esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 3 Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme  
In this section, we proposed a fuzzy logic and bio-inspired firefly algorithm based routing scheme. The main objective of our proposed scheme is to increase the probability of enriched information transmission while maximizing the lifetime of implanted NBSs. Our proposed scheme realizes the goal of energy-efficient data routing by dividing the data transmission process from NBSs to nanorouter into three different phases, briefly explained in this section. Furthermore, Figure 2 also elaborates on the complete working of our proposed scheme.  
* First Phase: In the first phase, nanorouter nominates information and energy enriched regions from the accumulated number of correlated regions--configured during network setup time. The Fuzzy logic-based decision ensures the selection of only those regions that have maximum enrichment values for energy and information. The selection of correlated regions supports in achieving the ultimate goal of important information retrieval with balanced energy consumption. After the selection of participating correlated regions for data reporting, nanorouter broadcast request messages \((P_{RqM})\) only for selected correlated regions. Those correlated regions that are excluded from data reporting cannot generate feedback message \((P_{FB})\) and response message \((P_{RM})\). Avoiding data aggregation from correlated regions that have not valuable information results in evading unnecessary data transmission and saving of energy resources.
* Second Phase: In the second phase, NBSs selection is carried out using the firefly algorithm to support further the objective of reduced redundant data transmission, increased probability of retrieving crucial information and prolonged lifetime of NBSs. Since NBSs located in the correlated regions are assumed to be spatially correlated, which transmit similar types of information. Therefore, the selection of NBSs using the proposed firefly algorithm from selected correlated regions minimize aggregation of the same information. For selecting NBSs, nanorouter determines the fitness of NBSs using the information transmitted by the NBSs in the feedback messages \((P_{FB})\). The feedback message \((P_{FB})\) contains the information about the priority bit (i.e., the value set to 1 or 0 according to the criticality of the last transmitted message) and residual energy of the NBS. The proposed fitness function ensures the selection of those NBSs for data reporting that have maximum residual energy, minimum distance from the nanorouter and have valuable information. These fitness criteria lead to improved network lifetime of NBSs and increase the probability of receiving important information.  
* Third Phase: After selecting NBSs, nanorouter broadcasts announcement message \((P_{AM})\) to notify selected NBSs to generate response messages \((P_{RM})\). Finally, in the third phase, selected NBSs transmit response messages \((P_{RM})\) to the nanorouter.
## 7 Discussion and Conclusions
### Contrasting Discrete and PFM Crack Representation Approaches
Extending the discrete crack methods to exhibit similar capabilities involves significant algorithmic changes, as these codes are custom and not readily extendable to further types of analysis. Furthermore, extension to 3D problems is not straightforward, in addition, the definition of crack propagation increment in 3D is difficult to specify. Furthermore, judging if a crack arrests or the method simply does not permit continuation across obstacles, requires expert knowledge. **Author Contributions:** The idea for this review emerged from discussions held between E.C. and S.P.T. ; A.E. was responsible for the SBFEM source code implementation and simulations, the discussion on the merits and drawbacks of the examined methods, and the consistent presentation of the results; K.A. was responsible for the XFEM source code implementation and simulations; U.P., E.K., and S.P.T. were responsible for the phase field source code implementation and simulations. E.C., S.P.T., and I.A.A., advised on the simulation methods, the manuscript organization, and contributed in the discussion of the results. The first three authors contributed equally to paper writing. **Funding:** This research was performed under the auspices of the Swiss National Science Foundation (SNSF), Grant # 200021_153379, A Multiscale Hysteretic XFEM Scheme for the Analysis of Composite Structures. Konstantinos Agathos would like to acknowledge funding received from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 795917 \"SiMAero, Simulation-Driven and On-line Condition Monitoring with Applications to Aerospace\". Udit Pillai, Savvas Triantafyllou, and Ian Aschroft would like to acknowledge funding received from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie SAFE-FLY project, grant agreement No. 721455. **Acknowledgments:** The authors are grateful to the University of Nottingham for access to its High Performance Computing facility and extend their gratitude to the group of Song from UNSW and in particular Albert Saputra for his guidance on SBFEM implementation. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
###### Abstract
The corresponding coupled governing equations were derived using the generalized Hamilton principle, laying the foundation for subsequent in-depth research. After this, the finite element simulation was performed in the COMSOL software to simulate the output voltage, stress distribution, and resonance frequency of the PEH-C under different curvatures. The impact of the changes in curvature on the PEH-C were then analyzed. Finally, an experimental platform was set up to verify the correctness of the finite element analysis, which guided the subsequent structure optimization.
# Metric Embedding Learning on Multi-Directional Projections
## 3 Methodology
### Multi-Directional Projections  
As defined in the paper introducing the MDIPFL transformation [19], the length of the output projection functions are equal and adjustable by setting the so-called bin number. Therefore, the sinusoid effect, observable in Radon space and which is caused by projecting rectangular shapes, is eliminated.  
The size of the output matrix is \(N\times\left\lceil\frac{\pi}{\mathtt{Step}}\right\rceil\), where \(N\) stands for the bin number, and \(\mathtt{Step}\) represents the rotation increment after each projection. Rotations are interpreted in range \([0;\pi]\), as rotations in \([\pi;2\pi]\) are exactly the same, but mirrored [50]. The intensity values of the elements are summed, including the partially covered elements. The method is visualized on Figure 4.  
The computational complexity is only dependent on the input image size and the \(\mathtt{Step}\) variable. parallel processing is possible, as the calculation of each projection angle is independent. Geometric decomposition based parallel execution is also possible, making GPU-enabled data-parallel implementation possible [51]. For further details on the algorithm and possible implementations, refer to our earlier work [19].  
Figure 3: Samples of the Radon and MDIPFL transformations: from left to right the original images, the Radon transformations and the MDIPFL transformations, respectively. Both projection transformations show the projection profiles on range \([0;\pi]\). The original images are samples from the NIST [49] dataset. Subfigure (**a,b**) show observations from the same class, subfigure (**c**) is different.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 4 Experiments and Results
### Data Normalization  
After pre-processing the sensor data, 12,852 groups of chronological data are used in constructing the improved EMD-based hybrid LSTM forecasting model. The collected water quality data are divided into two parts: the first 75% of the dataset is used for training the developed hybrid model and the last 25% of the dataset is used for model testing in order to analyze the forecasting performance of the proposed novel hybrid EEMD-based LSTM model. The collected raw data are shown in Figure 3. The structural representation of the data set can be expressed as follows:  
\[X=\left[X_{t,t}\right]_{N\times M}=\left[\begin{array}{cccc}x_{11}&x_{12}& \cdots&x_{1m}\\ x_{21}&x_{22}&\cdots&x_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ x_{n1}&x_{n2}&\cdots&x_{nm}\end{array}\right] \tag{14}\]  
where \(X\) represents the data matrix at a given time step within the time series, \(x_{ij}\) represents the \(j^{th}\) feature of \(i^{th}\) week, \(N\) and \(M\) denote the length and number of features of the time series data, respectively. Finally, the normalization of the data set was done by removing the dimension of each feature using normalization equation, \(\overline{x}_{ij}\):  
\[\overline{x}_{ij}=\frac{x_{ij}-\text{mean}(x\cdot j)}{\text{std}(x\cdot j)} \tag{15}\]  
where \(\overline{x}_{ij}\) denotes the normalized data set, \(\text{mean}(x\cdot j)\) represents the mathematical expectation of \((x\cdot j)\), and \(\text{std}(x\cdot j)\) represents the standard variance of \((x\cdot j)\).
# Magnetic Resonance Roadmap in Detecting and Staging Endometiosis: Usual and Unusual Localizations
## 2 Indications for MRI  
Nowadays, in the literature there is no consensus on the indications of MRI for patients dealing with pelvic disorders in premenopausal age. MRI is often required in cases of dysmenorrhea, dyspneauria, difficulty conceiving, evaluation of chronic pelvic pain, or unrecognized adrenal masses [11]. However, the most relevant indication for MRI in this age range remains the evaluation and staging of deep pelvic endometriosis, defined as at least one structure involved, such as the uterosacral ligaments, vagina, rectosigmoid colon, rectovaginal septum or bladder, for a distance of 5 mm or more.  
In the literature, the value of MRI as a first-line clinical technique, compared to transvaginal ultrasound, is not confirmed [12]; in fact, several studies underline that the first approach method in the evaluation of suspected endometriosis should remain transvaginal ultrasound, especially for ovarian and bladder endometriosis and for rectosigmoid endometriic lesions [13].  
MRI is able to recognize past hemorrhagic content in endometriosis, and owing to its large field of view (FOV), spatial resolution and contrast imaging, can identify multiple dislocated endometrial implants [14]. It is, therefore, often requested as a second-line technique in the case of still symptomatic patients without any ultrasound findings [15], for patients undergoing surgery for pre-operative staging and mapping of the implants, or in case of difficult diagnosis [16]. Only a few recent studies suggest the use of MRI as a triage test in the diagnosis of rectosigmoid colon endometriosis and as a first-line examination in women with a high suspicion of intestinal endometriosis [17].
## 3 Methodology
### Overview and Design Principles  
Figure 1 illustrates the overall process of our two-step methodology incorporating a dual attention Transformer. The approach takes API calls as its input and produces predictions for the respective categories. In the first step, a multi-head attention mechanism and local attention are employed within a multiple dual Transformer encoder to capture and represent samples as vectors. Following activation, the model yields probabilities indicating the likelihood of each sample belonging to specific categories for predictive purposes. Moving to the second step, the model treats API call classification as a binary classification task to train individual models, leveraging the pre-trained model from the initial step. Subsequently, the methodology stores the final weights of these models as Normal Vectors, serving as representations for the respective classes and facilitating future predictions. This two-step strategy enables us to proficiently accomplish the malware classification task, optimizing it effectively, with each step addressing distinct optimization objectives.  
To meet the security design requirements, the method applies some principles [39] to work, including economy mechanisms, open design, and input validation. We keep the overall architecture consistent with the Transformer, with the only introduction of the local attention module to avoid the complexity caused by excessive modifications. Then, we designed a two-step process to optimize different purposes independently, thus maintaining the clarity and openness of the algorithm. We make assumptions on the input, so we need to apply the validation, including checking if the API call is legal from the system library and validating the input of the API pair for the model. In addition, more privilege validation and fail-safe default design principles need to be considered when the algorithm becomes part of a secure detection system in the future.
## 1 Introduction  
Aflatoxins (AF) are mycotoxins produced by some species of _Aspergillus_ fungi. It is well known that these molecules are capable of causing death when consumed at high doses and that they co-relate with the occurrence of hepatocellular carcinoma [1]. There are four major naturally produced aflat [2]: B1, B2, G1, and G2 (See Figure 1). AFB1 is the most common aflatoxin found in foods. It has relevance in health problems associated with mutagenesis and carcinogenesis [3]. Due to the amount of information showing that AFB1 increases the risk of hepatocellular carcinoma (HCC) [4], this aflatoxin is classified by the International Agency for Research on Cancer (IARC) as a carcinogen agent of Group 1.  
Aflatoxins are metabolized by cytochrome P450 (CYP450), mainly by CYP3A4 and CYP1A2 enzymes. The double bond in the furan ring of AFB1 and AFG1 suffers an epoxidation reaction producing an exo-8,9-epoxide, which can bind to DNA, forming adducts that are linked to hepatocellular carcinoma [5,6]. CYP450 is a superfamily of hemoproteins with catalytic functions, which makes molecules more hydrosoluble and polar in order to be excreted [7]. Around 95% of all organic chemical compounds that are metabolized (oxidation/reduction reactions) are developed by CYP450 [8].  
The objective of this study was to describe the active site of the CYP3A4 and CYP1A2 enzymes with aflat B1, B2, G1, and G2 by molecular docking analysis. In general, this computational technique predicts the most stable conformation of a molecule when it is joined to another, thereby providing an idea of how these molecules can potentially bind in vivo or in vitro [9]. The electron transfer (\(\Delta\)N) inside CYP450 is recognized as an essential process in different metabolic pathways. Thus, modeling the interaction process of the ligand within the activated catalytic cycle provides a reliable approximation of the intermolecular characteristics of the oxidative process [10,11].  
In this work, the geometry optimization of the aflat B1, B2, G1, and G2 with two different theoretical methodologies is reported. These geometries were used for molecular docking analysis, predicting the possible electrostatic interactions, the amino acids of the active site, and the binding energies with two proteins: CYP3A4 and CYP1A2. AFB1 was analyzed with several PDB IDs for CYP3A4 to identify the main differences in the active site of the crystallographic structures of the protein. Afterward, single point energy calculations were performed in order to obtain the chemical reactivity parameters. The ionization potential (I), the electron affinity (EA), the chemical potential (\(\mu\)), and the chemical hardness (\(\eta\)) were used in the determination of charge transfer in the oxidative process of AFB1 inside the protein. The calculation pathway used for quantum chemical calculations and molecular docking analysis is shown in Scheme 1.
## 2 Materials and Methods
### Outcome Measures  
#### 2.3.1 Primary Outcome: Sensory Function Improvement  
The primary outcome of this study was the degree of sensory function recovery, assessed by the change in the normalized VAS ratios between the initial and final clinical visits. VAS values were calculated by dividing the patient-reported sensory intensity on the affected side (relative to the contralateral, unaffected side, defined as 100%) by 100. A ratio of 1.0 indicated normal sensation; values below 1.0 reflected sensory loss (hypoesthesia), and values above 1.0 indicated sensory gain (hypeesthesia or allodynia).  
To address distributional skewness, changes in the VAS ratios were log-transformed. Positive log-transformed values indicated an improvement toward normal sensation, whereas negative values indicated a persistence or worsening of the abnormal sensory perception.
## 5 Discussion
### Limitations and Future Work  
A limitation of this study is its narrow focus on a single company's work processes. Despite the value of presented asymmetry to industrial scenarios, the influence of asymmetry in distributed settings should be further explored in the work processes of other large manufacturers, as well as in other fields and contexts. Additionally, further work on using asymmetric VR, and especially a comparison of collaborative activities in multi-user distributed VR and in similar asymmetric VR, would shed more light on the topic, since both setups have advantages, limitations and application scenarios [60]. Future research may also look into advancing the scalability of the approach and expanding the asymmetry towards portable devices.  
To further advance the communication and teamwork quality, it is critical to address the lack of immersion and interaction of the non-HMD user. Additionally, it is critical to identify resource-efficient ways to increase the sense of co-presence when merging the use of VR and traditional conferencing tools. Our results demonstrated the desire to obtain at least some level of control, which suggests the use of VR user interfaces designed for 2D screens. In this case, it is not obvious to what extent freedom of interaction and control for non-HMD users would affect the collaboration practices. The symmetry between 2D and 3D VR asymmetry when applied remotely in an industrial context is another topic to investigate, since it holds the potential to advance the feel of co-presence without massive costs.
## 4 User eXperience Analytics (UXA) Evaluation Results
### Fourth UXA: Logistics and Set-Up  
The fourth UXA refers to all those issues related to the logistics and hardware set-up of the holographic showcase inside the museum context. This includes the position and its conditions of use (including chairs, sound systems, and interaction interfaces) (Figure 10).  
This tried to answer the following research questions: \"How comfortable is it for visitors to access the contents? What about visibility of the showcase along the museum visit path and users' comfort?\"  
This UXA clearly showed (Table 6) how the logistics and the set-up of the museum installation influenced the user experience, particularly its position and location inside the exhibition. In APM and BCM, the holographic showcase was in the middle of the museum visit path, and for different reasons, the logistics did not favor a relaxed and absorbed experience in both cases. In BCM, it was in the center of the path, and in APM, it was along the path but in a hidden corner in a very narrow space near the way out. In this second case, the visibility of the entire structure was not always recognizable, and users ended up discovering the showcase by chance (since no indications or signals were available in the room or on the museum guide). Furthermore, the possibility to have seats, benches, or chairs was stressed as relevant to the users. In BCM, their presence was appreciated by users, while in APM and LVR, people complained of their absence. Instead, in HNM, chairs were not suitable for the type of user experience, since the holographic showcase was integrated in a big projection wall. Digital contents sometimes appeared inside and sometimes appeared outside the showcase, and people had to walk a few steps backward in order to enjoy the outer projection from a longer distance. Sound was another issue, even if the style was appreciated by 75% of the users. The problems pertained to the volume and the diffusion. In APM, we had the sound shower, which was very limited in the mono audio quality, even if it was easy to install. In BCM, we had speakers, but this conflicted with another multimedia application located directly at the back of the holographic showcase, producing overlap and noise in the room. In LVR, we had two sets of headphones, but this limited the experience of the showcase to only two persons at a time, even if silence was maintained in the museum room. Another problem in LVR was that the headphones for the holographic showcase were not the same ones used for traditional guided tours, so the same visitors did not immediately understand that they had to change devices. Finally, language selection was considered very good by users, with 88% positive feedback.  
Figure 10: Holographic showcase’s disposition and accessories.