esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study Analysis
### Case Study 3: Separation System  
This case study is about an offshore separator system related to an unmanned floating, production, storage, and loading (FPSO) installation. Therefore, it shall be maintained by an optimal maintenance campaign, i.e., it shall be carried out during the annual maintenance campaign interval (2-week period once a year). The separator itself is considered robust equipment, while the surrounding instrumentation connected to the separator constitutes a major factor that causes the separator to be considered critical equipment. Historical data highlight separators as high-maintenance equipment in both preventive and corrective maintenance, as well as having a relatively high MTTR. The case company aims to explore an effective maintenance concept that can ensure an annual maintenance campaign without additional maintenance work. The company believes that the system should avoid redundancy and additional automation instrumentations, focus on the best available technology, explore design maintenance, condition-based, predictive maintenance, autonomous maintenance, and \"Plug & Play\" for a fast replacement scenario.  
In this case study, the separator is maintained through five typical preventive maintenance activities: (1) non-destructive erosion inspection is performed every three months, (2) radioactive profiling is performed every six months, (3) corrosion under insulation (CUI) inspection is performed every 12 months, (4) external inspection is performed every 24 months, and (5) internal inspection is performed every 144 months. Thus, several emerging practices have been observed in this case, summarized in Table 5. The first emerging practice is related to a new worksheet to determine the maintenance concept and the best available technology that is capable of deferring corrective and planned maintenance events (at least all events within a period of one year). The second emerging practice is new visualizations of the master data related to planned and corrective maintenance. The third emerging practice is a new guideline that explores the ability to depend on fixed annual maintenance intervals and planned visits. The fourth emerging practice is a model that estimates the maintainability enhancement using modular replacement, which is known as \"plug&play\" equipment. The fifth emerging practice is a new worksheet that determines the diagnostic coverage of CUI sensors to enable the transformation of inspection actions into monitoring systems.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Emerging Practice** \\ \hline
1 & New guideline to incorporate PsM into RBM program \\
2 & New model to estimate the remaining useful time under prescribed action \\
3 & New data flow for prescriptive maintenance \\
4 & New guideline to reinforce the prescriptive model \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of emerging practices in case study 2.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 4 Physical SCA Attacks on DNNs
### Recovering Network Architecture and Parameters
#### 4.2.1 Micro-Controller Targets
The authors started by (1) proposingpower computational models for the main components of the DNN including convolution, pulling and fully connected layers and different activation functions based on the number of addition and multiplication operations performed by each component. Then, by ignoring other operations that would usually have a negligible effect in comparison, the authors built an overall power consumption model. Next, (2) assuming that pre-training and pruning techniques are usually known to adversaries, the authors propose a characterisation of several DNNs pruned to different ratios in order to obtain various models with different parameters sparsity ratios. These models are implemented in a Rasperry Pi target and the voltage and current traces of the inference operation are collected and gathered into a _power-feature data set_. Part of this data set is used to train another part to feed an SVM classifier that distinguishes the used DNN architecture and the sparsity ratio. The attack framework is illustrated in Figure 4. For evaluation the authors considered AlexNet, InceptionV3, ResNet50 and ResNet101 models with 0.6, 0.8 and 1.0 sparsity ratios using ImageNet data base images reshaped into 224 \(\times\) 224 pixels. This technique achieved 96.5% average classification accuracy for distinguishing the DNN model. By extending this technique using sparsity as a feature, the authors are able to distinguish zero value-weights and biases without deducing their exact value and achieved 75.8% recognition accuracy when considering the AlexNet model. **Discussion and limitations:** This attacks assumes that the overall architecture of the possible DNN used by the victim is known by the attacker as well as the pre-training and pruning techniques. It exploits pruning techniques that result in parameter sparsity. Contrary to other attacks, this work aims at distinguishing the used DNN model among a set of publicly available and widely used ones without deducing the exact parameters. As a possible drawback, we can note that, afterwards, training models can be fine-tuned for optimisation. These modifications in the model could result in misclassifications of the SVM-based model classifier.
### Motivations  
Data-driven modeling often encounters three major data challenges: complexity, unavailability, and drift. The existence of outliers or anomalies in historical data, which are data points that deviate considerably from others, is referred to as data complexity. These anomalies are caused by extremely complicated working conditions and severe environments, which distort sensor readings. These outliers can impact the learnability of the model, misleading the learning process or creating confusion in the patterns [6]. Such a problem should be addressed by considering different relevant analytical or trainable methods that could improve data representation, including feature engineering, outlier detection and removal, denoising, smoothing, filtering, etc. [7]. On the other hand, data unavailability is the result of scarcity of failure patterns, whether under real conditions or in artificial failure generation and aging tests. Practically, such unavailability occurs owing to the rarity of downtime caused by continuous preventative maintenance function. Similarly, in accelerated aging and failure generation tests, failure modes are constrained to a set of operating conditions, and/or modes do not exactly emulate practical applications at some point. The problem created by such a challenge is the lack of representative data to effectively train a machine learning model. Without enough quality data, it becomes difficult for the model to learn different patterns, make accurate predictions, or generalize to new, unseen data well. Therefore, the requirement for generative modeling and/or transfer learning is critical, since they can give additional sources of expertise to address data unavailability gaps (see Figure 2 form [6]). Additionally, data drift refers to rapid change and quickly outdated data. Because data are constantly changing and evolving, trained models on old data may not accurately represent the current state of the system. This can lead to biased or inaccurate predictions, as learning models are not able to adapt to rapid changes in data. Correspondingly, the key solution is to constantly update and retrain machine learning models in order to remain up to date with quickly changing data. Indeed, learning models need to be adaptive, up-to-date, and resilient to this problem [6]. For instance, LSTM networks, adaptive least squares methods with a forgetting mechanism, are very helpful, as they address specific penalization parameters acting as a memory focusing on new driven sequences [8].  
Motivated by the urgent need for data-driven diagnosis and prognosis of aeronautical bearing health conditions in accordance with the aforementioned challenges, this work contributes further significant findings in this field. Accordingly, the following sections will also introduce a very interesting contribution to this field. This study looks into a more appropriate solution to such a problem by offering a well-structured research-gap extraction approach, well-defined analysis criteria, and a collection of contributions filling these gaps. Finally, at the end of this section, the key points of this work will be stated.
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 5 Discussion of Results
### Circadian Stimulus Autonomy  
In some models--such as LON205_300_0814 in Figure 6--CSA and DA300 show similar results; however, the results generally diverge markedly. This highlights the distinct criteria and specific applications of both metrics. Although CSA presents inherent limitations, as discussed in the introduction, their joint evaluation offers complementary insights across a wide variety of case studies. In contrast, DA500 shows minimal alignment with DA, consistently returning substantially lower values throughout the space.  
Both metrics display a clear spatial gradient, with higher percentages near the window. Only in the Seville models with a South orientation and a WWR above 30% are high percentages maintained at the rear of the room, as depicted in Figures 4 and 5. It is also noted that achieving high CSA values may correlate with illuminance levels exceeding the upper UDI threshold (>2000 lx) for a significant portion of the time. However, this does not occur towards the back of the room in the Seville-based models with a WWR above 30% and a southward orientation, as can be seen in Figure 4 and especially Figure 5. Further research is warranted to determine whether the illumination levels required to achieve adequate circadian entrainment could result in discomfort or glare for some occupants, particularly those located near window openings.  
The key aspects to highlight from this section are as follows:  
* CSA and DA300 occasionally align, but generally show distinct results, reflecting their different purposes and offering complementary insights, despite CSA's known limitations. DA500 consistently yields lower values, showing minimal correlation with CSA.
* High CSA values may coincide with exceeding upper UDI thresholds (>2000 lx) near windows, raising concerns about potential discomfort or glare, warranting further investigation.
* The models located in Seville with a WWR > 30% and south-facing orientation exhibit relatively high values deeper into the room, despite UDI showing no major signs of potential discomfort.
###### Abstract  
In this study, an algorithm to identify the maneuvers of a satellite is developed by comparing the Keplerian elements acquired from the two-line elements (TLEs) and Keplerian elements propagated from simplified perturbation models. TLEs contain a specific set of orbital elements, whereas the simplified perturbation models are used to propagate the state vectors at a given time. By comparing the corresponding Keplerian elements derived from both methods, a satellite's maneuver is identified. This article provides an outline of the working methodology and efficacy of the method. The function of this approach is evaluated in two case studies, i.e., TOPEX/Poseidon and Envisat, whose maneuver histories are available. The same method is implemented to identify the station-keeping maneuvers for TDRS-3, whose maneuver history is not available. Results derived from the analysis indicate that maneuvers with a magnitude of even as low as cm/s are detected when the detection parameters are calibrated properly.  
two-line elements; simplified perturbation models; Keplarian elements; trial & error and maneuver detection Article
## 4 [PERSON]'s Finite Element Simulation Analysis
### Analysis of Simulation Results  
#### 4.2.1 Frequency Domain Amplitude Response  
The transient simulation analysis of the damping system is performed in Ansys Electronics Desktop. Displacement excitation of the varying frequencies is applied to the damping system, with different magnetic field intensities and various loads. The comprehensive analysis yields the amplitude response of this damping system, as shown in Figure 13.  
Figure 13 illustrates the overall trend of amplitude response with changing excitation frequency and magnetic potential. Notably, the maximum amplitude response occurs for low magnetic potential around 4-6 Hz, attributed to resonance within that frequency band, resulting in amplification of the response.  
The two-dimensional amplitude response curves for different loads are depicted in Figures 14-18.  
Figure 14: The amplitude response at 60 kg in simulation model.  
Figure 13: Amplitude response at different loads in simulation model.  
Figure 16: The amplitude response at 80 kg in simulation model.  
Figure 17: The amplitude response at 90 kg in simulation model.  
Figure 18: The amplitude response at 100 kg in simulation model.  
Figure 15: The amplitude response at 70 kg in simulation model.  
Observations from these figures reveal that the resonant peak frequency for lighter loads is higher than that for heavier loads, and it is shifted further to the right. As the magnetic potential increases, the suppression effect on the resonant peak improves, but this also causes the resonant frequency to shift rightward. Larger magnetic potential narrows the isolation band, while smaller magnetic potential widens the isolation band.  
According to Equation (19), as the damping increases, the frequency of the resonance peak should slightly decrease. But the data results are the opposite. The speculated reason is that, when the eddy current damping structure vibrates, the cutting of the magnetic induction line by the conductor not only generates a damping force proportional to the velocity (similar to viscous damping) but may generate additional restoring forces related to displacement due to changes in the magnetic field distribution. This restoring force is equivalent to increasing the additional dynamic stiffness of the system. When combined with Equation (14), this additional dynamic stiffness increases the natural frequency of the vibration suppression structure, leading to a rightward shift in the resonance band [31]. Due to the limitation, it is necessary to introduce adaptive control strategies for performance optimization.
# Real-Time Eye Diagram Monitoring for Optical Signals Based on Optical Sampling
## 1 Introduction
The sampled optical pulse is then broadened, photodetected, and quantified by a low-speed electrical analog-to-digital converter (ADC) with a high dynamic range. Thanks to the broad bandwidth of the MZM and the high dynamic range of the ADC, the eye diagram of a 2.5-Gb/s optical signal with a dynamic range of about 12 dB is measured. In addition, due to the high optical sampling pulse repetition rate, real-time monitoring is implemented with an eye diagram measurement time of about 350-\(\upmu\)s. According to the measured eye diagram, more time-domain parameters such as period, time jitter, \(Q\) value, and bit error rate (BER) for the optical signals under test are obtained.
# Adaptive Learning in AI Agents for the Metaverse:
## 4 ALMA: A Framework for Adaptive AI in the Metaverse
### Theoretical Justification
#### 4.2.2 Relevance to Metaverse Applications  
The Adaptive Learning Model for AI Agents (ALMAA) is designed to address the unique challenges and opportunities the Metaverse presents. Its profound relevance to  
Figure 5: Effectiveness of adaptive behaviors in AI agents.  
various applications within this expansive virtual environment enables AI agents to operate with increased autonomy, sophistication, and user-centric responsiveness.  
(1) Enhancing User Experience  
One of the primary applications of ALMAA in the Metaverse is enhancing the user experience. By utilizing adaptive learning, AI agents can personalize interactions based on user preferences and history, adjusting in real-time to provide more engaging and relevant content. This capability is particularly valuable in virtual reality (VR) and augmented reality (AR) settings, where immersive experiences are critical for user satisfaction and retention [36].  
(2) Dynamic Content Generation  
ALMAA also facilitates dynamic content generation, a crucial feature for maintaining the vibrancy of virtual worlds [37]. AI agents with ALMAA can autonomously create and modify environments and narratives in response to user interactions [38]. This keeps the content fresh and exciting and reduces the workload on human developers, allowing for scalable growth within the Metaverse.  
(3) Social and Behavioral Moderation  
In virtual communities, maintaining social harmony and appropriate behavior is essential. ALMAA's adaptive capabilities enable AI agents to moderate interactions effectively, identifying and addressing potentially harmful behavior before it escalates. This application is especially important as virtual worlds grow in size and complexity, providing a safe and welcoming environment for all users.  
(4) Education and Training  
ALMAA is highly relevant in educational and training applications within the Metaverse [39]. Adaptive learning models allow for tailored educational experiences that adjust to each user's learning pace and style. This personalized approach can significantly enhance learning outcomes, making ALMAA an invaluable tool for virtual classrooms and professional training programs [40].  
The relevance of ALMAA to Metaverse applications lies in its ability to make virtual environments more adaptive, intelligent, and user-focused. By bridging the gap between static AI capabilities and dynamic user needs, ALMAA helps unlock the full potential of the Metaverse, paving the way for more innovative and sustainable virtual experiences.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 5 Performance Evaluation
### Dataset Description
After sampling the location of base stations, for each square, we assign the activity of it to its closest base station. When multiple base stations are equidistant from a square, the activity of the square will be evenly distributed among these base stations. We then assume we only know the aggregated activities in base station squares, which is usually the true case in reality. Figure 2c shows the regions split by 100 base stations, where each colour patch is a region charged by one base station. To save space, we don't present the figure for 200 base stations.
## 3 Results and Discussion
### Yield
Neutrase has also been investigated, resulting in a lower recovery of oil compared to corresponding extraction with Alcalase. For example, treatment with Neutrase resulted in 36% oil recovery from cod by-products when the raw material was mixed with water (1:1 \(w\)/\(w\)) prior to extraction but increased to 64% when the raw material was hydrolyzed without added water. The water addition often enhances formation of an emulsion layer, which trapped some of the lipids [28]. In comparison, the best oil recovery reported in our study was 69% using 0.4% Neutrase, which is a smaller enzyme concentration than used in the studies mentioned above.
# DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
## 4 Security, Cost, and Performance Analysis
### Performance Analysis
This FoM helps in algorithm decisions based on the cost and performance trade-offs during algorithm selection. Initially, the highest value in FoM was observed for the PRESENT block cipher with a value of 56.23; now, the highest achieved value is by DNA-PRESENT at 59.32. This indicates that DNA-PRESENT is now observing the perfect cost-performance balance, which was previously achieved by PRESENT. These 10 lightweight solutions are sorted based on their relative area concerning DNA-PRESENT. The last column of Table 11 has the relative size of these lightweight block ciphers compared to DNA-PRESENT. The LED, RECTANGLE, and PRESENT block ciphers have relatively fewer area requirements than DNA-PRESENT, but six other algorithms have a higher cost. This observation indicates that DNA-PRESENT does not have the lowest number of gates but still has relatively fewer gates than other similar block ciphers, as it is ranked 4 th among the 10 block ciphers under consideration. This endorses that the improved cost is still very competitive compared to other lightweight solutions for the security gain achieved. 5. **Execution Time**: Execution time is a measure to identify the time difference as a trade-off to achieve security. Execution time is calculated from three perspectives--encryption, decryption, and overall execution of the complete algorithm [46]. It is performed on 100 plaintexts for both PRESENT and DNA-PRESENT, and the average time is included as a measure. The encryption time for PRESENT is 0.141 s, and it is 0.1201 s using DNA-PRESENT. At the same time, the decryption time is 0.1751 s and 0.1132 s for PRESENT and DNA-PRESENT, respectively. For one plaintext, the total execution time is 0.3161 s and 0.2333 s for PRESENT and DNA-PRESENT, respectively. It can be seen that the execution time of DNA-PRESENT decreased compared to PRESENT because of the lesser number of rounds.
## 4 Results
### Insights from Case Studies
#### 4.1.4 Agri-Food Industry  
Regarding the agri-food industry, two companies were studied. In the case of the first, Company 9, the creation of dashboards was one of the best practices that may be pointed out. Concerning metrological control, data acquisition is linked to the ERP and the PLCs on the production lines. Regarding software, a vertical solution was created covering requirements such as metrological control or the evaluation of production line efficiency. There was also the creation of interfaces that made it easier to carry out inspections/audits.  
In the case of the second company studied, Company 10, systems were installed for the metrological control of processes, integrating mechanisms for validating measurement systems. The production and quality systems were integrated, and a management system for sensory tests and a system for assessing the traceability of flows in laboratories were also implemented. In this company, it could also be seen that there was a focus on the security and robustness of the data system, as well as the involvement of the teams in creating and implementing solutions. In these organizations, the role that algorithms may have in trend comprehension could also be observed.