esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 5 Consumption Forecasts
### Short-Term LPG Consumption Prediction
#### 5.1.1 Short-Term Prediction Models  
LPG consumers can be of different types and can include, e.g., a single-family house, a bakery, a primary school, a farm, or a factory. Their gas consumption can be, therefore, temperature dependent, constant, seasonal, or of a different type. Figure 5 presents two examples of consumption plotted in time, where the months of the year are marked as x-axis labels and the fraction of the tank's capacity is marked on the y-axis as consumption. Temperature-dependent consumption (Figure 5b) is characterized by higher values during winter months and lower values during summer. In the case of the other plot (Figure 5a), no dependency on temperature nor other seasonal changes can be recognized.  
The most typical and unambiguous is the temperature-dependent characteristic of LPG consumption, which is presented in Figure 5b. In such a case, LPG consumption can depend on minimal and maximal daily temperature values and the aggregated values, e.g., mean or median temperature. An example of the dependence of consumption on median daily temperature is visualized in Figure 6a). Such a dependency can be modeled by some kind of regression or a combination of linear models (see Figure 6), e.g., generated in the form of a regression tree.  
Figure 5: Consumption values (fraction of the tank’s capacity) within months—non-temperature-dependent characteristic (**a**); temperature-dependent characteristic (**b**).  
Figure 6: Consumption values (fraction of the tank’s capacity) dependent on the outside temperature (\({}^{\circ}\)C) for the exemplary temperature-dependent (**a**) and temperature-independent (**b**) tanks.  
Having in mind that customer consumption characteristics may be of different types (not only purely temperature based), the proposed model may be extended to other features, e.g., such as the number of the day of the week or the number of the month.
## 2 Peculiarities of ATR Measurements
### Combined Spectral Filters and Polarisers
Figure 8: (**a**) SEM images of THz filters defined by photolithography: \(\sim\)6 \(\upmu\)m-thick AZ4562 resist (applied on surface) and SU8 laminate 20–500 \(\upmu\)m thickness (SUEX from DJ Microlaminates Ltd.; applied or free-standing.) The period, length and width \(P,L,W\) define the central wavelength \(\lambda_{0}\) and the bandwidth \(\Delta\lambda\) of the filter [38]; gap \(G=P-L\). (**b**) Superimposed five THz bands filter based on the cross apertures (**a**). The binary mask (false colour) and the \((0-2\pi)\) phase map of the filter (see details in Figure 9). Spectrum of Australian synchrotron radiation at the THz beamline [40] is shown with selected five frequencies (star-markers). Due to the isotropic geometry of the cross lattice and symmetry of the square lattice, an isotropic polarisation response is expected [42]. Polarisation active filters can be fabricated using the same approach. Due to silicon's high transparency in the THz spectral band and readily available Si plasma etching for high aspect ratio structures, optical elements, such as optical vortex generators for spin and orbital angular momentum (SAM and OAM), manipulation at THz frequencies can be made [43]. A Fresnel rhomb with an apex angle \(\sim\)55\({}^{\circ}\) made from THz-transparent teflon or cyclic olein copolymer TOPAS [44], which have a refractive index of \(\sim\)1.5 over a wide THz spectral range can be used for a quarter waveplate generator of circular polarisation out of the linearly polarised input. This is especially useful for bio-medical and pharmaceutical applications where left-vs. right-hand enantiomers and chirally active molecules have to be detected. Long wavelength (low frequency) optical radiation is promising for opto-mechanical applications [45; 46; 47; 48; 49; 50; 51]. Indeed, the reaction torque exerted onto an object by a beam of power \(P\) is \(\Gamma=\Delta\sigma P/\omega\), where \(\Delta\sigma\) is the before-after change of the circular polarisation state [52].
###### Abstract  
Cavitation-induced degradation of metallic materials presents a significant challenge for engineers and users of equipment operating with high-velocity fluids. For any metallic material, the mechanical strength and ductility characteristics are controlled by the mobility of dislocations and their interaction with other defects in the crystal lattice (such as dissolved foreign atoms, grain boundaries, phase separation surfaces, etc.). The increase in mechanical properties, and consequently the resistance to cavitation erosion, is possible through the application of heat treatments and cold plastic deformation processes. These factors induce a series of hardening mechanisms that create structural barriers limiting the mobility of dislocations. Cavitation tests involve exposing a specimen to repeated short-duration erosion cycles, followed by mass loss measurements and surface morphology examinations using optical microscopy and scanning electron microscopy (SEM). The results obtained allow for a detailed study of the actual wear processes affecting the tested material and provide a solid foundation for understanding the degradation mechanism. The tested material is the Ni-based alloy INCONEL 625, subjected to stress-relief annealing heat treatment. Experiments were conducted using an ultrasonic vibratory device operating at a frequency of 20 kHz and an amplitude of 50 \(\upmu\)m. Microstructural analyses showed that slip bands formed due to shock wave impacts serve as preferential sites for fatigue failure of the material. Material removal occurs along these slip bands, and microjets result in pits with sizes of several micrometers.  
cavitation erosion; stress-relief annealing; INCONEL 625 superalloy
## 5 Simulations
### Evaluation Metrics  
Our routing algorithm was compared with the above routings algorithm to assess its performance in the same simulation environment. In addition, this study mainly focused on the following metrics [33]:  
* Delivery ratio: This measurement metric refers to the probability of choosing a suitable node as the next hop node, which is expressed as: \[D_{node}=D_{receive}/D_{send}\] (18) where \(D_{receive}\) is the number of received messages by surrounding neighbor nodes and \(D_{send}\) is the number of forwarded messages by nodes.
* Average end-to-end delay: This parameter comprehensively evaluates the delay caused by routing selections, relay nodes' waiting delay, and transmission delay. The average end-to-end delay can be described as: \[E_{del}=E_{sum}/N\] (19) where \(E_{sum}\) is the total delay of message forwarding from source nodes to destinations and \(N\) is the number of nodes that successfully acquired the message in the communication area.
* Overhead on average: This parameter represents the network overhead for successfully passing messages between a pair of nodes, which could be formalized as: \[O_{node}=\frac{N_{to}-N_{su}}{N_{to}}\] (20) where \(N_{to}\) is the total time of data transmission and \(N_{su}\) represents the total time of successful message forwarding between nodes.
* Energy surplus: this parameter records the energy surplus of the node during transmission.
## 5 Latency and Reliability Performance Evaluation in Wearable-Based MCPTT Applications
### Future Considerations
Figure 8a,b show that for each value of the PSCCH period, there is a scheduling method that provides better results than the other methods in terms of latency and reliability, respectively. The impact of resource selection methods is even clearer when considering MCPTT group calls with an increased number of users [6]. The obtained early-stage evaluation results show potential insights that motivate future standalone studies on the choice of suitable resource selection methods in sidelink transmission mode-2. The latest 3 GPP work items on NR sidelink enhancements recommend also the study of the feasibility and benefits of new solutions for improved reliability and reduced latency in sidelink transmission mode-2. The main solution that is defined in these items is inter-UE coordination [52]. In brief, a UE A determines a set of resources for the sidelink transmission and sends it to a UE B in mode-2, which takes the received set into account in the resource selection for its own transmission. The sent set of resources from UE A to UE B can be based on sensing results and/or potential resource conflicts. The final solution should be able to operate in all coverage scenarios and further studies are required to assess its feasibility and benefits in terms of latency and reliability [51].
## 3 Materials and Methods
### Dataset Preprocessing
#### 3.2.2 Data Annotation  
In this study, data annotation directly affects the model's performance and final results. Initially, in the preparation phase for data annotation, the collected data undergo preliminary processing and screening. This includes data cleaning and preprocessing to ensure the input data for the annotation system is clean and usable. For example, text data need to be noise-free, and irrelevant information needs to be removed and undergo tokenization and part-of-speech tagging. These steps can be achieved using standard text preprocessing techniques, such as regular expression matching and natural language processing toolkits (e.g., NLTK and spaCy). For image data, basic image processing is required, such as cropping, rotation, and color adjustment, to ensure the quality and consistency of the images. Next, devising an annotation strategy is the core step in data annotation. To ensure consistency and efficiency, semi-automated annotation tools are used in combination with manual verification. Text data annotation includes tasks such as sentiment classification, entity recognition, and text summarization, while image data annotation involves tasks like object detection, image classification, and image segmentation. Detailed annotation guidelines and standards are established for each annotation task to ensure that every annotator follows a consistent standard. Additionally, to further enhance privacy protection during data annotation, a user attention mechanism is introduced. This mechanism identifies and prioritizes the privacy needs of users' sensitive information, providing additional privacy protection during the annotation process. For example, for text data involving personal privacy information, the attention mechanism identifies sensitive words and phrases, and more noise is added during annotation to enhance privacy protection. Similarly, for image data, the attention mechanism identifies image areas containing personal identity information, and these areas are blurred or additional noise is added during annotation.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 2 Materials and Methods
### Validation of the Models  
Initially, the model was validated to prove its effectiveness for designing and analyzing the mechanical response of microcantilevers. The condition for validation of the model was that the microcantilever under test operated in the molecular regime where the damping of the surrounding fluid can be neglected.  
The integrated electrode/AlN/electrode layered structure on a nanocrystalline diamond (NCD) microcantilever, as reported in reference [27], was selected for comparison to validate the model developed in this study for the UNCD-based microcantilever. This choice is justified by the similarity in grain size between NCD (\(\sim\)10-20 nm) and UNCD (\(\sim\)3-5 nm). In reference [27], the characterization of the electrode/AlN/electrode/NCD microcantilever was performed using impedance analysis under relatively low vacuum conditions, specifically at a pressure of 200 mTorr.  
Determining the first resonant frequency and its corresponding quality factor is essential for evaluating the accuracy and reliability of the models implemented in this study.
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 1 Introduction
The proposed network exploits both the ability of the local feature extraction of the convolution kernel and the capacity of capturing the global context information of the Transformer to learn the spatiotemporal characteristics of video scenes. Meanwhile, considering the  
Figure 1: Schematic diagram of video snapshot compressive imaging. complexity of a video scene, according to the residual measurement mechanism [31], the proposed network is designed as two cascading stages: overall structure reconstruction and incremental details reconstruction. In the first stage, the multi-scale Transformer network is designed to reconstruct the overall structure by extracting multi-scale and long-range spatiotemporal features. In the second stage, the measurement residual of the first stage is taken as the input, and the dynamic fusion Transformer network is designed to adaptively fuse the features of the Transformer in the two stages so as to reconstruct incremental details. In this way, more detailed structures can be reconstructed, and the reconstruction quality can be effectively improved. The main contributions of our work are as follows:  
1. We propose a Transformer-based Cascading Reconstruction Network to learn multi-scale and long-range spatiotemporal features for video snapshot compressive imaging. The cascading network architecture can also improve the network's ability to effectively represent the content of complex video scenes effectively. 2. In order to coordinate the two stages better, we propose a dynamic fusion Transformer to adaptively fuse the features of the two stages so that the second stage can reconstruct more incremental details. 3. Experiments on simulated and real datasets show that the proposed network can improve the reconstruction quality significantly, and the ablation studies also verify the effectiveness of our network architecture.
## 5 Discussion and Implications
### Potential Benefits of Integrating ML and GenAI into Learning Analytics (RQ2)  
The integration of machine learning and generative AI into learning analytics in higher education presents benefits across pedagogical, institutional, and research domains.  
Pedagogically, ML enables early risk detection, allowing timely interventions, while adaptive systems personalize feedback, pacing, and resources. GenAI tools enhance formative feedback by generating scalable, context-aware responses to student submissions. Additionally, conversational agents support metacognition and self-regulated learning (SRL) through the scaffolding of reflection, planning, and help-seeking behaviors.  
Institutionally, predictive dashboards aid retention monitoring and academic advising, while LA-informed instructional design improves curriculum responsiveness. Scalable intervention systems, such as real-time alerts and adaptive prompts, enhance support in large or asynchronous courses. Cluster-based modeling of LMS data has also improved generalizability in early-warning systems, particularly when courses are grouped by structural similarity [131].  
For research and innovation, multimodal learner modeling--integrating behavioral, affective, textual, and biometric data--offers a richer view of the learning process. Advances in explainable AI (XAI), including SHAP, LIME, and knowledge graphs, contribute to transparency and increase educator trust. Furthermore, hybrid LA systems that combine the predictive power of ML with the generative capabilities of GenAI enable more personalized and dynamic learning environments.
## 3 Results and Discussion
### The Impacts of CPA on XPD  
Generally, the XPD varies inversely as the CPA. A radio link with low CPA simply implies that only a tiny component of the total signal is depolarized, hence there is a high discrimination (XPD) between the original polarized and orthogonally polarized waves. Conversely, higher CPA would reduce the XPD, which signifies high cross-talk and a consequential increase in the interference between two orthogonal channels in a communication system. A similar observation was reported for Akure (Nigeria) and Eindhoven (Netherlands) by [PERSON] [26] and [PERSON] [31], respectively. The observed effects of rain height variability on XPD, especially at high frequencies, informed the decision to investigate the impacts of seasonal CPA on XPD. Figure 3a-d present the relationship between the CPA and the resulting XPD due to rain.  
Figure 3: _Cont._Figure 3: _Cont._  
It could be observed from the curves that low values of seasonal CPAs have negligible effects on the seasonal XPD, but notable impacts are evidence at high CPAs. Figure 3a-f show that rain height variability has noticeable impacts on 11.7 GHz, 15 GHz, 20 GHz, 25 GHz, 30 GHz, and 35 GHz radio links when the CPA exceeds 22 dB, 32 dB, 41 dB, 48 dB, 53 dB, and 58 dB, respectively. Hence, special attention must be focused on the XPD of high-frequency signals whose demand is continuously growing due to its several advantages.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 4 Experiment and Results
### Performance of Indoor Localization
However, the proposed modified technique outperforms KNN and the mean error is 1.46 m for LG G6 while Galaxy S8 mean error is 1.15 m. Even though the same localization process is followed for KNN and mKNN and the same value of \(K\) is used, yet the accuracy is improved with mKNN. The user can be located within 1.08 m at 50 percent with mKNN irrespective of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method \& Device** & **Mean Error** & **Standard Deviation** & **50\% Accuracy** & **75\% Accuracy** \\ \hline KNN-G6 & 1.86 & 1.44 & 1.53 & 2.88 \\ mKNN-G6 & 1.46 & 1.23 & 1.08 & 2.22 \\ KNN-S8 & 1.40 & 1.24 & 1.02 & 2.18 \\ mKNN-S8 & 1.15 & 1.01 & 0.89 & 1.68 \\ MFP-S8 with scene & 2.04 & 1.44 & 1.50 & 2.9 \\ MFP-G6 with scene & 2.47 & 2.41 & 1.70 & 3.35 \\ MFP-S8 without scene & 14.05 & 17.45 & 5.74 & 2.28 \\ MFP-G6 without scene & 19.74 & 21.02 & 9.77 & 34.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error statistics for KNN and mKNN. Figure 10: Localization results for S8 and G6. smartphone used for localization. Indoor scene recognition helps to achieve the low localization error, as, without scene recognition the magnetic database search space is large which results in higher error.
## 5 Verification of the Simulation
As shown in Figure 18, the control strategy proposed in this article reduces the supercapacitor SOC and does not cause energy discharge. The supercapacitor exits frequency regulation due to energy discharge at 2.85 s and 2.84 s, respectively, by EMD distribution and supercapacitor-independent frequency regulation, resulting in the frequency regulation effect of the two not being ideal. At the same time, when the EMD algorithm is used for distribution, the supercapacitor exits the frequency regulation, causing the system frequency to fluctuate again at 2.85 s. The overall frequency regulation effect is not ideal. The following table records the lowest value of system frequency drop when using EEMD and EMD algorithm allocation. It can be seen from the Table 3 that the lowest value of the system frequency drop when using the EEMD algorithm is generally higher than the result when using the EMD algorithm for distribution. Under operating conditions that consider the wind power aggregation effect, when the system load suddenly increases by 10 MW, 15 MW, and 30 MW, the lowest frequency drop value of the EEMD algorithm is 0.02 Hz, 0.01 Hz, and 0.03 Hz higher than that of the EMD algorithm, respectively. Compared with the EEMD algorithm, the EMD algorithm lacks the step of adding white noise, so EMD cannot have the same precise distribution effect as EEMD, and it is difficult to obtain a more reasonable power distribution when reconstructing the IMF component. Therefore, EEMD has a better frequency regulation effect and working condition universality. Figure 17: Operation curve of P2H and SCESS under different control strategies. Figure 18: State of charge of SCESS.
## 3 Algorithmic Errors
The system uses sensors and data analytics to ensure that the handling and storage of hazardous materials comply with safety regulations. AI algorithms continuously analyze data to identify any deviations from compliance protocols [54; 55]. The system has been instrumental in maintaining Evergreen Marine's adherence to international safety standards. During a routine operation, the AI system detected that a container carrying hazardous chemicals was not stored at the required temperature. The system alerted the crew, who were able to take corrective action to ensure compliance and prevent potential risks [56; 58]. Barriers:  
* Maintaining continuous and reliable data transmission. * Integration with legacy compliance systems. * Ensuring data security and privacy. Opportunities:  
* Enhanced regulatory compliance through continuous monitoring. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Description** & **Benefits** & **Examples** \\ \hline Real-Time Monitoring & AI systems continuously monitor hazardous materials, ensuring compliance with safety protocols and detecting anomalies. & Enhanced safety, early detection of potential hazards & Real-time monitoring systems in chemical tankers [53; 54; 55] \\ \hline Automated Response Systems & AI-driven automated systems that can initiate immediate response actions in case of hazardous material incidents. & Quick and efficient response, minimized human exposure & Automated fire suppression systems [56; 57] \\ \hline Data Analytics and Prediction & AI analyzes historical data to predict potential hazardous material incidents and optimize handling processes. & Proactive risk management, reduced incidents & Predictive analytics in cargo handling [54; 55; 56] \\ \hline Regulatory Compliance & AI systems help ensure adherence to international and local regulations regarding hazardous materials. & Improved compliance, avoidance of fines and legal issues & Compliance management systems [55; 56; 57] \\ \hline Incident Management & AI supports the management of hazardous material incidents by providing real-time data and response recommendations. & Efficient incident resolution, reduced impact on operations & Incident management platforms [54; 56; 57] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AI in hazardous material handling. * Prevention of incidents through early detection of non-compliance. * Increased operational efficiency by reducing manual compliance checks. These case studies highlight the transformative impact of AI in hazardous material handling in maritime transportation. By providing real-time monitoring, predictive maintenance, and automated response capabilities, AI systems enhance safety, ensure regulatory compliance, and reduce the risk of hazardous material incidents. Addressing the identified barriers and leveraging the opportunities can further enhance the effectiveness and adoption of AI technologies in the maritime industry.