esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection and Analysis  
#### 2.4.1 Sensor-Based Data Acquisition  
The primary data collection was conducted through IMU sensors attached to participants' waist and ankle positions, collecting motion data at a 100 Hz sampling rate. The waist-mounted IMU was used to capture postural stability metrics such as center of mass (COM) displacement and sway area, which are critical for assessing balance maintenance during task execution. The ankle-mounted IMU was utilized to analyze lower limb movement patterns, step cycle characteristics, and temporal consistency across work cycles. This dual-sensor approach enabled comprehensive assessment of both overall body  
Figure 4: Sequential analysis of work cycle phases for each task.  
stability and specific movement dynamics throughout task performance. The selection of waist and ankle positions for IMU placement was based on prior studies in construction ergonomics and wearable motion tracking, which have validated these locations as effective for capturing postural sway, center of mass displacement, and step cycle dynamics. The waist-mounted sensor corresponds to the approximate location of the body's center of mass, making it ideal for postural assessment, while the ankle-mounted sensor is well-suited for lower-limb kinematics and gait-phase detection. Additionally, the sampling frequency of 100 Hz was chosen in accordance with established guidelines in human motion analysis. This rate provides sufficient temporal resolution to detect rapid movement changes in construction tasks, while maintaining efficient data handling and processing.  
The collected data included triaxial acceleration and angular velocity measurements, which underwent preprocessing through a low-pass Butterworth filter with a 20 Hz cutoff frequency to remove noise. Task completion data was simultaneously recorded, including the number of units completed (sacks transported, brick layers completed, or scaffolding components installed) and associated quality metrics for each task type.
# Security Risk Analysis of LoRaWAN and Future Directions
## Security Challenges of LoRaWAN v1.1
### Key Distribution Related  
As mentioned earlier in Section 3, there are two mechanisms for distribution of the keys: ABP and OTAA. Both methods have their unique advantages yet also have security implications as detailed below:  
(a) ABP related  
ABP can be an easy way to simplify deployment, at the cost of reduced security as ABP devices use the same session keys for their lifetimes (i.e., no re-keying is possible). It is important to have a different set of keys per end-device. Counters must be kept in non-volatile memory. If a problem occurs regarding the storage of the counters in the non-volatile memory and also in the process of resuming the final count of the counter following a reset event, then the ED will go out-of-SYNC and be useless after then.  
(b) OTAA related  
OTAA provides a more flexible and secure way of establishing session keys with the servers. Therefore, it is recommended that OTAA devices are used for higher security demanding applications. However, the following concerns are still valid for OTAA:  
* For the OTAA activation procedure, the root keys are needed for generating the session keys. These _NwkKey_ and _AppKey_ are AES-128 root keys specific to the end-device that are assigned to the end-device during fabrication. These keys need to be securely stored.
* During fabrication _NwkKey_/_AppKey_ key-pair will be generated for each end-device (all related session keys are generated later on by using these specific root keys); but it brings the challenge about keeping the track and taking care of these key pairs.
* PKI can be used for distributing the keys to the end-devices, instead of embedding them during the fabrication process. This would introduce more flexibility and security to the LoRaWAN. Hence, by this way, it would be possible to update the root keys (_NwkKey_/_AppKey_ pair) periodically as well. This remedy is further discussed in Section 6.1 with more details.
## 4 Application Results
### Rules Set Editing  
As previously mentioned, maintenance of the rule set is easy and does not require informatics specific knowledge. The rule set spreadsheet has in the first column of every row the name of the disease. Further columns have the name of a test parameter in the first row. In this way, it is possible to modify rules by only changing the value and operator (\"\(<\)\", \"=\" or \"\(>\)\") which relates a disease to a parameter deviation. Moreover, adding new diseases or parameters is also possible. Figure 9 shows a reduced sample of the Excel spreadsheet, which contains diseases' rules.  
We have established a role in the system which provides complete access to this spreadsheet in order to update and modify the ruleset. In this way, a previously appointed clinician administrator takes care of the maintenance of the ruleset. The rest of the clinicians can see the ruleset with diseases and thresholds, but they are not allowed to make changes. Thereby, clinicians using the system can propose modifications in the ruleset, and the clinician administrator decides if these changes are made.  
Figure 8: Patient development graphics (Spanish).  
Proposals for new settings or rules are submitted by doctors duly justified to a clinical administrator who analyses and validates all the changes. From time to time, all suggestions for improvements are taken into account and an evaluation of the changes with a set of cases previously established is performed. If the results are correct, the ruleset is updated accordingly. Therefore, the work of the clinical administrator is mainly of control and coordination of proposals for improvement.  
In order to prevent possible errors in human entries, a checking in the whole set of rules is performed. If any value in a rule is very far from the other values for the same parameter in the rest of the ruleset, a warning message is shown.
# Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
## 3 Results
### Analysis of Wake Vortex Parameters  
Figure 12 illustrates vorticity distribution at different distances behind the vortex core with varying vertical wind speeds. It can be observed that vorticity rapidly decays in the early stages of vortex formation, with a slower decay rate beyond x/c = 15. The influence of vertical wind reduces the initial vorticity of the vortex core and significantly accelerates the rate of vorticity decay. Comparing the two wind direction conditions under the same wind speed magnitude, it is evident that vorticity is consistently higher under negative wind speeds. This is attributed to convective effects, as upward-blowing winds are typically associated with ascending air currents. The ascending air may carry away momentum from the vortex and promote the re-mixing of upper-level air into the vortex, thereby prolonging the lifespan of the vortex.  
Figure 11: Vorticity contour profile of different positions behind the wing. (**a**) Wind speed \(-1\) m/s; (**b**) wind speed \(0\) m/s; (**c**) wind speed \(1\) m/s; (**d**) wind speed \(2\) m/s; (**e**) wind speed \(3\) m/s.  
Contrary to expectations, there is not a positive correlation between vorticity decay rate and wind speed under favorable wind conditions. Specifically, at the x/c = six cross-sections, vorticity is 15%, 29%, 23%, and 84% under still air conditions for vertical wind speeds of 1-4 m/s. The addition of vertical wind influences the vortex's rotational speed, alters the structure and distribution of the vortex core, reduces the vortex's stability, and leads to rapid vorticity decay. However, vertical wind also, to some extent, replenishes turbulent kinetic energy and compensates for losses incurred by the vortex due to atmospheric viscosity and turbulence effects. As such, the decay rate of vorticity at the vortex core is not positively correlated with wind speed.  
Figure 13 presents the distribution of three-dimensional vortex core trajectories under different vertical wind speeds. The two intersecting points on the left and right correspond to the locations of the wingtips. The changes in the vortex core, both in vertical and horizontal displacement, are visually depicted with distance traveled.  
Figure 12: Vorticity distribution at vortex core with changing distance at different wind speeds.  
Figure 13: Distribution of three-dimensional vortex core trajectories at different wind speeds.
## Materials and Methods
### Statistical Analysis  
Statistical analyses were done using the SPSS program (version 14.0, Chicago, IL, USA). The statistical significance for deviation from the Hardy-Weinberg equilibrium (HWE) was determined by \(\chi^{2}\)-test according to the [PERSON] and Thompson method [31]. For _GSTTI_ and _GSTM1_ null genotypes, the HWE could not be tested because the number of heterozygotes is not known. A normal distribution was checked for all variables by Kolmogorov-Smirnov test. Not normally distributed variables were analyzed with parametric tests after logarithmic transformation or with non-parametric tests. The level of significance was set at \(p<0.05\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Gene** & **Exon** & **Primer F** & **Primer R** & **Annealing Temperature** \\ \hline
**XRCC1** & 6 & TGTACCTGACTCCCATGG & CACTCTATCTATGGGCACAG & 58 \\ \hline
**XRCC1** & 9 & TGGGCCTGGATTGTGGGTCTG & CACCACCACTACCACCCTGAAGG & 62 \\ \hline
**XRCC1** & 10 & AGTACCTCCCGGCCTGCG & TCTCCCTGGTTCCAACCT & 58 \\ \hline
**XRCC3** & 7 & GGTGGACAGTGCAAACCAA & TGACAGGCTGAGGGTCTT & 62 \\ \hline
**XPD** & 23 & CCCCTCCTCCCCTTCTTCTT & GCTGCCTTCTCCTGTTA & 61 \\ \hline
**GSTTI** & 5 & TTCCTTACTGCGTCTCACATCTC & TCACCGGCATATGCCAGCA & 58 \\ \hline
**GSTM1** & 6 & GAACTCCCTGAAAGCTAAG & GGTTGGCCTAAAATATACGGTTG & 58 \\ \hline
**BLXH** & 12 & GCTGTGTAGGCAGAGACCAAATT & CCTCGATCTGCTTGCACCTCG & 58 \\ \hline \end{tabular}
\end{table}
Table 1: The primers sequences and amplification conditions for the genes investigated.
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 5 Intelligent Servo Tuning Results  
Table 5 shows the PI controller parameters and bandwidth of before and after optimization. A large and almost the same bandwidth of five axes feeding the drive systems, were obtained for synchronization. To verify the effectiveness of the intelligent servo tuning approach for MCU-5X CNC machine tools, there are some tests which are conducted with different parameter settings. The virtual feed drive system of the CNC machine tools is able to estimate the performance, and the tendency of simulation results are representative. The simulation and experimental results are introduced to illustrate our approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Before Optimization**} \\ \hline Parameters & \(X\)-axis & \(Y\)-axis & \(Z\)-axis & \(A\)-axis & C-axis \\ \hline \(K_{pp}\) & 48 & 32 & 55 & 32 & 32 \\ \(K_{pp}\) & 10 & 10 & 8 & 12 & 200 \\ \(K_{vi}\) & 1000 & 1000 & 800 & 1400 & 20,000 \\ Bandwidth (Hz) & 9.3771 & 7.2411 & 10.6631 & 4.500 & 9.4743 \\ \hline \hline \multicolumn{5}{c}{**After Optimization by PSO**} \\ \hline Parameters & \(X\)-axis & \(Y\)-axis & \(Z\)-axis & \(A\)-axis & C-axis \\ \hline \(K_{pp}\) & 63.914 & 48.532 & 65.1669 & 55.533 & 68.7714 \\ \(K_{cp}\) & 13.422 & 12.147 & 9.866 & 23 & 397.9897 \\ \(K_{vi}\) & 1341 & 1211 & 987 & 2305 & 41211 \\ Bandwidth (Hz) & 12.8667 & 12.2543 & 12.6139 & 11.1301 & 12.9859 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Proportional-integral (PI) controller parameters and bandwidth values.  
Figure 11: Operation illustration of the Machine Dr. (**a**) Auto-tuning screen; (**b**) Operation history data screen.
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Capabilities
#### 4.4.1 Communication  
The communication capability refers to the sensors' ability to communicate and change information locally. This ability may vary at different levels of interoperability between IoE sensors and systems and be classified as no connection (no connectivity between enablers), technical (basic network connectivity), syntactical (basic interoperability and data exchange), semantic (understanding about the semantics of the data), pragmatic or dynamic (applicability of the information), conceptual (shared view of the pervasive world) [27], or organizational (coordination and alignment of business processes across organizational boundaries) [128]. Additionally, based on communication capabilities, IoT devices are classified into two categories: gateway devices and constrained devices [43; 45]. Moreover, according to their abilities to interact with other objects, IoT objects can be classified into four levels (Level 0-Level 3). Level 0 objects only receive, and Level 1 objects only send information. Level 2 objects can perform both operations with one object, while Level 3 extends the interaction to any other object [50].  
Figure 6: IoE Taxonomy: capability category, its dimensions, and characteristics.  
Different networking protocols and technologies provide networking interoperability in IoT [27; 48; 114]. IoT systems can exploit several types of networks with different characteristics in terms of size, data transfer, coverage, latency requirements, capacity, and supported reachability [69; 74; 75; 83; 110]. The central networking and communication technologies are local area networks, wireless local area networks, wireless personal area networks, wide area networks, metropolitan area networks, wireless regional area networks, body area networks, mobile communication networks, wireless metropolitan area networks, satellite networks (e.g., GPS) [24; 118; 129], Neul, IPv6 over low-power personal area networks (6 LowPAN), low-range wireless area networks, cellular Sigfox, narrowband-IoT, and thread or mesh technologies such as Zigbee and SDNs [25; 30; 113].  
There are three types of communication protocol that enable IoT to interconnect and communicate: (1) device-to-device, which is applied to communication between mobile phones within reach and is the next-generation of mobile networks; (2) device to the server, in which the sensed data are sent to the servers, nearby or away from devices (applies to cloud processing); and (3) server-to-server, in which servers transmit data between each other--mainly used for mobile networks [99].
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
###### Abstract  
Gantries and beams, as the main load-bearing structures of heavy equipment, usually belong to the box structure consisting of outer walls and inner stiffened plates. The structure of the stiffener layout is bulky due to empirical design, leading to higher material consumption and impacting mechanical performance. There are challenges in effectively identifying load-transferred paths within 3D box structures through direct topological optimization. A method for optimizing the layout of internal stiffened plates of large box structures based on load paths is proposed in this paper. Initially, based on the load conditions acting on the structure, the 3D box structure is decomposed into 2D functional sections. Subsequently, the load paths on the functional cross-section are visualized according to the load path method. Finally, the stiffener layout of the ultimate optimized structure is designed according to the effective load path distribution. Taking the gantry of a heavy-duty aluminum ingot composite processing unit as an example, the optimization results indicate that the maximum stress of the structure decreased by 14.9%, the maximum deformation reduced by 32.95%, and the overall weight decreased by 14.4%. This demonstrates that the approach proposed in this paper is practical and effective for optimizing stiffener layouts in large-box structures.  
box structures; stiffened plate layout; load paths; optimization Article
## 2 Methodology  
### Physical Model and Numerical Method  
The schematic diagram of nanofluid flow in a grooved microchannel is shown in Figure 1. This two-dimensional model has a length of \(L\) = 500 \(\upmu\)m and a height of \(H\) = 25 \(\upmu\)m. Table 1 lists the detailed geometric parameters of the microchannel. The entrance section and the exit section are adiabatic. A condition of constant temperature \(T_{h}\) = 303 K is applied to the wall of the middle section. The nanofluid has a constant temperature of \(T_{c}\) = 293 K at \(x\) = 0 \(\upmu\)m. Nonslip velocity boundary is considered on all the walls. The nanofluid is assumed to be fully developed at \(x\) = 0 \(\upmu\)m. And the nanofluid exits the microchannel at atmospheric pressure.  
To obtain the thermal performance and flow behavior of the forced convection problem, steady and incompressible flow is supposed. A single-phase model and constant physical property are adopted. Therefore, the governing equations of continuity, momentum and energy are  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0 \tag{1}\]  
\[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial y}=-\frac{1}{\rho_{ nf}}\frac{\partial p}{\partial x}+\frac{\mu_{nf}}{\rho_{nf}}\bigg{(}\frac{ \partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{\partial y^{2}}\bigg{)} \tag{2}\]  
\[u\frac{\partial v}{\partial x}+v\frac{\partial v}{\partial y}=-\frac{1}{\rho_ {nf}}\frac{\partial p}{\partial x}+\frac{\mu_{nf}}{\rho_{nf}}\bigg{(}\frac{ \partial^{2}v}{\partial x^{2}}+\frac{\partial^{2}v}{\partial y^{2}}\bigg{)} \tag{3}\]  
\[u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}=\alpha_{nf} \bigg{(}\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial y ^{2}}\bigg{)} \tag{4}\]  
where \(u\) and \(v\) are the velocity along the \(x\) direction and \(y\) direction, respectively; \(p\) and \(T\) are the pressure and the temperature, respectively; and \(\alpha\) and \(\mu\) and \(\rho\) are the thermal diffusivities, the dynamic viscosity and the density, respectively.  
Finite volume method combined with second-order upwind discretization scheme is utilized to solve all equations. Semi Implicit Method Pressure Linked Equation (SIMPLE) and pressure-velocity coupling method is employed in numerical calculation. As the residuals of the governing equations are less than \(10^{-6}\), and the wall temperature and pressure drop residuals between adjacent iterative steps are less than 0.1%, the numerical calculation is considered to be convergent.
# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 3 Concept of Mixed Reality
### Algorithm
#### 3.2.1 Calibration  
Calibration models are used to set MR devices, such as see-through tools and cameras. Such models offer many benefits. For example, the calibration model in HMD installed on the user's eyes solves the instability between the tool and the hand to enable good interaction with the virtual object (such as a virtual button), thereby allowing for more accurate camera parameter estimation. This model is crucial to improve scene reconstruction from camera images. Ref. [28] used the latent active correction algorithm to tackle the problem of instability between the optical-see-through and the hand for hand-eye coordination. This algorithm is important for the user interaction that is achieved using a virtual button. Ref. [29] used a projection model using the matrix transformation formula to calculate the parameters of the fish-eye camera parameters. These parameters were useful for the creation of a full-view spherical image for scene reconstruction. Ref. [30] proposed a method in a surgical  
Figure 4: Mixed reality steps.  
application using iterative closest point (ICP) with a fast point feature histogram. This model registered the appropriate cone-beam computed tomography (CBCT) volumes and 3D optical (RGBD) camera.  
Ref. [31] implemented an MR-based needle navigation system for application in surgery. This system enables the rendering of tools such as needle trajectory and ultrasonic scanning images using an HMD device. It employs calibration techniques such as the procrustean method for ultrasound transducer calibration, and a template-based approach for needle calibration.  
Despite the great benefits of the proposed calibration models, there are various challenges that need to be addressed to improve these models. In using the calibration models for MR devices such as HMD, inappropriate tracking of the user's hand leads to a lack of proper fusion between the real and virtual hand. This effect reduces the stability accuracy between the user and the HMD device [28]. The camera calibration model requires the inclusion of additional parameters such as lens distortion or environmental effects (such as image brightness) to increase model accuracy [29]. The camera calibration model is sensitive to possible changes such as a camera or tools (e.g. surgery table) [30].
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 2 System Model
### Hybrid Beamforming and Pilot Placement
#### 2.1.2 Pilot Placement  
One of the key aspects in modern communication systems are pilot signals. These are used for channel estimation, synchronization, and beamforming. In 5G communication systems, pilots play a crucial role in achieving high data rates, low latency, and reliable communications [4].  
The pilot placement technique refers to the method or strategy used to determine the locations and allocation of pilot signals within a communication system [15]. A simplified version of the pilot placement equation is expressed as follows:  
\[p_{k}=p_{0}+\Delta_{p} \tag{3}\]  
Figure 1: Hybrid beamforming transceiver architecture for an m-MIMO system.  
where \(p_{k}\) represents the position of the \(k\)th symbol, \(p_{0}\) is the starting position of pilot symbols, and \(\Delta_{p}\) denotes the spacing between consecutive pilot symbols. In practical implementations, pilot placement algorithms may involve more complex equations to adaptively adjust pilot positions based on real-time channel conditions and system requirements. This is shown in Equation (4) below:  
\[p_{k}=p_{0}+\alpha\cdot k^{2}+\beta\cdot k+\gamma \tag{4}\]  
where the pilot positions are determined using a quadratic function. The coefficients \(\alpha\), \(\beta\), and \(\gamma\) can be optimized based on channel characteristics and system requirements in order to achieve better spatial diversity and interference mitigation.  
Regardless of the methods used, there are several disadvantages associated with pilot placement in communication systems. One disadvantage is the increased pilot overhead. Due to the allocation of additional resources for pilot transmission, such as time or frequency slots, there is a reduction in the available resources for transmitting actual user-data. This can impose restrictions on the system's ability to process data and handle workloads [16]. Another disadvantage is the potential for pilot pollution. Pilot pollution occurs when pilot signals from neighboring cells interfere with each other, leading to inaccurate channel estimation and degraded system performance [4]. To tackle these problems, several approaches have been explored. In [11], [PERSON] et al. proposed a pilot reuse technique that exploits the spatial sparsity of channel responses to achieve pilot overhead reduction in m-MIMO systems. In [17], [PERSON] et al. proposed a compressed sensing-based pilot design method that reduces the number of pilots required for channel estimation. We compare our results to these methods later.  
Accurate pilot placement enables precise estimation of the channel matrix \(H\), which is a prerequisite for effective beamforming. Leveraging SVD, the proposed system optimally utilizes the estimated channel information to reduce pilot overhead while enhancing performance metrics such as SE and BER. SVD is discussed in the next section.