esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Gaps and Proposed Directions  
This review highlights promising opportunities to bridge the gaps in AI-driven IAM systems by addressing interoperability, scalability, ethical concerns and privacy. Below, we propose directions to advance the field.  
One major issue is the lack of interoperability and standardized protocols across industries. This gap is particularly evident in environments where diverse legacy systems coexist with modern AI-driven frameworks, complicating seamless integration. The development of harmonized standards and APIs could address these inconsistencies, fostering smoother communication and compatibility between systems.  
Another issue is the scalability of AI solutions within resource-constrained industrial settings. Legacy systems often lack the computational capacity to support sophisticated AI algorithms, posing barriers to widespread adoption. Exploring lightweight and adaptive AImodels optimized for edge devices could offer a practical solution, enabling the deployment of high-performing IAM systems without overburdening existing infrastructures.  
Moreover, the reliance on AI for decision-making in IAM introduces ethical concerns, particularly around algorithmic biases. Decisions informed by biased datasets could lead to discriminatory practices, undermining trust in such systems. Incorporating fairness-aware machine learning techniques and regular audits of system decisions are crucial steps toward mitigating these risks. At the same time, the absence of benchmarking datasets and evaluation frameworks tailored to industrial IAM applications remains a significant limitation in the field. Establishing open-source resources would encourage consistent and replicable research efforts, paving the way for more robust and transparent advancements.  
Privacy concerns also play a pivotal role in shaping the future of AI in IAM. Federated learning offers a promising approach by enabling organizations to collaboratively train models without compromising sensitive data. Such techniques not only enhance security but also address the increasing demand for privacy-preserving solutions in industrial environments.  
Looking ahead, the field would benefit from research focused on dynamic policy adjustment mechanisms that leverage real-time user behavior analytics to fine-tune access control measures. Additionally, the creation of open-access datasets and privacy-preserving frameworks could further support the adoption and refinement of AI-based IAM systems. Together, these efforts could address the current gaps and catalyze the development of more effective and trustworthy solutions for industrial applications.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
###### Abstract  
The appearance of damage on metallic structures is inevitable due to complex working environments. Non-destructive testing (NDT) of these structures is critical to the safe operation of the equipment. This paper presents a non-destructive damage detection, visualization, and quantification technique based on laser-generated ultrasonics. The undamaged and damaged metallic structures are irradiated with laser pulses to produce broadband input ultrasonic waves. Damage to the structures plays the role of a nonlinear radiation source of new frequencies. Usually these new frequencies are too weak to be detected directly. Here, the state space predictive model is proposed to address the problem. Based on the recorded responses in the time domain, the state space attractors are reconstructed. Damage to the structures is shown to change the properties of the attractors. A nonlinear damage detection feature called normalized nonlinear prediction error (NNPE) is extracted from the state space to identify the changes in the attractors--and hence the damage. Furthermore, the damage is visualized and quantified using the NNPE values extracted from the entire area by using a laser scanning technique. Experimental results validate that the proposed technique is capable of detecting, visualizing and quantifying artificial damage to aluminum alloy plates and actual fatigue cracks to a twin-screw compressor body.  
Keywords:damage detection; damage quantification; laser-generated ultrasonics; state space reconstruction; normalized nonlinear prediction error +
Footnote †: journal: Applied Sciences
# Evaluation of IEEE802.15.4g for Environmental Observations
## 3 Results
### Line of Sight (LoS)  
RX nodes are located at 420 m, 700 m and at 1000 m from the TX node. Table 7 shows the PDR value for the three RX nodes considering packets of 127 B and 2047 B. At 420 m, radio settings with high data rate have a PDR around 100%. At 700 m, the PDR stays between 70% and 100% for radio settings using FSK and O-QPSK. Radio settings using OFDM have a poor PDR, in most cases incapable of getting any frame across. The exception is OFDM2-100, with PDR of 92% for short frames and 47% for long frames.  
Figure 6: Advance Metering Infrastructure scenario.  
The poor performance of OFDM at 700 m, in comparison to FSK and O-QPSK PHYs is due to that the TX power is not the same for all radio settings. According to Table 6, the maximum TX power for OFDM radio settings is +11 dBm whereas for the rest is +14 dBm. The robustness of OFDM can be shown in strong multi-path environments (e.g., dense cities), where singled-carrier radio signals would suffer from self-Interference caused by multiple rays of the same symbol arriving at different time and interfering with subsequent symbols. Here it is not the case. We see how singled-carrier signals have a good PDR and OFDM PHYs do not. Therefore, the low performance of OFDM is due to the attenuation the radio signal suffers through the path towards the RX node.  
At 1000 m, the radio link is almost nonexistent, not useful for data exchange.
## 3 Environmental Applications of Organoclays
### Air Pollution
[149] synthesized modified montmorillonite with choline chloride-urea as the eutectic solvent and used it for \(\mathrm{CO_{2}}\) adsorption. They found that it had an adsorption of 252 mg/g of \(\mathrm{CO_{2}}\) at 30 \({}^{\circ}\)C, a bar pressure of nine, and 50% of the solvent-modified clay. Meanwhile, [PERSON] et al. [150] studied the roles of different clay minerals, such as montmorillonite, illite, and kaolinite, in the adsorption of gaseous hydrogen at a lower temperature with lower pressure and a higher pressure with a higher temperature. They found that the specific surface area and the micropore volume have a positive effect on hydrogen adsorption and a negative effect on the pore size. [PERSON] et al. [152] recently modified montmorillonite by a combined thermal and acid activation treatment to adsorb gaseous \(\mathrm{PbCl_{2}}\). Figure 5: Applicability of cationic and anionic dendrimer-modified cationic and anionic clays as a potential binding site provider for \(\mathrm{CO_{2}}\) and \(\mathrm{NH_{3}}\) gas adsorption [27], copyright 2017, Elsevier. From the above data, it can be concluded that organically modified clay and acid-treated clay have tremendous potential for air pollution control. According to the data, the role of the amide-terminated group was dominant for CO\({}_{2}\) adsorption due to its physicochemical interactions. Meanwhile, acid treatment has been the most advantageous treatment for improving pore size and surface area for gas adsorption.
De-identification is defined as a process of removing personal identifiers by modifying or replacing them to conceal some information from public view [8]. However, de-identification has not been a primary focus of biometric research despite the pressing need for methodologies to protect personal privacy while ensuring adequate biometric trait recognition.  
There is no agreement on a single definition for what de-identification truly is in the literature on the subject. For instance, [PERSON] et al. [9] defined de-identification as follows: \"The process of concealing personal identifiers or replacing them with suitable surrogates in personal information to prevent the disclosure and use of data for purposes unrelated to the purpose for which the data were originally collected\". However, [PERSON] et al. [10] proposed the following definition: \"De-identification refers to the reversible process of removing or obscuring any personally identifiable information from individual records in a way that minimizes the risk of unintended disclosure of the identity of individuals and information about them. It involves the provision of additional information to enable the extraction of the original identifiers by, for instance, an authorized body\". While the primary goal of de-identification is to protect user data privacy, its implementation is strikingly different depending on the application domain or the commercial value of the designed system. In the subsequent sections, we explore the differences among de-identification methodologies in depth, create a taxonomy of de-identification methods, and introduce new types of de-identification based on auxiliary biometric features.  
This article summarizes fragmented research on biometric de-identification and provide a unique classification based on the mechanisms that achieve de-identification. Thus, it makes the following contributions:  
1. For the first time, a systematic review is presented with a circumspect categorization of all de-identification methodologies based on the modalities employed and the types of biometric traits preserved after de-identification.
2. Four new types of emerging modalities are presented where de-identification is desirable and beneficial, namely sensor-based, emotion-based, social behavioral biometrics-based, and psychological traits-based de-identification.
3. A new paradigm for the design and implementation of multi-modal de-identification is proposed by considering the categories of traditional, soft, and auxiliary biometric traits and their de-identification.
4. A list of applications in the domains of cybersecurity, surveillance, risk analysis, mental health, and consumer applications is presented, where de-identification can be of critical importance in securing the privacy of biometric data.
## 4 Results and Discussion
### Spatiotemporal Variations of Multi-Year ET, GPP, and WUE for 12 Irrigation Districts  
The temporal dynamics of the multi-year ET, GPP, and WUE in each irrigation district from 2010 to 2016 are shown in Figure 7 (histograms represent the average of oasis agricultural areas with the geographical location identified by the 12 irrigation districts). The multi-year regional average values (Figure 7, black box) and the multi-year temporal dynamics coefficient of variation (CV) characteristics (Figure 7, red stars) of GPP, ET, and WUE for each irrigated district were similar in midstream oasis agricultural areas.  
The highest mean multi-year ET (580 mm) was found for the Banqiao, Wujiang, and Yingke districts, and the lowest mean (500 mm), for the Liyuan River district (Figure 7a). An analysis of annual variations (\(\sigma_{\rm ann}\) and CV) indicates the annual ET of Ganjun had the largest variations from 2010 to 2016 (\(\sigma_{\rm ann}\) 96 mm, CV 0.19) and that of Wujiang had the smallest variations (\(\sigma_{\rm ann}\) 16 mm, CV 0.03). No significant temporal variation trend in annual ET was found for the seven-year study period (2010-2016) over the midstream oasis.  
The highest mean multi-year GPP (530 gC/m\({}^{2}\)year) was found for the Yingke irrigation district, and the lowest (370 gC/m\({}^{2}\)year) for Liaquan (Figure 7b). The annual variations (\(\sigma_{\rm ann}\), CV) indicated that the annual GPP of Ganjun was the largest (\(\sigma_{\rm ann}\) 108 gC/m\({}^{2}\), CV 0.27) and that of Wujiang was the smallest (\(\sigma_{\rm ann}\) 28 gC/m\({}^{2}\), CV 0.06). No significant temporal variation trend in annual GPP was found for the study period (2010-2016).  
The results show that on an annual scale, the oasis croplands of the irrigation districts with higher ET also had higher GPP, which ultimately produces larger WUE. However, while the Wujiang irrigation district had higher annual ET and GPP than the Daman district, its WUE was lower (Figure 7c). WUE exhibited relatively small interannual variability. For example, WUE for Yingke and Wujiang was basically the same over all years.  
In addition, the highest mean multi-year WUE (0.80 gC/kgH\({}_{2}\)Oyear) was found for Yingke and the lowest (0.58 gC/kgH\({}_{2}\)Oyear) for Liaquan (Figure 7c). The annual variations (\(\sigma_{\rm ann}\), CV) indicate that the annual WUE of Ganjun was the largest (\(\sigma_{\rm ann}\) 0.19 gC/kgH\({}_{2}\)O, CV 0.30) and that of Yingke was the smallest (\(\sigma_{\rm ann}\) 0.02 gC/kgH\({}_{2}\)O, CV 0.03). In a previous study, [PERSON] et al. (2016) showed mean annual WUE values from 0.5 to 1.0 gC/kgH\({}_{2}\)O for vegetation (including grassland and cropland) across various ecosystem types in China.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 2 Materials and Methods
### Proposed Model  
The end-to-end CNN model proposed in this paper is shown in Figure 3. The network contains a total of 28 layers, including 16 convolutional layers and 3 maxpooling layers, while the Atrous Spatial Pyramid Pooling (ASPP) module contains 10 convolutional layers. In the structure shown in Figure 3, the first three convolutional layers are used to extract image features, and the output feature maps are then input into the ASPP module, whose structure will be described in detail in Section 2.2.3, to extract the multi-scale crack feature information. Furthermore, the depthwise separable convolution contained in the ASPP module reduces the computational complexity of the model, thus making the network easier to optimize. In the last three convolutional layers of the network, we apply atrous convolution to replace the maxpooling layers, thus avoiding the degradation of the image resolution while increasing the receptive field. At last, the network uses Softmax to classify the input pictures (cracks or backgrounds).
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Challenges and Insights from Cross-Domain Analysis  
While deep learning-based domain adaptation approaches show impressive performance gains, our study reveals several issues and insights that call for consideration in the search for more developments.  
The choice of acceptable target domains presents a major problem. To develop representations that are domain-invariant, deep learning-based techniques largely rely on target domain data [306]. As a result, the performance of the models' generalization and adaptation depends greatly on the choice of target domains. To achieve successful domain adaptation, it becomes essential to make sure that the target domain data appropriately depict real-world circumstances. Another difficulty is presented by the intricacy of robotic vision tasks [297]. The adoption process must be quick and effective in situations when robotic vision necessitates making decisions in real time. Deep learning-based methods sometimes need a lot of computing power and can lengthen inference times. There are still ongoing studies on how to solve these computational problems accurately [298].  
The requirement for domain-specific adjustments is highlighted by insights from the cross-domain study. While deep learning-based systems succeed at some tasks, there are instances where more conventional approaches, like TCA and MMD, show comparable efficacy [307]. A fascinating direction for future study is the incorporation of a hybrid strategy that benefits from the advantages of both conventional and deep learning-based approaches. The performance of deep learning-based models is also substantially impacted by the availability of annotated datasets for the target domains [308]. Unsupervised or weakly supervised domain adaptation approaches might be useful alternatives when labeled target domain data are difficult to get or are expensive [309].  
Overall, a landscape of potential and problems is revealed by the comparative comparison of domain adaptation strategies in computer and robotic vision. The domain-specific nature of the tasks and computational concerns call for deliberate modifications and multidisciplinary cooperation, even though deep learning-based approaches show considerable promise [301]. The knowledge gathered from this analysis will help researchers and professionals navigate the difficulties of domain adaptation and encourage the creation of more reliable and effective vision systems for practical applications [311].
## 3 Finite Element Calculations
### Boundary Conditions  
The process of determining the effective properties of a fictive equivalent homogeneous medium involves solving a boundary value problem within a representative elementary volume \(dV\). It is crucial that this volume is adequately sized to capture the complexity of the microstructure inherent in the heterogeneous material. This complexity arises from the presence of various heterogeneities. In the case of periodic distribution of constituents, the required volume is reduced to an elementary cell. This cell can effectively replicate the entire microstructure through simple translation, forming a sort of tiling pattern. Subsequently, this selected volume is subjected to elementary loads or external conditions to obtain a response. This response is then analyzed to derive the effective properties of the composite material. The main challenge of this process lies in the choice of boundary conditions to be applied to the volume under consideration. These boundary conditions [23; 27] are crucial to ensure that a given global average strain or stress, i.e., macroscopic, is accurately reproduced in calculations. The idea is basically to model and analyze the material's behavior on a larger scale, taking into account the collective effects of its microstructure on its overall mechanical response.  
Figure 3: Effect of changing the number of finite elements on the variation of bulk modulus and thermal conductivity.
## 6 Verification and Results
### Episode 2: Resilience and Availability (req: v, vii)
#### 6.2.1 SFC Protection  
Our Proof-of-Concept implementation of the P4 data plane did not include any event handlers of detecting network failures such as link-down or node-down (such as LLDP discovery events in OpenFlow). However, we simulated an \"NFVi fault and management\" [1] approach similar to ceilometer monitoring in OpenStack, by monitoring the VMs instead of the virtual switch interfaces. Consequently, we assume that the NFV infrastructure is capable of automatically migrating the VMs to another Compute Node during hardware failures. However, this migration process results in network outage time for the end-user where network outage time = detection time + VM provisioning + key distribution + route distribution (Table 1 (2.b)). In order to reduce the network outage time, it is possible to pre-instantiate redundant VMs. Consequently, the VM provisioning time is removed from the equation. Hence, we simulated a protection of software and hardware failure by duplicating the VMs running at ISP-B to also run at ISP-A. By letting the VMs run in both domains, the VM becomes protected and, consequently, it also creates an SFC protection. In order to simulate a hardware failure, we manually shut down the VMs running at ISP-B by using KVM virsh directly on the VMs.  
Table 1 (2.a, 2.b) shows the failover time when SFC protection is enabled and when it is not. A network topology with SFC protection enabled provides evidence for a resilient and dynamic SFC architecture (req: vii). However, it is noted that the SFC protection introduces an additional resource consumption for all the VMs that are running in standby mode.  
A method of reducing the resource consumption is to reduce the number of duplicate EFs. The aforementioned demonstration duplicated four EFs. Consequently, the additional pairs of EFs were already authenticated and failover is performed by a distribution of the flow rules only. By not duplicating EF1 and EF4, these EFs can be reused in order to reconnect to the duplicated versions of EF2 and EF3. This adds an additional 1.2 s to failover process, but it reduces the overall resource consumption.
## 4 Research and Workshop Methodology
### Questionnaire Activity  
The activity involved administering a structured questionnaire, an excerpt shown in Figure 7, consisting of 10 diverse instruments adapted from the larger pool of 30 instruments used in the brainstorming activity. The selection of these instruments was guided by two primary criteria: First, some instruments were chosen due to their widespread use and acceptance in the relevant literature, such as _effort expectancy_[66; 77; 78; 79; 80; 81; 82; 83; 84]. Second, other instruments were selected based on their specific relevance to the project, such as _compatibility and privacy concerns_.  
Participants, identified as either PwF residents or specialists (i.e., healthcare professionals or caregivers), were asked to provide their responses on a 5-item unipolar Likert scale, indicating their level of agreement or disagreement with statements related to each construct. This workshop activity aimed to collect quantitative data on the preferences, concerns, and factors influencing technology adoption among PwF and relevant specialists. This would reveal specific preferences, expectations, and concerns regarding aspects like ease of use, perceived usefulness, compatibility with routines, and privacy considerations.  
Figure 7: Excerpt from CIREI questionnaire form. Full details are presented in Appendix C.  
It would also shed light on potential barriers, such as perceived difficulty, lack of compatibility, or privacy concerns, as well as facilitators such as perceived benefits or social influence. By analysing the data, we could highlight areas requiring interventions or support strategies, such as training for improved ease of use or transparent data management practices to address privacy concerns. Additionally, comparative analysis between the perspectives of PwF and specialists could reveal areas of alignment or divergence, informing more inclusive and holistic technology design.  
This activity, as discussed, seeks to quantify preferences, concerns, and factors influencing technology adoption, providing valuable data on learning preferences and barriers to comfort with new technologies, thus addressing both **R2a** and **R2b**. Its results can inform the design of educational content and interfaces to enhance ease of use and perceived usefulness, aligning with the learning preferences of older adults in independent living communities and facilitating better technology adoption.
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
###### Abstract  
This study is an examination into the feasibility of a quasi-static approach to evaluating the reaction forces that impact tires. This information could lend valuable insight into efforts to limit overturning and side-slip accidents of vehicles exposed to strong side winds. The performance of the quasi-static approach was evaluated by comparing the calculated tire forces with those created using TruckSim, which is a dynamic vehicle analysis software. Governing equations were formulated for two types of vehicles that are susceptible to the force of wind, vans, and trailers, by considering a force equilibrium, a compatibility condition, and several assumptions. The quasi-static approach for trailers is a novel expansion of the conventional approach to a two-axle vehicle. Proposed enhancements to the quasi-static approach reflect the additional rolling moment of a trailer. The curvature and the cant of a curved road were accounted for via centrifugal forces. Both uniform and turbulent wind conditions were considered in questioning the feasibility of this novel approach.  
vehicle; safety; wind; quasi-static; TruckSim; road +
Footnote †: journal: Applied Sciences