esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Mathematical Models
### Lightning Electromagnetic Pulse Current Models  
Heidler Current Model  
Heidler's function is expressed in Equation (1) [21]:  
\[\mathrm{i}(0,t)=\frac{\mathrm{I}_{0}}{\exp\left[-\left(\tau_{1}/\tau_{2} \right)\cdot\left(\mathrm{n}\tau_{2}/\tau_{1}\right)^{\frac{1}{n}}\right]}\cdot \frac{\left(\frac{t}{\tau_{1}}\right)}{1+\left(\frac{t}{\tau_{2}}\right)}\cdot \mathrm{exp}\left(-\frac{t}{\tau_{2}}\right)\cdot\mathrm{u}(t) \tag{1}\]  
The variables are as follows:  
\(\tau_{1}\): time constant of the rising current (s).  
\(\tau_{2}\): rot steady of the waveform of the rising current (s).  
\(\mathrm{n}\): specified steepness factor (between 2 and 10).  
\(\mathrm{I}_{0}\): sufficiency esteem of the channel base current (A).  
\(\mathrm{u}(t)\): unit step function.  
t: time (s).  
The LEMP form obtained by the Heidler current model is depicted in Figure 3a, with a detailed part denoted in Figure 3b. Figure 3a,b are derived mathematically [22]. Subsequent strokes can be modeled by varying the values of \(\mathrm{I}_{0}\), \(\tau_{1}\) and \(\tau_{2}\) in the Heidler model. Two  
Figure 2: Indirect drop of an LEMP on an exemplary ETL (d: the distance of the pulse from the line, h: the height of the transmission line, \(\mathrm{z}_{1,2}\): the characteristic impedance of the transmission line pole).  
Heidler capacitors with specific parameters can be included to approximate the required current waveform. The advantage of the Heidler function is its ability to model a real lightning current more realistically because it closely approximates the characteristics of a real lightning strike.
## 5 Implementation and Evaluation
### Reputation Scoring Patterns  
We examine the patterns of the reputation scores of the topic by using the results of the quality assessment and the drifting scores obtained by introducing noise and delays in \(\delta\) and analyze how they can impact the reputation score of the topic. The reputation score of the topic becomes closer to 1.0 under ideal conditions where there is no delay and the noise in the data is very low (around 1%). In the worst-case scenario, when there is very high noise in the data, such as 20%, and the maximum delay is double the \(\delta\), the reputation score drops to 0. Figure 10 shows the patterns of reputation scores under different noise and delay conditions.
## 4 Investigation of Proposed OAM-OSC System at Different Input Parameters
### A Detailed Comparison of Proposed OAM-OSC System with Reported OSC Systems  
In this section, the performance of the proposed OAM-OSC system is compared with the different reported OSC systems in terms of data rate, channels, capacity, OSC link length, modulation used, multiplexing employed, and performance, as shown in Table 2. It is observed that the highest capacity is accomplished in the proposed work as compared to [41; 42; 44; 61; 62; 63]. Moreover, the use of DQPSK [41], 4-QAM/PSK-OFDM [43], PDM-QPSK [44], SP-QAM [59], PDM-4-QAM [37], PDM-16-QAM [58], PDM-64-QAM [59], and PDM-128-QAM [62] have been reported in these researches. However, the proposed system has employed high spectral efficiency (eight bits per symbol) based PDM-256-QAM in OSC-OAM system. As far as multiplexing is concerned, MDM was used by [41; 43; 44], but due to the consecutive or lesser differences in LG/HG mode profiles, more deterioration and limited reach were obtained. On the other side, the proposed OAM-enabled OSC system with PDM-256-QAM offered the highest capacity, ultra-long reach, and high cost-effectiveness using single wavelength thrice for data modulation with OAM beams.
# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 5 Results and Discussion
### Readability Prediction at the Word Level
#### 5.1.1 Features' Contribution  
Regarding our second question about the features that contribute the most to Arabic readability prediction, we used the Correlation Ranking Filter in WEKA. We ranked the features in the first round based on their correlation with the predicted value (OSMAN score) as shown in Figure 4. (Is_Faseeh) was shown to have more correlation with the predicted OSMAN score than the number of syllables (No_of_Syllables). In the second round, it was found that some eye features, which are IA_REVISIT_TRIAL_%, IA_FIRST_FIXATION_DURATION, and IA_RUN_COUNT were ranked as the three top correlated attributes, followed by Is_Faseeh as a linguistic feature.  
Based on this, we can assume that participants tend to fixate on a word and revisit previous parts of the text when facing difficult words. The rest of the eye measures (IA_DWELL_TIME, IA_DWELL_TIME_%, IA_FIXATION_COUNT, IA_FIX_COUNT_%, and IA_VISITED_TRIAL_%) reflect the late processing stage of readers, which justify their ranking below (IA_REVISIT_TRIAL_%, IA_FIRST_FIXATION_DURATION, and IA_RUN_COUNT). We also noticed that although used vocabularies affect readability significantly [80], No_of_Syllables was always the lowest-ranked attribute in both rounds, indicating that it does not affect the predicted score. This might be because, as mentioned in [81], \"an Arabic word could contain more than three syllables and still be considered a non-complex word\" [29].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reg. Model** & **Using Gaze Data** & **CC** & **MAE** & **RMSE** & **RAE (\%)** & **RRSE (\%)** \\ \hline \multirow{3}{*}{LR} & x & 0.96 & 11.98 & 13.83 & 28.86 & 26.56 \\  & ✓ & 0.98 & 9.14 & 11.50 & 21.99 & 22.05 \\ \multirow{3}{*}{SMO-reg} & x & 0.96 & 11.47 & 14.50 & 27.62 & 27.86 \\  & ✓ & 0.98 & 8.88 & 11.67 & 21.36 & 22.38 \\ \multirow{3}{*}{MLP} & x & 0.93 & 15.09 & 19.48 & 36.33 & 37.43 \\  & ✓ & 0.95 & 12.43 & 15.87 & 29.90 & 30.42 \\ \multirow{3}{*}{M5P Tree} & x & 0.96 & 12.44 & 14.70 & 29.97 & 28.23 \\  & ✓ & 0.97 & 9.78 & 12.73 & 23.54 & 24.40 \\ \multirow{3}{*}{REP Tree} & x & 0.95 & 12.19 & 16.76 & 29.31 & 32.13 \\  & ✓ & 0.91 & 16.51 & 21.69 & 39.83 & 41.68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of using different regression techniques with and without eye-tracking data.  
These results indicate that eye-tracking could be a valuable technique for monitoring the cognitive activities associated with reading and explaining the correlation between cognitive processes and eye movement.
## 5 Experimental Results

Table 17: Time consumption during an iteration of inference for each algorithm based on Coordinate-A method (unit of time: milliseconds, batch size = 10, measurement by RTX 2080-Ti GPU. E, F, and K denote English, freestyle, and Korean patterns, respectively). Figure 13: Training and inference time consumption for each length of each drawing pattern. Bubble size corresponds to the length of sequence. E, F, and K denote English, freestyle, and Korean patterns, respectively. The average FAR and FRR values obtained by the proposed TCN-based DDP model for the used 13 patterns are listed in Table 18. The average FAR and FRR values of this model are in the ranges of 0.67-2.38% and 0.50-2.25%, respectively, and the total average FAR and FRR values are 1.41 and 1.23%, respectively. Therefore, this model exhibits fairly good user-authentication performance, even if all potential intruders know the authenticated user's drawing pattern. Therefore, it can prove to be robust to threat scenarios, such as SSA and SA. In conclusion, the proposed TCN-based DDP model provides statistically superior authentication performance and higher computational efficiency than the RNN-based model in most cases. Moreover, the time consumption ratios from RNN to TCN for training and inference increase linearly with the sequence length. Therefore, the TCN-based DDP model is more suitable than the RNN-based DDP model for smartphone user authentication using drawing patterns. From the perspective of computational time for real-world implementation, the proposed model requires a training time of less than 5 s and inference time of less than 9 ms, which is acceptable in a real-time service scenario.
## 3 Results  
### Antimicrobial Susceptibility Testing  
The antimicrobial susceptibility testing results of the _Salmonella_ strains (five serotypes) are shown in Figure 1. According to the results reported in Figure 1, 50.5% were resistant to ampicillin, 36.2% to carbenicillin, 31.8% to amikacin, and 19.7% to ciprofloxacin. Furthermore, there was a statistically significant difference in resistance between ampicillin and amikacin, and carbenicillin and ciprofloxacin (\(p<0.05\)). On the basis of the obtained antibiotic resistance profiles, 49.4% of the _Salmonella_ serotypes were multidrug resistant (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strains** & **No. (\%) of Isolates with Resistance** & **Resistance Profile** \\ \hline \multirow{4}{*}{_Salmonella_ Rubislaw (\(n=17\))} & 4 (4.3) & AM-CB \\  & 1 (1) & AM-CB-GE-NF \\  & 1 (1) & AK-CF-CL-NF \\ \hline \multirow{4}{*}{_Salmonella_ Newport (\(n=17\))} & 3 (3.2) & AM-CB \\  & 1 (1) & AK-AM \\  & 1 (1) & AK-AM-CB \\  & 2 (2) & AK-CFX-CPF-CL \\  & 1 (1) & AK-CF-CFX-CPF-CL \\ \hline \multirow{4}{*}{_Salmonella_ Oranienburg (\(n=3\))} & 1 (1) & AM-CB \\  & 1 (1) & AK-AM-CB \\ \hline \multirow{4}{*}{_Salmonella_ Infantis (\(n=40\))} & 7 (7) & AM-CB \\  & 2 (2) & AK-CPF \\  & 1 (1) & AK-CFX \\  & 2 (2) & AK-AM-CPF \\  & 1 (1) & AK-CPF-CL \\  & 1 (1) & AK-CFX-CPF \\  & 1 (1) & AK-AM-CB \\  & 1 (1) & AK-CB-FFX-CPF \\  & 1 (1) & AM-CB-CF-NF \\  & 1 (1) & AK-CFX-CPF-CL \\  & 1 (1) & AK-AM-CFX-CPF-CL \\ \hline \multirow{4}{*}{_Salmonella_ Thyphimuriumurium (\(n=14\))} & 4 (4.3) & AM-CB \\  & 3 (3.2) & AK-AM-CPF \\ \cline{1-1}  & 1 (1) & AK-CFX-CPF \\ \cline{1-1}  & 1 (1) & AK-AM-CB \\ \hline \hline \end{tabular}  
* AMK, amikacin; AM, ampicillin; CB, carbenicillin; CE, cephalothin; CFX, cefotaxime; CPF, ciprofloxacin; CL, chloramphenicolicol; GE, gentamicin; NET, netilmicin; NF, nitrofurantoin; NOF, norfloxacin; STX, trimethoprimrim-sulfamethoxazole.  
\end{table}
Table 1: Characteristics of multidrug resistance of serotypes of _Salmonella_ from fresh fruits.  
Figure 1: Antimicrobial resistance pattern of _Salmonella_ strains (five serotypes) to different antibiotics. AMK, amikacin; AM, ampicillin; CB, carbenicillin; CF, cephalothin; CFX, cefotaxime; CPF, ciprofloxacin; CL, chloramphenicolicol; GE, gentamicin; NET, netilmicin; NF, nitrofurantoin; NOF, norfloxacin; STX, trimethoprimrim-sulfamethoxazole. * Statistical significance at the \(p<0.05\) level.
# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 6 Results and Discussion
This type of gesture typically involves smaller amplitude displacements, which can overlap with natural arm sway during normal activities, thereby reducing classification confidence. These findings underscore the importance of selecting gesture types with high signal-to-noise ratios and further motivate the development of filtering and stabilization techniques to enhance the reliability of horizontal gesture detection in wearable control systems. To enable multi-robot and multi-operator deployment, the system architecture must incorporate user identification, session management, and conflict arbitration mechanisms. Each smartwatch can be uniquely paired with a robot or assigned dynamically based on task context using authentication tokens or device IDs. Large-scale goals, such as collaborative manipulation or distributed inspection tasks, require a centralized coordination platform. This can be achieved using middleware such as ROS or IoT platforms like ThingWorx, which allow the dynamic routing of gesture commands, task scheduling, and state synchronization across devices. Scalability is challenged by latency bottlenecks, wireless interference, and gesture ambiguity when multiple users operate simultaneously. These can be mitigated through frequency isolation (e.g., using BLE channel management), gesture personalization (per-user gesture profiles), and priority-based task allocation. Future developments may also explore cloud edge hybrid models, where gesture recognition runs locally on the smartwatch for a fast response, while high-level orchestration is handled in the cloud. These architectural upgrades will be essential for real-world industrial use cases involving complex robot networks and heterogeneous human teams. While the system shows strong potential for alignment with Industry 4.0 and 5.0 paradigms--particularly in terms of human-centric interaction and digital connectivity--its current validation is limited to laboratory conditions. Full industrial readiness would require further testing under real-world conditions involving ambient noise, electromagnetic interference, and multiple simultaneous users. These aspects are planned for future investigations to assess system scalability and robustness in operational environments. The current study was designed around a fixed set of predefined gestures to ensure controlled evaluation conditions and consistent baseline performance. However, such an approach does not fully capture the variability present in real-world scenarios. Gesture execution can be influenced by factors such as user fatigue, stress, or individual motor patterns. To address these challenges, future developments will focus on adaptive gesture recognition systems capable of continuous learning and real-time recalibration. Expanding the training dataset across diverse user profiles and usage contexts will also be essential to improve robustness and long-term usability.
## 1 Introduction
Our approach consists of developing an ontology-driven adaptive system for DVIs that leverages a driver profile ontology to improve safety in interactions between humans and vehicles. The Onto-CMS allows dynamic adaptation to individual preferences while preserving the integrity of standardised safety elements. Therefore, the main contribution of our work consists of reducing the likelihood of accidents and addressing liability issues. The Onto-CMS focuses on personalising the interfaces of vehicles for a driver, making driving more intuitive while adhering to standardised safety features. The system monitors driver behaviour in real-time and adjusts interface elements, ranging from design features to alerts, to match individual preferences. Meanwhile, the system ensures that critical functions, such as emergency interface elements, remain operational and unaltered, thereby prioritising safety. For instance, the system is designed to prevent any modifications that could potentially compromise essential safety features. This is achieved through a rigorous examination of user inputs, ensuring no compromise of the system's critical functionality. The other key component of the Onto-CMS is the ontology of the driver's profile, which acts as an extended data repository, holding the behavioural tendencies and needs associated with each driver. This information enables real-time adjustments, even in high-pressure or emergency situations. Therefore, the Onto-CMS integrates personalisation with safety, offering a friendly and trustworthy interface. The development of this system is, thus far, informed by three key questions. * How could an ontology of driver profiles be used to personalise interfaces without compromising the issues of safety and consistency? * What aspects of the driver profile ontology must be globally standardised to ensure safety, while allowing flexibility for non-essential customisation? * How would customised interfaces enhance performance and response times of drivers in life-critical situations? The paper is organised as follows. Section 2 introduces the subject of HAVs and DVIs. Section 3 presents a review of the existing literature of current trends, methodologies and gaps in the field and discusses the challenges of creating safe and efficient interactions for all stakeholders. Section 4 outlines the methodology employed, focusing on the architecture of the Onto-CMS and its modular workflow (Section 4.1). It also details the development of the driver profile ontology (Section 4.2), the creation of a knowledge graph (KG) based on this ontology (Section 4.3), and the integration of the system with driver preferences to deliver real-time customisation (Section 4.4). Section 4.5 describes the implementation phase and focuses on the practical application of the Onto-CMS for driver preference management. Section 5 presents the results obtained, evaluating the effectiveness of the Onto-CMS in improving driver experience, interface design and safety. Finally, Section 6 discusses the implications of these results.
# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 4 Two-Cycle Methodology to Build a Semantic Model
### The Context Cycle  
BFO is a formal ontology framework developed by [PERSON] and his associates [44]. BFO comprises two disjoint ontologies: continuant and occurrent ontology. The continuant ontology includes continuant entities, such as three-dimensional enduring objects; occurrent ontology covers entities with four dimensions that unfold during a time interval (e.g., life, smiling, sending an email). Based on the BFO, we established ontology design principles as follows:  
* Single nouns should be used (except \"data\").
* Acronyms should be avoided.
* Univocity of terms and relational expression should be ensured.
* General should be distinguished from particular.
* All non-root terms should be provided with their definitions.
* Key features should be used to define terms and avoid circularity.
* The most generic terms of the domain should be defined first.
* Simpler terms should be used in comparison to the term being defined (to ensure intelligibility).
* Creating terms for universals through logical combinations should be avoided.
* The ontology should be structured around an \"is_a\" hierarchy, and completeness of \"is_a\" must be ensured.
* Single inheritance should be ensured.  
The authors held biweekly meetings to address feedback and comments from domain experts and business partners. In these meetings, all participants intensely discussed terminology in the ontology and its definitions. Then, the relationships were checked to ensure alignment with the reference ontologies.  
Not only the context cycle but also vocabulary is iterative. Consequently, competency questions, the entities, their relationships, and definitions were updated by iterations of the two-cycle methodology. Utilizing the stated methodology, a maintenance ontology was constructed through iterative cycles. This was applied to a specific project, providing a platform for PdM that led to improvement in the useful remaining life of the production systems.
## 4 Experiment Results and Discussion
### Performance Evaluation and Discussion  
#### 4.4.1 Natural Model Training and Natural Model Adversarial Training Results  
Three training methodologies were implemented during the investigative phase to determine the baseline accuracy of the employed model. The hyperparameters used in this training effort are meticulously outlined in Table 1. The first approach involved training the model on a normal dataset of 7344 RGB images with a size of 180 \(\times\) 180 pixels. Of these, 5140 images were used for training, and 1102 images were used for both validation and testing. The subsequent method entailed training the model using an adversarial technique, incorporating FGSM adversarial samples into a normal dataset. This composite dataset consisted of 2448 FGSM adversarial samples and 4896 normal RGB images, totaling 7344 images. The images were randomized and partitioned into separate datasets for training, validation, and testing, following the same distribution as the first approach. The third method replicated the scenario of the second, with the only distinction being the use of PGD adversarial samples instead of FGSM adversarial samples to populate the dataset.  
Subsequently, these three trained models were evaluated using an undisclosed dataset that remained untouched during the aforementioned phases. As a reminder, the original dataset [49] contained a total of 12,240 images. Therefore, 1224 normal images were selected to assess the accuracy of these models. The results of this evaluation are presented in Table 4.  
The test accuracies for the first, second, and third methodologies were 91.92%, 91.65%, and 94.28%, respectively, as shown in Table 4. The corresponding test accuracies for unseen data were 90.36%, 91.01%, and 92.16% for the first, second, and third methodologies, respectively. Notably, the third methodology exhibited the highest level of accuracy, in contrast to the accuracies observed in the first and second methodologies. These findings suggest that adversarial training utilizing both FGSM and PGD adversarial samples yields more robust models in the context of the natural model training paradigm.
## 4 Results
### Modification Analysis of the Mudstone Shear Strength
#### 4.2.2 Variation in the Shear Strength of Modified Mudstone  
The shear strength index of the mudstone specimens under different modification conditions is a combination of the cementing effect of electrochemical modification and the drainage consolidation effect. In this study, the direct shear test was conducted with the normal stress of \(\sigma\) = 200 kPa, and the shear specimens were mudstone samples that had undergone various electrolytic modifications. Table 3 displays the electrolytic conditions of the specimens. Figure 7 depicts the process curves for the three shear tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Content** & **15\%** & **20\%** & **25\%** \\ \hline Shear strength & \(\varphi\) (\({}^{\circ}\)) & 23.9 & 20.7 & 14.9 \\ parameters & \(C\) (kPa) & 12.3 & 8.28 & 6.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shear strength parameters for in situ mudstone.  
Figure 6: The \(\tau\)-\(\sigma\) curves of mudstone under different water content.  
Figure 7 demonstrates that electrochemical modification significantly increased the shear strength of the mudstone specimens. At a vertical pressure of 200 kPa, the maximum shear strength of unmodified mudstone specimens with a 25% water content was only \(\tau_{0}=59\) kPa. As the amount of electrolyte increased, the shear strength of the electrochemically modified mudstone specimens increased further. The maximum shear strength of specimen 1 was \(\tau_{1}=70\) kPa, whereas specimens 2 and 3 had maximum shear strengths of \(\tau_{2}=82\) kPa and \(\tau_{3}=83\) kPa, respectively. The increase in shear strength of the mudstone under the electrochemical modification conditions for specimen 3 was not statistically significant compared to specimen 2, primarily because the electrolyte solution injected during the electrochemical modification of specimen 3 had already reached the quantity necessary for complete mudstone specimen modification. Even if the amount of electrolyte solution continues to increase indefinitely, it will be difficult to further enhance the shear strength of the mudstone.  
The shear stress-shear displacement curve can be divided into five stages, which are the compaction stage, linear elasticity stage, yield stage, peak shear stress stage, and post-peak softening stage [32]. Generally speaking, the shear stress of clay soft rock reaches its peak value and then gradually decreases to the residual strength (post-peak softening stage), and the curve shows a continuous process of change. At this time, the specimen displays shear damage, plastic damage, and a residual strength that was slightly less than or approximately equal to the peak strength. It can be seen from Figure 7 that the shear stress-shear displacement curve of the mudstone sample after electro-osmotic consolidation and drainage still conforms to the characteristics of clay soft rock.