esa-sceva/satcom-chunk-collection huggingface dataset:
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 4 Human-Machine Interaction (HMI)
### Key Enabling Technologies and Goals of HMI  
Key enabling technologies of human-machine interaction (HMI) include [32; 33]:  
Figure 6: Human perception: context- and situationally dependent (adapted from [31]).  
Artificial Intelligence (AI) and Machine Learning (ML): these technologies enable machines to learn from data and adapt to user behavior, making interactions more personalized and efficient;  
**Natural language processing (NLP):** NLP enables machines to understand and respond to human language, making interactions more natural and intuitive;  
**Robotics:** robotics technology involves the design, construction, and operation of robots to perform tasks in a wide range of settings;  
**Computer vision:** computer vision allows machines to perceive and interpret visual information, enabling them to recognize objects and understand gestures;  
**Haptic technology:** haptic technology provides tactile feedback to users, enhancing the sensory experience of interacting with machines;  
**Augmented Reality (AR) and Virtual Reality (VR):** AR and VR technologies enable users to interact with virtual objects and environments in real time;  
**Internet of Things (IoT):** IoT technology involves connecting physical devices to the Internet, enabling them to exchange data and interact with each other.  
The goals of HMI include:  
**Improve efficiency:** the goal of HMI is to improve efficiency and productivity by automating routine tasks and augmenting human capabilities;  
**Improve user experience:** HMI aims to create intuitive and user-friendly interfaces that make it easy for people to interact with machines;  
**Increase safety:** HMI strives to improve safety by reducing the risk of accidents and errors in high-risk industries such as aviation, transportation, and healthcare;  
**Enable personalization:** HMI enables machines to adapt to individual preferences and behavior, providing a personalized experience for each user;  
**Foster collaboration:** HMI aims to foster collaboration between humans and machines, enabling them to work together to achieve common goals.  
Additionally, in the Industry 4.0 era, we encountered a wide variety of human-technology interactions (HTIs); any time a human uses technology, there is some type of hardware and/or software involved that enables and supports interaction. HTI concentrates on the aspects in which technologies facilitate the interaction between the human and the environment. An important goal of HTI is to develop principles and algorithms for autonomous systems to enable safe, direct, effective, and trustworthy interaction with humans [34]. Figure 7 depicts the comparison between HCI, HMI and HTI whereby the current new technologies and trends not only are assisting in the human-machine interface, but also have moved towards interaction and interfacing technology between humans and autonomous systems.  
Figure 7: Comparison between HCI, HMI, and HTI (adapted from [35]).
# Biological and Technical Challenges Related to Cultured Meat Production
## 4 Technical Challenges in Cultured Meat Production
### Scaffold Fabrication
#### 4.1.2 Non-Conventional Porous Scaffold Fabrication Technologies  
* **Electrospinning**[78]: The method is based on an electric field generated between a polymer solution delivery system at a controlled flow rate and a collector, drawing the solution into a fiber, an illustrative example is shown in Figure 3. The result is a membrane of non-woven fibers. The textile-based technique has been created to reproduce fiber-based materials, such as those similar to the extracellular matrix (ECM). The resulting porosity is interconnected and the achievable pore size is lower than that by other scaffolding techniques, achieving fibers with diameter up to a few nanometers [78], which can be an advantage for specific applications (e.g., vascular [79]), but tends to limits the cell migration to a point where its applicability in tissue engineering becomes a problem. Several process parameters can be controlled to tune fiber diameter and alignment, adapting textural properties to the specific cell type to be seeded.
* **Self-assembly**[81]: The technique involves specifically designed amphiphilic peptides with the capacity to spontaneously organize into ordered structures, including nanofibers. The method allows a great process control starting from the building blocks in a bottom-up design approach for tissue engineering application.
* **Hybrid scaffolds:** With the aim of controlling structural and composition features, mainly porosity, at different length-scales, several approaches with mixed techniques are used, such as SCPL and electrospinning combination [82], multilayer electrospun composites with different parameters [83], and a combination of more than two fabrication techniques [84,85].  
Figure 3: Illustrative example of the electrospinning method for scaffold fabrication and image of a construct (adapted from [80]).  
* **Additive manufacturing (AM):** The conventional techniques of porous scaffolds fabrication, as well as other emerging alternatives, are implemented to produce scaffolds to recreate the complex micro and macrostructures of biological tissues. However, all of them have limitations and allow narrow control over important textural parameters such as pore shape, dimensions, and interconnectivity [86]. An emerging family of technologies, based on additive AM techniques, has proved to enable the manufacture and control of complex shapes. AM, popularly called 3D printing, is a generic definition and represents a large group of processes that can be classified in several ways [87]. Within the porous scaffold fabrication context, according to [PERSON] et al., some of the AM technologies could be used to produce scaffold with a high spatial resolution, the structural complexity, and control over the internal pore architecture. The most promising techniques are powder-bed based 3 DP, such as selective laser sintering (SLS), and liquid raw-material-based 3 DP, such as stereolithography (SLA) [86].
## 3 Results and Discussion
### Bacterial Characterization
#### 3.1.2 Biolog  
BIOLOG microplate system can be applied as one of bacteria identification tool, as well as to screen carbon source utilization pattern. BIOLOG exploits the principle of substrate utilization by coupling metabolic activity to a simultaneous reduction of a redox dye. The reduction of redox dye was measured colorimetrically within a 96 wellled microplate. The substrate utilization spectrum was then compared with internal database for possible identification [23]. In this study, two distinctive BIOLOG microplates were used. Gen III microplate is designed to identify pure culture of aerobic Gram-negative and Gram-positive bacteria. Each Gen III microplate contains 23 wells of chemical sensitivity assays, 71 wells of carbon source utilization assays and 2 wells of control assays. AN microplate is designed for identification of a very wide range of anaerobic bacteria. Each AN microplate contained 95 discrete carbon source utilization assays and one well with water as negative control. The isolate reacted with each assay in order to provide metabolic and phenotypic fingerprint patterns of the microorganism in order to identify it at the species level. However, due to system database limitations, some of the atypical isolates were not identified at the species level using this method, i.e., the system will not identify bacterial species that are not classified in the database. As a result, isolate KBH 6958, a new strain, was not identified directly from both Gen III and AN microplates. Based on the existing database, the closest bacterial similarities with KBH 6958 found from Gen III microplate and the AN microplate patterns were _Enterobacter aerogenes_ and _Clostridium aminovalericum_, respectively. Isolate KBH 6958 definitely did not belong to genus _Clostridirium_, as the _Clostridium_ was spore-forming, Gram-positive, and strictly anaerobic bacterium [10], whereas isolate KBH 6958 did not form spores, is Gram-negative and can grow in aerobic or facultative anaerobic conditions. Nonetheless, this method was useful for rapid screening of a large amount of substrate for bacterial growth. Table 1 outlines the carbon source utilization pattern of isolate KBH 6958. The information is presented as a positive threshold value (0-100%), with a higher value indicating a more favorable carbon source for bacterial growth. This result can also be used to investigate the effects of different substrate structures and functional groups on its suitability as a carbon source for isolate KBH 6958.
# Post-Quantum Authentication in the MQTT Protocol
## 2 Problem Statement
### KEM Used for Authentication  
Our proposal for using KEM for authentication in MQTT is based on the proposed change to TLS found in [23]. KEM was proposed as an option to provide faster authentication than with postquantum digital signatures. The authors claim that there is a reduction of 90% in CPU cycles when KEMs are used.  
The interaction for the handshake when authentication is provided by a KEM rather than a digital signature is slightly different and is outlined in Figure 3. This interaction is over a public MQTT channel. The client first generates an ephemeral secret and public key pair \(sk_{e}\), \(pk_{e}\). The client then sends a ClientHello message with a random nonce, \(r_{c}\), and their public key. The broker then encapsulates an ephemeral shared secret, \(ss_{e}\), using the public key and sends it to the client along with a random nonce, \(r_{b}\), and their own public key, \(pk_{s}\). The broker's public key is signed by an authority to verify that it does indeed belong to the intended broker (this signature cannot be avoided). The client verifies the signature and encapsulates a static shared secret, \(ss_{s}\), with the broker's public key, and sends this to the broker.  
The broker decapsulates the shared secret using their secret key, \(sk_{s}\), and then both the client and broker generate several keys based on the shared secret. They then exchange HMACs of the previously exchanged messages and the final key to ensure that both parties have received the same messages and have calculated the same keys. At this point, the client has authenticated the broker and the client can be sure they are connecting to the broker they intend to. The rest of the MQTT protocol can continue as normal.
## 5 Experimental Results and Simulations
### Impact of Feature Selection Technique  
For the final part of the experiment, we evaluate the impact of the feature selection on sleep apnea classification. This sub-section employed the Relief method as a filter feature selection to select the significant attributes. The Relief method works by evaluating the quality of the features based on its ability to classify instances from one category to another in a local neighborhood. For example, the most valuable features can increase the distance between different class instances. In contrast, those features have less contribution to improving the distance between same class instances [46]. In other words, the Relief method can handle multi-class, noisy, and incomplete datasets.  
Figure 9 shows the weight results of the Relief method. Note that the greater the weight, the higher the discriminative power of the feature is. Figure 9 shows that the eighth features had the highest importance while the ninth features provided the lowest weight. Table 9 explores the performance of the number of features using a different set of features. Based on the reported results, the ensemble DT* classifier's overall performance has been improved considerably. Our finding indicates that the highest accuracy of 74.56% was achieved with eight features.  
Figure 8: Ensemble vs. KNN.  
Figure 9: Comparison between KNN* and ensemble DT* based on the best oversampling ratios.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 5 Experiments
### Ablation Study  
#### 5.6.1 Necessity of Independent Camera Feature Extractor \(E_{\text{C}}\)  
When trying to extract camera style features, the authors first tried to add a new branch at the identity feature extractor. However, the re-ID accuracy drops dramatically. That is because person identity classification and camera style classification are two different tasks, and they cannot be fulfilled in the same network. The gradient brought by the camera style classifier affects the identity classification. Then, the authors tried to stop the back propagation of the camera style classifier to eliminate its influence to identity classification, but the accuracy of camera style classification decreased. Because the original feature extractor is trained to extract person identity features, camera style information might be lost during training. Since the camera style feature extractor is the input of the generative model, the quality of the camera style feature is crucial for the CST model. The authors decide to add a light-weighted independent feature extractor \(E_{\text{C}}\) to the framework. The comparison with the former setting shows that by introducing an independent feature extractor \(E_{\text{C}}\), the camera classification accuracy can reach almost 100%, and the quality of the identity feature is not influenced.  
This study is carried on the IDE baseline; the camera style classification accuracy and re-ID results are listed in Table 4, where 'branch' means adding a branch to the identity classifier,'stop bp' means stopping the back-propagation of the camera classifier, and 'independent' means using an extra independent classifier for the camera style.
# The Smart Buildings Revolution: A Comprehensive Review of the Smart Readiness Indicator Literature
## 4 Results and Discussion
### Research Gaps and Future Directions in the SRI's Dominant Themes
#### 4.2.5 Research Gap in SRI's Expansion to Neighborhoods and Districts  
Expanding the SRI to neighborhoods and districts offers an opportunity to scale its impact from individual buildings to larger urban environments, fostering more sustainable and energy-efficient communities. [PERSON] et al. [64] recommended that future research should prioritize the development of a comprehensive reporting framework, mirroring the levels specified for building sustainability. This framework should specifically focus on implementing smart solutions for energy conservation within neighborhood building stocks.  
[PERSON] and [PERSON] investigated the relationship between SRI scores and carbon emissions in the Nordic context to understand why the highest level of smartness does not always lead to reduced carbon emissions [13]. Despite Nordic countries demonstrating a strong commitment to climate change mitigation, their lifestyle-based carbon footprints and consumption are among the highest globally [81].  
[PERSON] and [PERSON] [51] suggested several avenues for advancing the assessment of the load-shifting potential in smart districts. First, further refinement and validation of the proposed quantitative methodology and SRI models through empirical studies in diverse settings are needed. Second, research should enhance the accuracy and timeliness of building and energy data inputs to improve the assessment reliability. Third, integrating emerging technologies into the SRI framework could better align it with evolving energy systems. Additionally, studies should examine the socio-economic and policy impacts of load-shifting strategies, including cost-effectiveness, stakeholder acceptance, and regulatory considerations. Finally, efforts should focus on creating decision-support tools and guidelines to help policymakers, stakeholders, and urban planners optimize energy management and foster sustainable development in smart districts.  
[PERSON] et al. [82] proposed future directions for the demand response, emphasizing the incorporation of additional variables such as indoor activities, outdoor weather conditions, window openings, and wind incidence. Additionally, integrating various building systems, including lighting, climate regulation, mobility, and security, is vital for a more comprehensive understanding of the complexity of the built environment in the context of the demand response.
## 5 Conclusions  
This article presents the application of multi-user interactive evaluation to the UA-FLP problem. To the best of our knowledge, this application has never been used in this kind of problem. Nevertheless, the multi-user evaluation strategy could be applied similarly to other kinds of problems where the collaborative intervention of several human designers is needed. The multi-user intervention has been proven to be as useful in a synchronous (or _alternative_) as in an asynchronous (or _sequential_) way, properly capturing the designers' personal requirements in both cases.  
Furthermore, the users' intervention is only required for a short number of solutions since they are grouped by similitude between them, and they are only needed for a reduced number of generations along with the algorithm evolution, since the algorithm evolves by itself during a predetermined number of generations from the moment at which the users' preferences are captured. In this way, the humans' intervention is reduced to the minimum possible to reduce their fatigue.  
The proposal has been tested using three problem instances taken from the literature. The three cases refer to the layout distribution of realistic industrial plants, including an industrial slaughterhouse and two recycling plants for cartons and plastic. In the three cases tested, the algorithm has been shown to be able to yield good results for the qualitative fitness function but adding the personal preferences of three expert designers and with a moderate intervention of the human designers.  
Future research should be devoted to further reducing the designers' fatigue by attempting to incorporate a more automated means of capturing their preferences. In the same way, new techniques of incorporating the combined evaluation of the DMs can be implemented. Finally, more investigation could be conducted about the way in which the DMs intervene during the algorithm's evolution, attempting to obtain more definitive conclusions about the influence of the order in which they act over the algorithm, as well as implementing a ubiquitous synchronous method via the web for capturing several DMs' preferences.  
Figure 18: Best solutions found in _Sequential_ launch 4 to 5 of _ChoppedPlastic_ problem.  
**Author Contributions:** Conceptualization, [PERSON]-M. and [PERSON]-[PERSON]; methodology, L.S.-M.; software, C.C.-M.; validation, L.S.-M., [PERSON]-H. and C.C.-M.; formal analysis, L.S.-M. and [PERSON]-H.; investigation, L.S.-M., [PERSON]-H. and C.C.-M.; resources, L.S.-M.; data curation, C.C.-M.; writing--original draft preparation, L.S.-M.; writing--review and editing, L.S.-M.; visualization, L.S.-M. and [PERSON]-H.; supervision, L.S.-M. and [PERSON]-H.; project administration, [PERSON]-M. and [PERSON]-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussions
### Parametric Analysis
#### 5.1.2 Effects of the Turbine Inlet Temperature (T\({}_{5}\))  
The effects of \(T_{5}\) on \(\eta_{ex}\), \(c_{total}\), \(COPP\), and \(COHP\) are displayed in Figure 4 for four thermal systems. Clearly, \(\eta_{ex}\) increases with \(T_{5}\) while \(c_{total}\) shows an opposite variation trend. It indicates that increasing \(T_{5}\) could be beneficial to the improvement of system performances for these thermal systems. Figure 5 depicts the net output power \(W_{\text{net}}\), the temperature of stream 8 (\(T_{8}\)) and the mass flow rate of 8a (\(m_{\text{8a}}\)) varying with \(T_{5}\) for the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system. When \(T_{5}\) ascends, the working capacity of unit mass working fluid is strengthened, and \(W_{\text{net}}\) increases correspondingly. As a result, \(COPP\) in Figure 4b also increases with \(T_{5}\). On the other side, the increase of \(T_{5}\) enlarges the heat absorption amount of the unit mass flow rate CO\({}_{2}\) from the reactor, so that the mass flow rate of CO\({}_{2}\) decreases. Correspondingly, \(m_{\text{8a}}\) decreases with the increase of \(T_{5}\), as shown in Figure 5. It means that the mass flow rate of CO\({}_{2}\) flowing into AHP and DHE decreases, which in turn decreases the heat absorption amount of AHP and DHE from the topping cycle. At the same time, \(T_{8}\) increases with \(T_{5}\), so that the generator temperature ascends and the heat absorption amount of AHP and DHE from the topping cycle will increase. The opposite effects between \(T_{8}\) and \(m_{\text{8a}}\) makes the \(COHP\) of sCO\({}_{2}\)/AHP systems change slightly, as shown in Figure 4b. Thus, the increase of \(\eta_{ex}\) with \(T_{5}\) is dominated by the increase of \(COPP\), i.e., the power generation capacity of sCO\({}_{2}\) topping cycle, while the increase of \(T_{5}\) exerts slight influences on the performances of the bottoming cycle. As the increase of \(T_{5}\) results in the decrement of  
Figure 3: Variation trends of \(W_{\text{tur}}\), \(W_{\text{com}}\), and \(W_{\text{net}}\) with \(PRc\) for the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system.  
mass flow rate of working fluid, the capital investment costs for the turbine, compressors, and pressure vessels decrease correspondingly and lead to the decline of \(c_{total}\), as shown in Figure 4a.  
The following Figure 4 shows the variation trends of system performance indicators with \(T_{5}\) for different thermal systems: (a) \(\eta_{ex}\) and \(c_{total}\); (b) \(COPP\) and \(COHP\).  
The following Figure 5 shows the variation trends of \(T_{8}\), \(m_{8a}\), and \(W_{\rm net}\) with \(T_{5}\) for the sCO2/LiBr-H2O AHP system.  
Figure 4: Variation trends of system performance indicators with \(T_{5}\) for different thermal systems: (a) \(\eta_{ex}\) and \(c_{total}\); (b) \(COPP\) and \(COHP\).
## 1 Introduction
It supports more effective resource allocation and emergency planning, particularly in complex scenarios where traditional models fall short. This integration offers a comprehensive framework for improving disaster resilience and optimizing resource deployment. Specifically, the proposed research makes contributions in the following areas:  
* The Generalized Additive Model (GAM) based framework enables to capture complex, non-linear spatial and temporal evacuation patterns, addressing gaps in traditional static forecasting models and providing real-time insights into evacuation trends and shelter utilization hotspots for emergency planners. * Improved Shelter Demand Prediction Accuracy: The proposed model significantly outperforms the baseline Generalized Linear Model (GLM), reducing prediction errors (RMSE) and improving correlation with observed shelter demand for both shelters and lodging facilities. * Consideration of Non-Congregate Shelters During Hurricane-Pandemics: Unlike most existing studies that focus on congregate shelters (e.g., schools, community centers), we examine hotels and lodging facilities as alternative non-congregate shelters, crucial for mitigating pandemic-related risks. * Impact Analysis of School Closures on Evacuation Patterns: We quantify the influence of school closures on evacuation behaviors and shelter demand, providing key insights for policymakers in adaptive disaster planning.