esa-sceva/satcom-chunk-collection huggingface dataset:
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 3 System Functionalities
### Data Sensing, Storage and Processing
Semantic classification of seabed textures and structures requires computationally intensive texture analysis algorithm based on computer vision or deep learning techniques [78; 79; 82], but it enables the possibility of sending only relevant compressed streams to the RMCC. Onboard image processing simplifies also the task of analysing huge datasets, thus making the whole mission faster and more responsive.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## Overall Architecture of the Proposed ME System
### High-Performance Computing Resources Management for CPU-Intensive ME Apps  
One of the differentiating aspects that characterizes the proposed ME system is the presence of High-Performance Computing resources (i.e., GPUs) in the NFVI that hosts the ME apps. To manage such resources, the presented system exploits the PCI passthrough mode [25], and the standard capabilities offered by the OpenStack VIM; in particular, it relies on OpenStack flavors [38], which are used to define the compute, memory, and storage capacity of a virtual server.  
To manage HPC resources in the NFVI, two preliminary steps must be carried out. First, in the Openstack VIM, the descriptor of HPC-equipped compute nodes must signal the presence of GPUs, by including an ad hoc referencing alias [38]; then, a specific OpenStack flavor containing the same alias must be created.  
The VNFD of any VNF that could require GPU resources then has the possibility to indicate that specific flavor and thus require a compute node equipped with GPUs for the execution of the described VNF.  
For the sake of brevity, we will skip further details, since flavor management in OpenStack and its use in VNFD is a well-established topic, which is out of the scope of this paper. Interested readers can refer to Reference [38].  
The use of HPC resources in the NFVI makes it possible to combine the advantages offered by NFV with high computing performance levels even in the presence of energy consumption or space occupation constraints, as will be shown in Section 5.  
Figure 7: Data plane architecture based on Local Break Out at SGW.
# Physiological Study of Visual and Non-Visual Effects of Light Exposure
## 1 Introduction  
Light induces both visual and non-visual effects on humans [1]. Visual effects are processed in the visual cortex of the brain via the lateral pallidum. These are accompanied by the perception of light color and a subjective impression of light, directly affecting emotion [2]. In fact, humans perceive physical characteristics such as the size, shape, and motion direction of objects through light. Such visual perception not only changes emotions but also influences human psychology and physiology [2, 3]. Red light is reported to have a more arousing effect than blue light [2, 4, 5]. In contrast, non-visual effects are processed in the hypothalamus via the suprachiasmatic nucleus and the preoptic area. These effects do not involve the perception of light colors. Physical characteristics such as light intensity and wavelength affect physiology [6]. In fact, humans regulate circadian rhythms by detecting the physical characteristics of light, impacting self-reported arousal [6, 7], heart rate [8], body temperature [8, 9], brain activity [7], hormone secretion [10], and performance in cognitive tasks [11, 12]. Unlike visual effects, the non-visual effects of blue light are reported to have a more arousing impact than those of red light due to the intrinsic photosensitive retinal ganglion cells (ipRSCs), which respond strongly to blue light [7, 12, 13]. Previous studies [3, 7, 8, 12] have suggested that blue light impacts the humanarousal level (arousal/sedation). However, some studies [14; 15; 16] have shown the opposite results (arousal effects from red light). The difference may be due to the perceived color of light.  
In general, human emotions are quantified using sensory evaluations and physiological responses. Although subjective, sensory evaluations directly measure the psychological state. However, individual differences between participants are influenced by their personal preferences [17]. In contrast, physiological responses indirectly but objectively measure the psychological state. Previous studies on the emotions generated by light have measured physiological responses (e.g., brain activity [18; 19], heart rate [5; 20], and electrodermal activity [5; 21]).  
This study aimed to distinguish and clarify visual and non-visual effects. To compare physiological responses with and without light color perception, a lighting environment where it was difficult to perceive light colors due to chromatic adaptation and change blindness was constructed. Knowledge about these effects can be applied to the development of lighting devices to control users' arousal without considering their color preference (e.g., lighting devices used in space stations for controlling circadian rhythms and in working offices for improving work efficiency).  
The rest of this paper is organized as follows. Section 2 details the method used to measure and analyze the physiological responses. Section 3 describes the experiment on the visual and non-visual effects of light exposure, while Section 4 provides conclusions.
# Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity
## 7 Conclusions and Future Work  
Based on the experimental results, we can conclude the following:  
1. A trojan malware execution affects the network behavior at a given host, causing a significant reduction of the similarity value between real-time traffic and the profile. Therefore, our methodology is capable of triggering an alarm when the predominant behavior of the user starts deviating from the expected one.
2. An anomaly-based IDS that builds a profile for each individual network user represents an additional security mechanism because it is capable of detecting unexpected network behaviors that might be originated by a malware. The antivirus was not capable of detecting the installed trojan in either of our experiments.  
An anomaly-based IDS must update the profiles on a regular basis because the normal behaviors of users change periodically. The proposed profiling method relies on the creation of TopK lists instead of using a supervised classifier as other approaches do. Therefore, updating the profile is computationally viable because it does not involve a re-training process.  
Future work will go in two directions: (1) a dynamic profiling method capable of removing the least common behaviors from the profile, which might include the behavior induced by an attack that occurred during a previous profiling process; and (2) a decision model that triggers an alarm when an unexpected behavior is detected.  
Conceptualization, A.P.-P.; methodology, A.P.-P., I.P.-D. and F.C.; software, A.P.-P.; formal analysis, A.P.-P., I.P.-D. and F.C.; investigation, A.P.-P., I.P.-D. and F.C.; writing-original draft preparation, A.P.-P.; writing-review and editing, I.P.-D.; funding acquisition, A.P.-P.  
This research received no external funding.  
This work was supported in part by ITESO's Program for Academic Level Enhancement (Programa de Superacion de Nivel Academico, PSNA) through an assistantship granted to [PERSON].  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAN & Campus Area Network \\ IDS & Intrusion Detection System \\ LAN & Local Area Network \\ NIST & National Institute of Standards and Technology \\ WAN & Wide Area Network \\ _PB_ & Predominant Behavior \\ \end{tabular}  
Figure 12: Predominant behavior of user C during a full work week, highlighting unexpected behavior periods.
## 3 Results
### Microstructures
#### 3.4.2 As-Welded Microstructure  
For the Inconel 625 filler material, Figure 8 shows an inverse pole figure map (IPFM) image of the FZ and HAZ (a) and a high-angle annular dark field (HAADF) image of the FZ (b). This image shows that second-phase particles were distributed in the grain interiors and at the grain boundaries of the FZ (Figure 8b). No significant grain growth was observed in the HAZ. The IPFM of the Haynes 230 filler weldment exhibited a similar characteristic, so it is not shown here.  
In the FZ of the Inconel 625 filler material, the two main types of second-phase precipitates (SPPs) are Nb-, Ti-, and Si-rich MC carbide (face-centered cubic, or FCC) and Mo- and Si-rich intermetallic Laves phase (hexagonal close-packed, or HCP). For example, in Figure 9a, the SPPs at the grain boundary are an HCP Laves phase, verified by tilting them to different zone axes. Figure 9b shows the NBED pattern taken from the Laves phase at the zone axis of [0001]. The SSP in the grain interior is an MC carbide; Figure 9c shows the NBED from its [111] zone axis. Figure 9d-i provide the element EDX mapping of the studied region showing the difference in chemical composition between the two phases. Nano-sized MC carbide was also observed within the FZ. Figure 10 provides the HAADF image (a) and the element EDX mapping (b-f) of the region containing these nano-sized Nb- and Ti-rich MC precipitates. These carbides were embedded in a dense dislocation network, as shown in Figure 10a. The formation of dislocations likely occurred during the precipitation of MC, potentially contributing to the strengthening of the fusion zone for improved high-temperature properties. When comparing the SPPs in the FZ of the Inconel 625 filler weldment with those in the Haynes 230 filler weldment, it was observed that the majority of the SPPs in the latter are Ti- and Cr-rich carbides (Figure 11a-d) and W-Cr-Mn-Si-rich carbides (Figure 11e-h), and some of them are at nano-size scales (less than 100 nm). This indicates a distinct composition of precipitates in the two different weldments.  
Figure 12 shows the microstructure of the as-welded HAZ adjacent to the fusion boundary; it appeared to be quite similar to that of the as-received material. The only noticeable difference is the presence of a thin film layer of Cr-rich carbides along the grain boundary, as illustrated in Figure 12c. This thin layer is likely a consequence of the thermal cycles during the welding process.  
Figure 8: **(a)** IPFM image at as-welded condition of the weldment with Inconel 625 filler material, showing the FZ (**left**) and HAZ (**right**) grain morphology. **(b)** HAADF image showing the precipitates in the FZ.  
Figure 10: As-welded FZ using Inconel 625 filler material. (**a**) HAADF image. (**b–f**) ChemiSTEM EDX maps of Fe (**b**) Ni (**c**), Cr (**d**), Nb (**e**), and Ti (**f**) showing the Nb- and Ti-rich nano-sized MC carbides.  
Figure 9: As-welded FZ of the weldment with Inconel 625 filler material. (**a**) HAADF image. (**b,c**) NBED patterns of the Laves and MC phases. (**d–i**) ChemiSTEM EDX maps of the studied region in (**a**).
## 4 Power Loss Model of the DAB Converter  
Efficiency is related to transferred power and power losses. By taking into account the power switch losses, transformer losses, and inductor losses, a power loss model was built to verify the practical measured power efficiency.
## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Foundational Technologies and Advanced Methodologies
#### 4.4.3 IoT Integration in Predictive Monitoring Systems  
IoT integration is crucial for real-time, distributed predictive monitoring. Wireless sensors have been employed to track vibrations and temperatures in mobile mining machinery, significantly reducing unplanned maintenance costs [49; 88]. The use of edge computing and 5G networks has enabled low-latency data analysis, improving anomaly detection and operational response times.  
Distributed ledger technologies (DLT) [38; 39] have been introduced to securely share sensor data across multiple industrial partners, addressing traceability issues in extended supply chains [40; 41; 42]. The integration of DLT with big data analytics [43; 44] has facilitated the streamlining of service agreements and the clarification of accountability for equipment failures at various stages of the mining process.  
Recently, domain-specific case studies on agricultural sensors and maritime power systems have also demonstrated how RNNs, LSTMs, and edge-based inference can support predictive analytics for rotating equipment even outside conventional industrial sectors [31; 68]. Moreover, bridging IoT data streams with advanced condition monitoring in nuclear facilities and solar photovoltaic systems further highlights the cross-sectoral traction of IoT-based predictive maintenance solutions [67; 81]. These broadening applications illustrate the versatility and scalability of IoT-driven fault detection strategies.  
In renewable energy applications, turbines and solar panels are monitored for preemptive interventions, enhancing efficiency. Machine learning models, such as random regression, have been applied to accurately predict motor faults, while IoT platforms have supported advanced, continuous asset diagnostics. This collaborative approach, combining IoT with AI-driven analytics, has enabled maintenance schedules to align with real-time risk assessment, thereby extending asset lifecycles and reducing the total cost of ownership [62; 68].
# Telehealth Secure Solution to Provide Childhood Obesity Monitoring
## 7 Conclusions  
Childhood obesity is a serious problem worldwide, with serious adverse consequences for those who present this condition. The remote monitoring of children's weight can be very helpful to ensure that they comply with the daily recommendations for key factors that can be promoters of obesity. Hence, the importance of developing remote monitoring to successfully carry out this effort on a large scale and in a timely manner. Telehealth appears to be useful solution. The advantage is the continuous monitoring of children. For this purpose, a telehealth monitoring system has been proposed that consists of three parts: (1) collecting data from children and monitoring their obesity-promoting parameters, (2) informing parents and physicians about the anthropometric indicators and about whether children comply with the daily physical activity and sleep recommendations, and (3) finally, health professionals provide parents with clear, accurate, and meaningful information and recommend behavioral interventions that promote improvement in children's weight status. Due to the sensitivity of the information sent through public channels, it is essential to safeguard the information from the monitoring of children. Therefore, such information is encrypted with methods that ensure the confidentiality of the data. The information is transmitted simultaneously through the public channel. The encryption system is based on a multiuser network, spread spectrum, and chaotic Henon map, which is capable of encrypting/decrypting the information of each child and their monitoring parameters. Based on simulation results, high security, high resistance against noise interference, and efficiency in the encryption process were achieved with high sensitivity against the secret key, enough space key to resist exhaustive search attack, a BER of zero under 0.001 SNR, and achieving a speed of 4.99 Mb/s. Another advantage of the system is its flexibility to be extended to \(n\) children with \(m\) monitoring parameters and \(k\) specialists. In future work, the physical implementation and its evaluation of scalability and secure data transfer over various numbers of patients will be considered.  
Conceptualization, E.J.-G. and M.A.M.-E.; methodology, [PERSON]-G. and [PERSON]-D.; software, M.A.M.-E. and L.C.-A.; validation, [PERSON]-D. and R.M.L.-G.; formal analysis, [PERSON]-G., [PERSON]-D. and [PERSON]-G.; investigation, M.A.M.-E. and L.C.-A.; writing--original draft preparation, E.J.-G. and M.A.M.-E.; writing--review and editing, L.C.-A.; supervision, [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Council for Research and Technology (CONACyT), Mexico, under research grant A1-S-31628 and by the Professional Development Program in Higher Education (PRODEP) with the scholarship granted UABC-234.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Finite Element Model and Structural Analysis
### Development of Finite Element Model  
The developed CAD model of external fixation device and bone is discretized using linear (TE4) and paraboloic (TE10) tetrahedron finite element. Linear tetrahedron has four nodes. It is also called constant strain tetrahedron (CST element). Parabolic tetrahedron is curvilinear tetrahedron with ten nodes and linear change of deformation inside element (LST element--linear strain tetrahedron). FEM model of external fixation device is discretized with 133,315 TE4 (47.58%) finite elements, and with 118,632 TE10 (42.34%) finite elements. Both types of finite elements are 3D isoperimetric solid elements with six edges. For geometry approximation and the approximation of unknown variables of isoperimetric elements, the same nodes are used. All nodes of this types of elements have three degrees of freedom (displacements). Connections between parts of the external fixation device (fixed, contact and screw connection) and connection to supports (slider connection) are modeled with Spider type of elements (28,265 finite elements, or 10.08%). Total number of finite elements for external fixation device is 280,212 for 245,732 nodes, which results in 737,199 degrees of freedom (Number of D.O.F), (Figure 4).  
External fixation device is manufactured using stainless steel for medical devices. This material is modeled as an isotropic linear elastic material. Stress-deformation equations for isotropic materials have only two constants: modulus of elasticity and Poisson's ratio, with the values of E = 215 \(\times\) 10\({}^{9}\) Pa and \(\
u\) = 0.29.  
Bone models are manufactured using beech tree with known mechanical properties which are similar to the mechanical properties of cortical bones of the human tibia [18]. Moreover, experimental testing for axial pressure, which is already done for human tibia and bone, showed small deviations of results [19]. Tree is an anisotropic material, but because of cylindrical symmetry of its structure it can be considered as orthotropic material which mechanical properties are defined in three planes with transverse, tangential and radial cross-section. A small rectangular part from the tree can be taken as a sample, with three symmetry axes: longitudinal (L), radial (R) and tangential (T), which are approximately vertical to one another.  
All parameters for orthotropic linear elastic material must be defined (Table 2).  
Defined properties of orthotropic linear elastic material must fulfill following conditions:  
\[\begin{array}{c}\frac{\mathrm{E}_{11}}{\mathrm{E}_{22}}<10^{7};\frac{\mathrm{E }_{11}}{\mathrm{E}_{33}}<10^{7}\\ \mathrm{v}_{12}<\sqrt{\frac{\mathrm{E}_{11}}{\mathrm{E}_{22}}};\ \mathrm{v}_{13}<\sqrt{\frac{ \mathrm{E}_{11}}{\mathrm{E}_{33}}};\ \mathrm{v}_{23}<\sqrt{\frac{\mathrm{E}_{23}}{\mathrm{E}_{33}}}\\ \mathrm{v}_{12}^{2};\frac{\mathrm{E}_{22}}{\mathrm{E}_{11}}+\mathrm{v}_{23}^{2 };\frac{\mathrm{E}_{33}}{\mathrm{E}_{22}}+\mathrm{v}_{13}^{2};\frac{\mathrm{ E}_{33}}{\mathrm{E}_{11}}+2\cdot\mathrm{v}_{12}\cdot\mathrm{v}_{23}\cdot\mathrm{v}_{13} \frac{\mathrm{E}_{33}}{\mathrm{E}_{11}}<1\end{array} \tag{1}\]
# A Cumulant-Based Method for Acquiring GNSS Signals
## 6 Conclusions  
In this paper, we proposed an acquisition method for GNSS signal processing, demonstrating its ability to efficiently eliminate side-peaks, particularly when applied to real Galileo signals. The method also exhibited strong side-peak cancellation performance when acquiring GNSS signals. We presented two different approaches for implementing the proposed method, and while our focus was on GPS and Galileo signals, we believe this method can be extended to other GNSS systems, such as BeiDou or GLONASS.  
The proposed method relies on cumulants and Fourier transforms, both of which are computationally intensive. Consequently, this approach may not improve the time-to-first-fix (TTFF); in fact, the TTFF may increase due to the added computational load, which is the primary drawback of our method. However, as the simulation results illustrate, the proposed method achieves a satisfactory detection rate even for signals with very low carrier-to-noise density ratios (C/N0). Additionally, a simple indoor experiment demonstrated that our method is capable of acquiring signals inside a building, suggesting its potential for superior performance in weak signal environments and indoor applications.  
Future research should focus on an in-depth analysis of this method, particularly in terms of optimizing computational efficiency and exploring its applicability across different GNSS systems and challenging environments. We also recognize the need to investigate methods to reduce the computational burden without compromising the method's robustness, which remains a crucial area for future work.  
Conceptualization, H.-S.W. and D.-J.J.; methodology, H.-S.W. and H.-Y.W.; software, H.-Y.W. and H.-S.W.; validation, H.-S.W. and D.-J.J.; writing--original draft preparation, H.-S.W. and H.-Y.W.; writing--review and editing, H.-S.W. and D.-J.J.; supervision, H.-S.W. and D.-J.J. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the National Science and Technology Council, Taiwan under grant number NSTC 112-2221-E-019-030.  
Not applicable.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.2 PsiE Domain  
In _E. coli_, the phosphate-starvation-inducible (_psiE_) gene is positively and negatively regulated by both PhoB and cAMP-CRP (cAMP receptor protein), which are respectively involved in the phosphate and carbon metabolism [84]. The phosphate and carbon sources regulate the _psiE_ gene by using the _lacZ_ and _chloramphenicol acetyltransferase_ gene (_cat_) fusions, respectively [84]. Although the function of PsiE has not yet been determined, sometimes it has been predicted to have features like DNA-binding protein inhibitor-related, putative transcriptional regulators or hypothetical DNA binding proteins (IPR020948).