esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Configuration for Continuous Exposure  
To simulate an AC MF emitted by real-life sources like transmission lines under laboratory conditions, the presence of a special exposure configuration is deemed necessary. The capability to control exposure conditions, ensure the proper positioning of biological objects, and minimize potential biomedical errors should be encompassed by this system. To address all the mentioned demands, the entire exposure configuration (Figure 1) was composed of the following main components: an incubator Q-Cell 240/40 (POL LAB, Poland), metal shielding and two identical cylindrical coils, each with a height of 1000 mm and internal diameter of 135 mm, for exposure and control samples. The supporting structures of both coils were constructed from polytetrafluoroethylene, and each supporting structure was wound with a total of 912 m of enameled copper wire with a diameter of 1 mm, comprising 2000 turns. The precise placement of experimental samples, glass vials, within the coil cavities was defined by a polyethylene support structure consisting of 4 guide rails and 5 shelves in positions E to A. The samples were uniformly distributed within each coil's cavity, maintaining each placement 190 mm apart, with the edge sample positions (E and A) located 100 mm from the respective edge of the coil. The level height of the culture medium placed in the bank is also considered to determine the position from the edge of the coil. To shield the control samples from the generated time-varying electromagnetic field, a 3 mm thick metal sheet, primarily composed of iron (over 96% iron content), with dimensions of 1000 mm in length and 160 mm in width, was positioned in the center of the incubator between the coils.  
The alternating magnetic field was generated by an exposure coil driven by harmonic current sourced from the RIGOL DG4162 generator (manufactured by RIGOL Technologies, Suzhou, China). This signal was subsequently amplified using a HUBERT A1110-16 linear amplifier (manufactured by Dr. HUBERT GmbH, Dietrich-Benking-Strasse 41, Bochum, Germany), while the current value was measured by a 6-digit digital multimeter, the Agilent 34401A (produced by Keysight Technologies, Colorado Springs, CO, USA).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Induction of DC MF**} & \multicolumn{2}{c}{**Induction of AC MF**} \\  & \multicolumn{2}{c}{**B\({}_{\text{DC}}\) (\(\mu\)T)**} & \multicolumn{2}{c}{**B\({}_{\text{AC}}\) (\(\mu\)T)**} \\ \hline  & C & E & C & E \\ \hline First experiment & & & & \\ (Exposure settings with nonspecific conditions) & 39 & 39 & No AC field & 70.2 (50 Hz) \\ \hline Second experiment & & & & \\ (Artificial settings of IPR conditions) & 50.92 & 254.6 & No AC field & 458.28 \(\mu\)T \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exposure levels of magnetic field for each experimental set.
## 4 Results and Discussion
### The HRVD Oscillator Model with a Periodic External Excitation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Embedding Dimension \(m\)** & **MSE** & \
\begin{tabular}{c} **Degree of Smoothness** \\ **(Embedded Flow Field)** \\ \end{tabular} &
\begin{tabular}{c} **Degree of Smoothing (Time Series** \\ **of Forced Characteristics)** \\ \end{tabular} \\ \hline \(m=10\) & \(4.8309\times 10^{-10}\) & low & low \\ \(m=50\) & \(2.4387\times 10^{-11}\) & medium & low \\ \(m=100\) & \(2.661\times 10^{-11}\) & high & medium \\ \(m=200\) & \(1.978\times 10^{-10}\) & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MSE and Smoothness Metrics for Various Embedding Dimensions. Figure 5: (**a-h**) Show the two-dimensional-embedded flow field and its two-dimensional reconstructed flow field obtained by taking para1, r = 4, and varying the value of m for model (24). (**a,b**) \(m=10\); (**c,d**) \(m=50\); (**e,f**) \(m=100\); (**g,h**) \(m=200\); (**i**) Time series of forced features at \(m=100\). (**j**) Time series of stress characteristics at \(m=200\). (**j**) Significantly smoother compared to (**i**). accuracy and reconstruction smoothness table. However, m = 200 is not necessarily the best choice, and we believe that the choice of the embedding dimension is a problem that needs to be further investigated in the future.
# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 5 Conclusions  
With a focus on moving nodes within a LoRa network and with the assistance of a high range WuR transmitter, this article proposed an access protocol to minimize data latency and the possibility of packet loss. Our study unveiled that the proposed communication scheme outperforms existing solutions from the literature in data latency. Such deductions instate the use of MOTILO as a study solution for mobile IoT networks. A future version of the algorithm will handle the migration of a node for more than one SF zone in order to eliminate any packet loss and will be capable of even ultra high-speed nodes. As a future work, we intend to evaluate the performance of the proposed protocol in scenarios that will model in detail the fading and propagation fluctuation phenomena in the wireless medium, so that the algorithm can more accurately adjust the duration of the reserved time slot based on more realistic network conditions. Moreover, the future version of MOTILO will be amended accordingly so as to cope with unlimited SF zone migrations while offering zero lost transmissions without sacrificing mean data latency. Figure 7 depicts this result, as the system with the higher velocity nodes seems to have zero lost packets in addition to the slightly better mean data latency. Under the same rationale, nodes with the same speed but more change directions result in the same mean data latency but their packet loss is not the same' as for the case with less direction changes, their movement pattern creates less dense formations while more directions changes result in denser formations and by this way the nodes have more time to return to their initial position.Less direction changes mean that their movement pattern will have many indentations while higher number of direction changes result in more flattened moving patterns resulting in less SF migrations.  
Conceptualization, K.F.K. and G.A.B.; methodology, K.F.K., G.A.B. and A.V.; software, K.F.K. and G.A.B.; validation, K.F.K., G.A.B., A.V., P.N. and G.I.P.; investigation, K.F.K., G.A.B. and A.V.; writing--original draft preparation, K.F.K. and G.A.B.; writing--review and editing, K.F.K., G.B, A.V., P.N. and G.I.P; supervision, P.N. and G.I.P. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Energy evaluation of TDMA-PL, TDMA-2M and MOTILO algorithms for 9 EDs.  
Funding: This research has been co financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE 2 (project code: T2 EDK-02617).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Participants  
Research experiments were conducted at the Center of Biomechanical & Rehabilitation Engineering (CBRE) at the University of the District of Columbia (UDC) and the Anthropometry & Biomechanics Facility (ABF) at the NASA Johnson Space Center (JSC). This research complied with the American Psychological Association Code of Ethics and was approved by the Institutional Review Board at the University of the District of Columbia (Protocol ID: 1837316-1). Informed consent was obtained from each participant who was recruited via flyer postings around the University campus. Twelve physically fit and healthy individuals (5 males and 7 females) aged 20-40 years old (26.91 \(\pm\) 6.81 years old) participated. None of the participants had a previous injury or condition, nor were they taking any medications that affected their balance and/or gait. At the NASA JSC ABF, five participants (4 females and 1 male) aged 20-40 years old (25.6 \(\pm\) 6.34 years old) were assessed. This particular group was used to assess the effects of the 3D-printed HUT in addition to the control and xEMU vest. Participants were selected based on their ability to fit in the HUT.  
Both NASA ABF and UDC CBRE participants were tested unsuited (or control configuration) and donning the xEMU Vest (20 lbs (9.072 kg)) concentrated at the torso, as  
Figure 1: Flow chart that overviews the participants and experimental procedures for this research study.  
seen in Figure 2A. The purpose of the xEMU Vest was to raise the CoG of the participants, mimicking the superior shift of CoG due to the xEMU spacesuit.  
Aside from the control/unsuited and xEMU Vest configurations, NASA participants also underwent testing using a 3D-printed hard upper body torso (HUT) shown in Figure 2B. The HUT (11 bs or 4.98 kg) is a 3D-printed replica of the torso section from the xEMU spacesuit that restricts the range of motion in the upper body and alters the resting, standing posture of the individual, shifting the shoulders forward and hunched over (Figure 2B). This stance is unique to spacesuits and cannot be replicated with the xEMU Vest.
## 3 Results and Discussion
### Predictive Modeling of Cutting Forces
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dependent Variable** & **Model** & **R\({}^{2}\) Train** & **R\({}^{2}\) Test** & **MAE Train** & **MAE Test** & **RMSE Train** & **RMSE Test** \\ \hline \multirow{8}{*}{Fx} & RFR & 0.9959 & 0.9243 & 0.1627 & 0.4910 & 0.2562 & 0.8887 \\ \cline{2-7}  & GBR & 0.9967 & 0.9409 & 0.1571 & 0.4832 & 0.2062 & 0.7800 \\ \cline{2-7}  & LightGBM & 0.9837 & 0.9698 & 0.2824 & 0.3825 & 0.4576 & 0.5572 \\ \cline{2-7}  & CatBoost & 0.9990 & 0.9642 & 0.0849 & 0.3889 & 0.1113 & 0.6073 \\ \cline{2-7}  & KNN & 0.9849 & 0.9707 & 0.2781 & 0.3370 & 0.4397 & 0.5493 \\ \hline \multirow{8}{*}{Fy} & RFR & 0.9957 & 0.9613 & 0.1338 & 0.4072 & 0.2037 & 0.6102 \\ \cline{2-7}  & GBR & 0.9958 & 0.9549 & 0.1517 & 0.4530 & 0.2000 & 0.6663 \\ \cline{2-7}  & LightGBM & 0.9831 & 0.9645 & 0.2570 & 0.4079 & 0.4022 & 0.5848 \\ \cline{2-7}  & CatBoost & 0.9987 & 0.9563 & 0.0891 & 0.4607 & 0.1135 & 0.6481 \\ \cline{2-7}  & KNN & 0.9800 & 0.9420 & 0.2790 & 0.4370 & 0.4374 & 0.7467 \\ \hline \multirow{8}{*}{Fz} & RFR & 0.9942 & 0.9642 & 0.0543 & 0.1372 & 0.0953 & 0.2282 \\ \cline{2-7}  & GBR & 0.9962 & 0.9562 & 0.0564 & 0.1538 & 0.0776 & 0.2371 \\ \cline{1-1} \cline{2-7}  & LightGBM & 0.9794 & 0.9730 & 0.1105 & 0.1221 & 0.1798 & 0.1800 \\ \cline{1-1} \cline{2-7}  & CatBoost & 0.9992 & 0.9763 & 0.0292 & 0.1126 & 0.0357 & 0.1688 \\ \cline{1-1} \cline{2-7}  & KNN & 0.9843 & 0.9818 & 0.0963 & 0.0866 & 0.1567 & 0.1477 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performance metrics for cutting forces (Fx, Fy, and Fz) prediction. Figure 9: Predictive performance of regression models for Fz based on actual and predicted values with corresponding R\({}^{2}\) scores. It is noteworthy that the Fz component exhibited the highest prediction accuracy among all force directions across all models. This may be attributed to the lower variance and greater stability of the thrust force in micro-milling, which is less sensitive to tool-workpiece dynamics compared to tangential (Fx) and radial (Fy) forces. Similar conclusions were drawn in earlier research [25], which emphasized the comparatively stable nature of the thrust force under varying cutting conditions. The superior predictive performance for Fz also opens opportunities for integrating ML-based force models into digital twin systems, enabling more precise simulation and real-time optimization of micro-milling tasks in Industry 4.0 and 5.0 environments.
## 3 Codes and Data  
Based on the given salt system investigation [20] for iMAGINE and the dimensions study [5], the applied standard salt composition for the study for the nominal case is the heavy-metal-rich composition 20%NaCl-56.35%UCl\({}_{4}\)-23.65%UCl\({}_{3}\). This composition was originally studied for the required thermo-physical properties by [21]. In addition, some comparative studies have been based on the eutectic 42.5% NaCl-40.5 UCl\({}_{4}\)-17% UCl\({}_{3}\). The salt system has been chosen to be similar to the salt system envisaged for the iMAGINE proposal. From reactor operation point of view, the key parameter to obtain for self-sustained operation on spent nuclear fuel is the high possible loading for heavy metal, which is decisive to achieve sufficient breeding. However, the salt system is currently to be seen as an initial primary candidate of choice based on the UK experience in working with chlorine salt melts, but for the final decision, the corrosion control opportunities have to be investigated.  
The simulations for this step of the study of the zero-power reactor configurations were mainly performed using the TRITON/NEWT sequence of the SCALE code system [22], applying TRITON for the preparation of the shielded multi-group cross-section set and NEWT for the 2D transport solutions for the core part of the study. This was complemented with the use of Keno VI (multi-group as well as continuous energy) for creating 2D cross-checking solutions, as well as for the following 3D studies using the multi-group solver and the continuous energy solver for cross-testing. For TRITON, the v7-252 cross-section set of the SCALE package was used, which was based on ENDF/B 7.1. The sequence and the cross-section set was also the basis for the multi-group cross-section preparation for the multi-group (MG) Keno VI calculations, while the continuous energy version used the ce_v7.1_endf library of the SCALE package.  
For the TRITON/NEWT study, two specific 2D models for a fast molten salt reactor was built for the simulation (Figure 2). Both NEWT models reflected a 2D quarter of the core with x-y discretization consisting of the ring core (with added ring-shaped discretization for the evaluation of the power distribution) and the steel vessel surrounded by reflector, while the rest of the cell was filled with vacuum using reflective boundaries at the bottom and the left side and vacuum boundaries at the other two sides. The 2D and 3D Keno VI models shown in Figure 3, reflecting a cylindrical core surrounded by a vessel and a reflector on all sides for the 3D model and reflective boundary conditions at the top and bottom for the 2D model, were used for cross testing with the NEWT results.  
Figure 2: TRITON/NEWT model for the 2D study of reflector materials (**left**) and for the analysis of the variation of the effect of different reflector thicknesses (the thickness of the green reflector region is varied). The evaluation of the power distribution in the 2D system is based on a ring structure in the core (**right**). Molten salt core—blue; steel vessel—light green; reflector—yellow; surrounding vacuum—grey.
# IoT-Based Implementation of Field Area Network Using Smart Grid Communication Infrastructure
## 2 Hierarchy-Based Smart Grid Communication Infrastructure for Smart Farming Applications  
Smart grid is an integration of electrical as well as information and communication technology to make the power grid more reliable, flexible, efficient, and robust. It is an intelligent power grid with an integration of various alternative and renewable energy resources using automated monitoring, data acquisition, and control and emerging communication technologies [16; 17; 18; 19; 20]. The application of a diverse set of communication standards requires analysis and optimization depending upon requirements [21; 22; 23; 24; 25; 26; 27; 28]. These requirements can be decided on the basis of the area of coverage, the application bandwidth requirement, etc. It can be categorized as FAN, NAN, or WAN for agriculture applications. Various sensors for the measurement of different parameters such as humidity, temperature, current, flow, etc. can be deployed for the monitoring and control of the agriculture field. An automatic irrigation system can be implemented using a communication infrastructure. Smart farming can result in a higher agricultural yield. Figure 1 illustrates the communication infrastructure of a smart grid technology for smart farming using different hierarchical network layers.  
A smart grid communication network for intelligent agriculture applications can be designed on the basis of the following hierarchical layers.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### General Features of Simulation Systems (GQ7-9, Fq7-10)
#### 3.8.3 Portability (GQ9)  
The portability of a simulator is a major factor in spreading the use of medical simulators in educational and training settings. In answering question GQ9 (\"How portable is the simulator? (very portable, portable, not portable)\"), we have defined three levels of portability:  
* A very-portable simulator is a commercial or custom system designed to be held in the hands and/or on the head or that can be easily carried. Seven studies [22; 23; 25; 37; 32; 26; 30] report the use of very portable simulators.
* A portable simulator is a system designed to have a simple assembly/disassembly of the setup and (eventually) an easy calibration procedure. Thirteen studies [24; 26; 27; 28; 31; 33; 34; 36; 39; 41; 43; 44] report the use of portable simulators.
* A non-portable simulator is a system that requires a dedicated room and/or has a complex assembly/disassembly of the setup and (eventually) a difficult calibration procedure. Two studies [29; 42] report the use of non-portable simulators.  
In [40], the authors use two simulators: one commercial, identified as very portable; and one custom, identified as portable.
## 4 Blockchain-Assisted Cloud Frameworks: Security Perspective
### Decentralized IDM Approaches
The identity verification steps are explained in detail in Figure 10.  
As a solution for insider attacks in the cloud DB, a method was suggested by the authors of paper [76]. The authors have proved logically as well as experimentally the theorem, \"All authentication conditions of the blockchain are met if and only if a user authenticated\". The authentication process will be initiated for both insiders and outsiders upon receipt of a request. A user's authentication is verified by first confirming that their authentication process is not necessarily the same as the authentication process. The authentication process is not the same as the authentication process. Figure 10: User authentication procedure of “EIDM” [75]. Figure 9: Data access and user authentication procedure of blockchain-based IDM model for cloud data [74]. login ID and signature are legitimate, and then verifying that the current index value is correct, and finally authentication is granted. In [77], the Single Sign-On (SSO) multi-resource access technique is suggested for the cloud environment. The author used three DBs and four smart contracts to implement SSO multi-resource access. When a user submits an access request, they need to provide certain identity information to the access control service gateway. This information is hashed and matched to the hash value kept in the cloud DB. The smart contract verifies the resources to which the requesting user has access permissions if both match. The user receives an SSO token, which enables them to access the resources without multiple sign-on. Figure 11 depicts the SSO token generating process in detail. The paper [78] suggested blockchain-based identity management, access control, and secure sharing (BC-IAS) in a cloud environment. The article proposes a token-based authentication system where the identity token consists of user name, user attribute tag, and values. A user obtains access to requested resources only if the identity token and access control list are verified. The authors, however, have not discussed the different user attributes used for identity token creation. Also, the paper suggested access provision but no method to maintain access logs. The IAS token generation and user authentication verification is shown in Figure 12.  
The detailed observation of the above-discussed solutions is presented in Table 2. We have compared the solutions based on the security parameters (privacy, availability, and access control) related to IDM/authentication, the authentication mechanism, and the amount of time it takes to verify the identity. The model discussed in paper [74] has an access control framework but requested data are transferred from a foreign CSP to the user in un-encrypted form, as a result of which the privacy of the data is compromised. In [75], the data access control mechanism is absent. Figure 11: SSO token generation [77].
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 3 Results
### Functional Features and Non-Indigenous Species  
When divided into categories (habitat types, trophic level, and ISR), averages of abundant sequences showed different patterns among the zones (I, II, III-1-2-3). The most dominant habitat type of OTU was the marine type for the three different zones (Figure 4A), while the types of feeding habit and indigenous rate showed complex response patterns. The dominant trophic level was first consumer and second consumer, followed by producer and symbiotic trophic levels (Figure 4B). The indigenous rate of Zone III-3 was significantly higher than other zones, but Zones II and III-1 showed opposite patterns (Figure 4C), while Zones I and III-2 presented no significant difference in indigenous rates. The three divisions of Zone III from CCA results demonstrated similar patterns within the three zone types (Zones I, II-and III).  
We found significant relationships between environmental factors (Table 1). Specifically, temperature had positive relationships with nutrient-related factors such as TP and PN, and showed a negative relationship with TOC. Salinity showed significant negative relationships with TN and TP, but also displayed a positive relationship with carbon-related factors such as EC and TC. However, there were no significant relationships between environmental factors and divisions of categories except for habitat types (marine, estuarine, freshwater). The marine habitat of OTUs revealed a significant positive relationship with salinity (r = 0.879) and EC (r = 0.878), respectively, whereas freshwater and estuarine types of OTUs showed negative relationships with salinity (r = \(-\)0.888 and \(-\)0.856) and EC (r = \(-\)0.884 and \(-\)0.856) respectively.  
Figure 3: Abundance of OTU sequences along the study sites (**A**) with site description of physical features and zone classification (**B**) based on genus identification level.  
Figure 4: Comparison of the habitat types ((**A**), marine-freshwater-estuarine), trophic level ((**B**), consumer 1 [filter feeder], consumer 2 [carnivore], producer and symbiotic) and natives ((**C**), non-indigenous and indigenous) among five zoning types based on genus identification level.