elastic body. For all samples subjected to the uniaxial compressive strength test, the maximum compressive strength and elastic modulus in the elastic region were calculated from the stress-strain relationship obtained from the uniaxial compressive strength test. For the range of hardfill mixes investigated in this study (i.e., mixes in the range of unit cement content of 80 kg/m\({}^{3}\) and unit water contents of 70-100 kg/m\({}^{3}\) for aggregates with upper- and lower-limit PSDs), the relationship between the hardfill's uniaxial compressive strength in the elastic range and elastic modulus is shown in Figure 8. For all mixing conditions, the hardfill's elastic modulus is in the range of approximately 220 to 609 times the uniaxial compressive strength (i.e., 220 q-609 q, where q is the uniaxial compressive strength). The elastic modulus of the aggregate mix with the upper-limit PSD is approximately 433 times (i.e., 433 q) the uniaxial compressive strength on average, and the elastic modulus of the aggregate mix with lower-limit PSD is approximately 314 times (i.e., 314 q) on average. Consequently, the elastic modulus of the hardfill of the aggregate mix with the upper-limit PSD is rather large. As shown in Figure 7, the peak value of the stress-strain relationship in the uniaxial compressive strength test (that is, the uniaxial compressive strength measured at the time of sample failure) shows a smaller range in the hardfill of the aggregate mix with the upper-limit PSD than that in the hardfill of the aggregate mix with the lower-limit PSD. However, the above-mentioned elastic modulus results indicate that, on average, the hardfill's stiffness, i.e., the elastic modulus (slope of the stress-strain curve) in the initial strain (elastic deformation) section is slightly larger in the hardfill of the aggregate mix with the upper-limit PSD than that in the hardfill of the aggregate mix with the lower-limit PSD.
# Prioritization of Drugs for Fungal Keratitis Eye Infections: An In-Silico Analysis
## 3 Materials and Methods  
### Homology Modeling  
The DNA dependent RNA polymerase (RNAP) protein sequences from _A. flavus_ (ACL99632.1) and _A. fumigatus_ (ACL99633.1) were retrieved from the NCBI database [32]. The sequences were subjected to NCBI-BLASTP against the PDB database [33], and the 3D structure of RNA Polymerase II was obtained from _Schizosaccharomyces pombe_ (PDBID:3h0g_Bchain) [34] as a potential template. By using the Modeller 9v9 [35], the 3D structures of RNAP from _A. flavus_ and _A. fumigatus_ were built as homologous models with reference to the potential template. For each target sequences, a total of five 3D models were built from the starting structure of the templates by satisfying the spatial restraints through random generation [36]. From the generated models, the best model was selected by considering the least root mean square deviation (RMSD) value in comparison with the template structure. For the selected best model, the energy was minimized through 20 steps of the steepest descent and a conjugate gradient by using the GROMOS [37] of the Swiss-PDB viewer [38]. Finally, an energy-minimized model was used for further analysis.
###### Abstract  
Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the application of FRA technique to induction motors has not yet been fully explored. This paper presents analogical experimental studies for the application of FRA on power transformers and induction motors. For a consistent analogy, the FRA technique has been employed to detect short and open circuit turns in both appliances, which helps explore a wider scope of the FRA applications on rotating machines. In this regard, experimental FRA measurements are performed on an 11/0.415 kV, 500 kVA, three-phase distribution transformer and a 5.5 HP three-phase induction motor. Several short and open circuit faults are staged on the windings of both tested equipment and the FRA signature is recorded and compared with the reference signature at no fault. To quantify the impact of faults on the FRA signature, several statistical indicators are used and threshold limits for these indicators are proposed to automate the interpretation process. Results reveal a good correlation between the FRA signatures of induction motors and power transformers that attests to the feasibility of using FRA technique to detect various faults within large rotating machines. keywords: frequency response analysis; power transformer; induction motors; fault diagnosis; statistical indicators +
Footnote †: journal: Applied Sciences  
Several online and offline diagnostic techniques have been developed over the years to detect emerging faults within power transformers and IMs. Among these techniques, partial discharge (PD) measurement has been used to assess the insulation condition within transformers and IMs [4]. Other techniques to detect internal faults within transformers and IMs based on current-voltage signatures have been presented in the literatures [5,6]. Frequency response analysis (FRA) is an accepted commercial technology used to detect internal mechanical and electrical faults within power transformers [7]. As a matured technology for power transformers condition monitoring and fault diagnosis, several studies can be found in the literature to enhance its fault detection of which some are listed in Table 3. While FRA technology is not fully explored yet for IMs, a few relevant studies can be found in the literature, as summarized in Table 4. \begin{table}
\begin{tabular}{l c} \hline
**Failure Mode** & **Occurrence (\%)** \\ \hline Tap changer & 40 \\ Main Windings & 30 \\ Bushing & 14 \\ Tank & 6 \\ Core & 5 \\ Auxiliary & 5 \\ \hline \end{tabular}
\end{table} 
Table 1: Power transformer failure modes according to CIGRE international survey [1]. \begin{table}
\begin{tabular}{l c c} \hline
**Failure Mode** & **Occurrence (\%), EPRI** & **Occurrence (\%), IEEE** \\ \hline Bearings & 41 & 44 \\ Stator winding & 36 & 26 \\ Rotor winding & 9 & 8 \\ Others & 14 & 22 \\ \hline \end{tabular}
\end{table} 
Table 2: IM failures modes According to EPRI and IEEE surveys.
## 4 Computational Validation
### Results in the Four-Bus Grid  
For this test feeder, we considered two simulation cases, i.e., the neutral wire solidly grounded in all of the buses and the neutral wire grounded only at the substation bus. Table 4 presents the voltage in the positive, neutral, and negative poles. Both cases were solved with the proposed hyperbolic power flow approximation; Table 5 presents the solutions obtained with the successive approximation power flow method.  
The numerical results in Table 5 show that: (i) The successive approximation power flow approach and the hyperbolic power flow proposal reached the same numerical voltages with a tolerance assigned to \(1\times 10^{-10}\) and the same power losses for both simulation cases; however, the number of iterations of the hyperbolic approximation method was three for the solidly grounded case and eight for the non-solidly grounded scenario, while the successive approximation power flow method took seven iterations for the solidly grounded case and eight iterations for the non-grounded case. This behavior was attributed to the linear convergence of the successive approximation power flow method, while the hyperbolic approximation exhibited quadratic convergence, as demonstrated in [16]. (ii) As expected, the non-grounded neutral wire presented high power losses compared to those in the solidly grounded case. This situation occurred due to the currents that flowed through the neutral wire because of the presence of monopolar constant-power DC terminals. (iii) The voltage regulation for the solidly grounded neutral wire case was 3.6142%, and in the non-grounded scenario, it was 3.6210%. In addition, it was noted that, due to the load imbalances in the monopolar loads, the positive and negative voltage magnitudes per node differed among them.
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Covariance Matrix Regularization  
Although the spatiotemporal beamformer, in theory, achieves optimal separation between target and non-target classes, in analogy to linear discriminant analysis [27], it does not always perform well on unseen data. The main challenge is to find a good estimator for the inverse covariance matrix \(C^{-1}\) since the real underlying covariance matrix generating the data is, in principle, unknown.
## 1 Introduction
This paper studies the low-latitude ionospheric variations and coupling mechanisms during the multiphase geomagnetic storm in February 2014. Ref. [43] studied the multiple signatures of this particular storm using data from Japan and Egypt geomagnetic stationsand from the Van Allen Prove satellites (Applied Physics Laboratory, Laurel, MS, USA). It was shown that Pi2 pulsations (period range from 40 to 150 s) may have originated from the ionospheric current systems rather than the cavity resonance mode. Their work shows the magnetospheric variability during this complex multiphase storm but without further study on the coupled ionospheric variability. A clear understanding of the TEC variations during these kinds of events is very important for ionospheric modeling and applications, and very few studies on ionospheric variability during this multiphase storm, or similar events, can be found in the literature. In this paper, we present a study of the ionosphere responses to this complex storm using multiple GNSS, ground magnetometers, and solar wind data. There is further identification of possible discrepancies when compared to a typical isolated storm. In Section 2, there is a brief introduction to the materials and methods used in this study. Then, in Section 3, the results are presented with detailed descriptions and highlights. In Section 4, some explanations and discussions are given, and, finally, a summary of conclusions is provided in Section 5.