esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application in the Reconstruction of the HWRR Decommissioning Model
### Point Cloud Data Acquisition for the HWRR Decommissioning Area
The operation of the FARO Laser Scanner Focus[5] 350 combines the principles of laser measurement and photogrammetry; therefore, the obtained point cloud data include three-dimensional coordinates (\(XYZ\)), laser reflection intensity (\(I\)), and color information (RGB). We aimed to use a positive viewing angle as much as possible to acquire point cloud data within the HWRR plant. A single-station scan took approximately 6.5 min, and approximately 30 scanning stations could be completed within a working day. In the 3D laser-scanning work within the HWRR plant, a total of 1186 white target spheres were employed as auxiliary controls, taking 28 working days to complete the 3D laser scanning for 572 stations. Throughout the laser-scanning process in the HWRR plant, approximately 14.3 billion points of point cloud data were acquired, resulting in a total raw point cloud data size of 97.6 GB. Due to the irreversibility of decommissioning, to preserve valuable digital images of the nuclear facility before decommissioning and provide auxiliary data for subsequent point cloud processing, we simultaneously used a camera for on-site photo collection during the 3D laser scanning of the HWRR plant. During the shooting process, efforts were made to maintain a horizontal orientation to capture frontal images. We took a certain number of overall and detailed photos of buildings, equipment, and pipes within each decommissioning region from different directions. The overlap between adjacent pairs of images should not be less than 30%. A total of 34 GB of image photos were collected, comprising a total of 2537 high-definition photos.
## 4 Discussion
Additionally, this modality required a three times longer acquisition time. Therefore, polarization microscopy with WWC is preferable. The gold-coated microscope slide enhanced the contrast in brightfield images. Another modality that profits from the reflective gold coating is Raman spectroscopy where the excitation laser passes through the tissue twice. The selected integration time of 4 s was sufficient to detect proper Raman spectra. Moreover, the integration time did not result in visual photodamage of the tissue. This was confirmed by calculating the mean-squared error based on a pixel-to-pixel comparison of two images. Therefore, the local scan's brightfield image obtained within step no. 2 and the additional brightfield image after the Raman scan (step no. *) were used (see Appendix A, Figure 10). The comparison resulted in a mean-squared error of 0.05. Although the maximum recommended accumulation time of 10 s [39] to conduct a tissue scan in a realistic time was not extended, it is advised that the spectroscopic modalities are exclusively applied to scan designated ROIs. One reason is the long measuring time per data point, the other reason is the spatial resolution. Although all spectroscopic modalities were able to distinguish GM and WM within the global scans, the mapping area was under-sampled at step sizes of 200 \(\upmu\)m; moreover, the OCT interfered with the evaluation. Much better are the created images of the local scan. In all images, it can clearly be distinguished between GM and WM. The chosen step size of 5 \(\upmu\)m was experimentally determined as the smallest step size of the microscope stage. As expected, the images of the H&E-stained sample did not show any abnormalities regarding the cellular structure of the brain sample. For further studies, the brightfield microscopic image of the H&E-stained sample could be used to determine an ROI. In order to compare the H&E-stained sample one-to-one with the different microscopic images of an unstained sample, images should have similar pixel resolution. Recording photographs of the H&E-stained sample with the 20\(\times\) magnification and the multimodal imaging system is possible without any problems. Only the activation of the condenser in the excitation path and the adaption of the illumination (Koehler illumination) are required. This opens up new possibilities for the multimodal imaging system to include the results of the H&E-stained sample in the multimodal data evaluation for tissue research and diagnostic within ex-vivo applications. Those applications are not limited to the separation of GM and WM although structural changes within GM and/or WM are indicative for patients with HIV [85], multiple sclerosis [86], and Alzheimer's disease [87,88]. Ex-vivo applications of any tissue section for research and diagnostic purposes are possible. The expansion of further modalities, such as epifluorescence microscopy, is feasible without any problems. This is due to the modular principle of the microscope. Space for an extra lamp housing and filters is already provided in the incident light as well as the transmitted light path of the modular microscope.
## 3 Results and Discussion  
### Conception and Construction of the Apparatus  
In order to meet the requirements for both reproducibility and easy handling, a simple mechanical apparatus was designed. The overall requirements for this device, which can be considered a laboratory or even medical product, include an operation at room temperature, contact to cell cultures, cleaning in a conventional laboratory washer, sterilization in an autoclave, and portability [58].  
The device itself was designed to be completely disassemblable and to be easily manufactured by a common milling machine and lathe. It consists of two modular parts to fulfil the function of the sample scratching and locomotion, respectively (Figure 2).  
Figure 1: Scheme of the data acquisition. In the first step, acting forces are collected in a variety of experimental configurations. Secondly, each scratching setup (F, \(\alpha\), dir) is evaluated microscopically in a sixfold determination. Finally, the obtained pictures are processed concerning the scratch border recognition and subsequent regression for roughness quantification.  
The scratching part of the device needs to scratch a cell substrate of undefined thickness from above in different, defined angles with respect to the sample surface [18; 57; 59; 60]. The scratching blade should be disposable or at least replaceable and changeable in geometry for multiple simultaneous scratches. Moreover, the force impacting on the sample should be able to be varied as different cell cultures are known to show diverging adhesion forces.  
This has been realized by a T-shaped scratch head that can mount any suitable construction material (e.g., polymers) with a thickness of 2 mm. The blade is fixed in position by two threaded shafts. The scratch head itself can be adjusted by rotating around the lower axis and fixed in five positions. Two extension arms, fixed to a polytetrafluoroethylene (PTFE) bushed axle, enable the head to be lifted up for sample exchange or revision. In order to tune the normal force impacting on the sample surface, additional weights can be attached. The rear mounting brackets also feature an axle that allows mounting a gas spring for an automated sample locomotion. Stainless steel was chosen for the axles and aluminum alloy for all other construction parts.  
The sample locomotion part of the device needs to suppress culture dish rotation, tilting, as well as undesired axial and radial movements. Additionally, the actual locomotion should be automated, smooth, directionally stable under force impact, easy to handle and to release, and ensure maximum accuracy.  
This is solved by the construction of a linear slide. The sled guide consists of a PTFE sleeved stainless steel rod on which the sled's brackets can glide. The sample holder plate provides an annular culture dish holder with a bayonet type lock that secures the culture dish.
# A Study on Reactive Ultra-Fine Fly Ash and Sulfoaluminate Cement in Self-Leveling Mortar
## 3 Results
### Mercury Intrusion Porosimetry (MIP) Test  
In this study, the specimens were cured in a room-temperature environment for 28 days, and MIP was employed to determine the porosity, pore size, and pore size distribution of the samples. Different pore sizes and corresponding mercury intrusion volumes were obtained under varying pressures. The results in Table 11 show that the total porosity is in the order of F5 > F10 > F15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** &
\begin{tabular}{c} **Cracking Time** \\ **(Days)** \\ \end{tabular} & **Maximum Strain** & **Stress (MPa)** \\ \hline S10F0 & 5.8 & \(-\)0.0162\% & 0.91 \\ \hline S10F5 & 7.1 & \(-\)0.0338\% & 1.42 \\ \hline S10F10 & 11.5 & \(-\)0.0268\% & 0.77 \\ \hline S10F15 & 12.8 & \(-\)0.0654\% & 1.49 \\ \hline S0F10 & 7.2 & \(-\)0.0195\% & 0.94 \\ \hline S10F10 & 11.2 & \(-\)0.0265\% & 0.78 \\ \hline S20F10 & 13.2 & \(-\)0.0813\% & 1.80 \\ \hline S30F10 & 15.0 & \(-\)0.0175\% & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Restrained shrinkage test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pore Type** & \multicolumn{4}{c}{**Microscopic Pore Size Distribution**} & **Total Porosity of Specim** \\ \hline
**Pore Diameter (nm)** & **\textless{}1.25** & **1.25–25** & **25–50,000** & **5000–50,000** & **Total Porosity of Specim** \\ \hline S0F5 & 0\% & 12\% & 64\% & 24\% & 36\% \\ \hline S0F10 & 0\% & 11\% & 65\% & 24\% &  
As the PUFA volume percentage increases, the total porosity decreases, further confirming that PUFA addition effectively solves the porosity issue of the specimens. It also helps explain why compressive strength improves in later stages as the PUFA volume percentage increases, as the smaller particle size allows for more effective packing within mortar, leading to greater densification, as suggested by theory [18,19]. As the SAC volume percentage increases, the total porosity also increases, which confirms the earlier explanation regarding compressive strength. Increasing SAC content leads to the formation of large amounts of ettrinigite, and these expansive products cause cracking in later stages. Consequently, the pore volume increases, leading to a decrease in strength and an increase in permeability.
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 1 Introduction
In these examples, a computer determines the walking direction of the visually impaired person by using obstacles in front and the position of the person detected by sensors and conveys the direction to the person by tactile stimuli. A tactile stimulus is more easily transmitted when it is applied to the skin, which has high sensory sensitivity, but it is not suitable for long-term use because direct stimulation of the skin tends to cause abrasions. The authors previously proposed a wearable hearing assistance system that estimates the direction of a sound source by computer from acoustic signals detected by ear microphones and notifies the person of the direction of the sound source by vibrators attached to the shoulders [39]. In this study, we modified the previous hearing assistance system so that it can present not only the direction of the sound source but also the distance to the source. The system presents the horizontal movement of the sound source to the person through the vibrators on both shoulders according to the distance to and direction of the sound source estimated from the acoustic signals detected by the ear microphones. Two small speaker drivers attached to a vest are used as vibrators so that they can be worn on top outdoors. The ratio of the vibration intensity of the two vibrators varies according to the angle of the sound source: the closer to the sound source, the stronger the vibration intensity, thereby presenting the relative angle of and distance to the sound source to the person. By turning their face in the direction where the vibration intensity on both shoulders is equal, the person can perceive the approach of the sound source as a change in vibration intensity. We conducted an experiment in which a subject wearing an eye mask and the hearing assistance system tracked a single sound source. The system can track the direction of a moving sound source with an accuracy of less than 5\({}^{\circ}\) by adding an analog vibration pattern to the direction of the sound source. By presenting the direction of the sound source with high accuracy, it is possible to show the approach and departure of the sound source to the subject. Section 2 describes the methods. Section 3 describes the configuration of the hearing assistance system, the structure, vibration characteristics of the vibrators, which can be used outdoors and a flowchart including the subject's behavior. Section 4 describes the tracking of a moving sound source by a subject wearing the system. Section 5 describes the discussion. Finally, the conclusions and future plans are provided.
# Modeling and Control of an Articulated Multibody Aircraft
## 1 Introduction  
The potential applications of small unmanned aerial vehicles (SUAVs) for both military and civil applications continue to drive interest in their design. Insect-inspired maneuverability and agility are desirable characteristics for small SUAVs that might be expected to operate in cluttered environments [1].  
Reproductive efficiency and the survival of many animals depends on locomotion for migration, territorial defense, predation and escape [2]. During aerial locomotion, animals actively change their body posture, and hence mass distribution, to produce forces and moments for propulsion and control [3, 4]. Active maneuvering, therefore, relies on the ability of the animal to optimize the benefits of three main physical mechanisms to control posture: aerodynamic lift and drag on wings and body [5, 6], control of the center of body mass and thus distance to aerodynamic force vectors [3, 7], and body moments of inertia quantities and rates through active changes in body shape [8].  
For mass-distribution modification, air and space craft have used internally moving masses as an addition to, or replacement of, traditional aerodynamic and moment generating actuators. Moving masses generate moments due to gravity or change the inertiaproperties for the purpose of controlling the motion of the vehicle. Most efforts have focused on employing movement of internal masses for trim, attitude, stability margin, detumbling of spinning spacecraft, trajectory, orbit, glide path and depth control.  
The biological systems exploiting moving masses have similarities to technological examples and substantial differences. In addition, existing formulations of flight dynamics do not adequately capture the effect of comparatively large swinging appendages (masses) observed in insect flight. A convenient mathematical representation appropriate to aerodynamic systems connected to comparatively large, possibly numerous, mass elements connected by joints will be presented. This formulation allows modern multiple-input-multiple-output (MIMO) control techniques to be implemented to manage winged craft with actuated joints and weighted appendages.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 3 Methodology of Modeling Hydraulic Amplifier
### Pressure Recovery in Deflector  
For the jet impact acting on the sidewall of the deflector, the stagnation phenomenon always exists and divides the jet into two parts, i.e., the downstream flow and the upstream flow. Assume that the stagnation line on the sidewall is \(l_{M}\) with coordinates \(x_{r}\), \(y_{r}\) and \(-h_{l}\ \leq\ z\ \leq\ h_{u}\), as depicted in Figure 5, which determines the location of the critical section.  
Considering that the fluid in the potential core basically keeps the same flow direction when it enters the high-pressure region, we can infer that only part of fluid collides with the sidewall.  
**Assumption 5**.: _The entrainment flow outside the potential core impacts on the sidewall, and then, flows along the sidewall, while the fluid in the potential core passes through the deflector directly without changing direction._  
Therefore, according to the wall attachment model [29], the total momentum along the sidewall is  
\[J_{0}\text{cos}(\theta)=J_{1}-J_{2} \tag{14}\]  
where \(J_{1}=\int_{-h_{l}}^{h_{u}}dz\int_{y_{br}}^{y_{r}}\rho u^{2}dy\), denoting the momentum of the downstream flow, in which \(y_{br}\) is the half-width of the potential core here, calculated by Equation (5) with \(x=x_{r}\); \(J_{2}=\int_{-h_{l}}^{h_{u}}dz\int_{y_{r}}^{\infty}\rho u^{2}dy\), denoting the momentum of the upstream flow; \(J_{0}=\ 2\rho u_{0}^{2}(b_{0}-y_{br})l_{0}\), denoting the fluid momentum outside the potential core.  
According to the definition of the stagnation line \(l_{M}\), \(y_{r}\) is a function of \(x_{r}\)[16]; hence, Equation (14) can be solved numerically, with the result that \(x_{r}=\ 0.695\) mm and \(y_{r}=\ 0.114\) mm.  
Figure 5: Jet impact on the sidewall.  
With the sidewall extruding, the kinetic energy of the fluid passing through the critical section is converted to pressure energy and a high-pressure region forms again. Because the deflector is much greater in thickness than the jet orifice, it is difficult for the fluid in the deflector far away from the jet to be affected.  
**Assumption 6**.: _The flow of the deflector outlet is limited to the extended theoretical boundary with a uniform velocity._  
As depicted in Figure 3, the boundary coordinate is \(z_{e}\). Consequently, for this convergent flow, the mass continuity is expressed as  
\[\iint\limits_{D_{cs}}\rho udy\ dz=4\rho u_{0}^{\prime}b_{0}^{\prime}z_{e} \tag{15}\]  
where \(u_{0}^{\prime}\) is the deflector outlet velocity and \(D_{cs}\) is the critical section (Figure 4); hence,  
\[\iint\limits_{D_{cs}}\rho udy\ dz=2\int_{-y_{br}}^{y_{br}}dy\int_{-h_{l}}^{-z_{ br}}\rho u\ dz+2\int_{y_{br}}^{y_{br}}dy\int_{-h_{l}}^{h_{br}}\rho u\ dz+2\int_{-y_{br}}^{y_{br}}dy\int_{z_{br}}^{h_{ br}}\rho u\ dz+4\int_{0}^{y_{br}}dy\int_{0}^{z_{br}}\rho u_{0}\ dz \tag{16}\]  
It follows from Equations (15) and (16) that \(u^{\prime}{}_{0}=\ 160.33\ \mathrm{m/s}\).
###### Abstract
In addition to lithological grouping and reservoir rock typing, the application of machine learning includes a prediction of continuous petrophysical properties from core data [12; 13] and a prediction of missing well log data [14; 15]. In the Croatian part of the Pannonian Basin, several researchers have successfully applied Artificial Neural Networks (ANNs) for lithology prediction based on seismic attributes and well log data. For instance, refs. [16; 17; 18] applied ANNs in the Sava Basin (Croatia, Figure 1) for a prediction of lithology and hydrocarbon saturation. The authors of ref. [19] demonstrated the efficiency of ANNs for lithofacies modeling in Pozega Valley, Sava Basin, based on limited well data and 2D seismic reflection data, while [20] applied an ANN for the lithology prediction of clastic sediments in the eastern part of the Drava Basin. However, there are very few studies in the Croatian part of the Pannonian Basin that apply machine learning algorithms for lithology prediction or classification. For example, ref. [21] applied the XGBoost algorithm for lithology classification in the Serbian part of the Pannonian Basin based on well logs, core data, and depositional environment information. In Gola Field, which is in the western part of the Drava Basin, 17 machine learning algorithms were tested and used for predictions on log data and five were used for unsupervised learning regarding lithological determination in order to clarify the subsurface relations in the area and apply the most suitable methods on the eastern Drava Basin dataset.
## 4 Analysis of Grain Shape Effects
### Effects of Grain Shape on Rock Macroscopic Parameters
The increase of the roughness was accelerated from models R4 to R6; thus, the decrease of the peak strength was also significantly reduced. In addition, it was found that the grain shape also had effects on the cohesion and internal friction angle of the model. These are two important parameters for characterizing the mechanical properties of mineral grains. Generally, the greater grain surface roughness means a higher internal friction angle. In bulk materials such as sand, samples with complex grain shapes generally have higher strength, which is mainly because the interlocking between mineral grains can increase the internal friction angle [12; 13; 40]. In other words, the strength of the bulk material is positively correlated with the internal friction angle. However, in rock materials, the strength of rock was found to be positively correlated with cohesion. Taking the model with circular grains (shape number 1) as an example, as shown in Figure 9, the peak compressive strengths of the model under three confining pressures (0, 50, and 200 MPa) were obtained. Three ultimate stress circles and their common tangent line were further obtained according to the confining pressure and the corresponding peak compressive strength. The intersection of the common tangent line and the \(\tau\) axis was the cohesion of the model. Figure 10 shows the effects of grain shape on rock cohesion. It can be seen that the effects of grain shape on rock cohesion were similar to the effects on rock uniaxial peak strength. In PFC models, the mineral grains are bonded together, and from a macroscopic view, the cohesion controls the strength of the model. Here, the peak strength of the rock increased with the increase of the cohesion. Figure 9: Cohesion of the model with circular grains (shape number 1).
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 6 Future Directions
### Resilience and Reliability for Real-World Environments  
The operational environments for edge devices often include harsh elements like extreme weather conditions that inevitably compromise hardware and sensor integrity over time [52]. Ensuring consistent AI performance in these environments demands robust hardware design, but it also relies on fault-tolerant software that can handle partial failures gracefully. For instance, sensor readings may become noisy or intermittent, yet the AI system must still produce reliable inferences [29]. Traditional machine learning models trained on clean, consistent datasets may falter in such real-world conditions. In response, developers employ redundancy strategies, sensor fusion, and error-correcting techniques to mitigate hardware-induced variability [61].  
At the software layer, models can be trained with augmented data that simulate potential noise or missing channels, improving resilience during inference. Another approach is to integrate continual learning methods that periodically retrain or fine-tune models in situ, helping the system adapt to evolving environmental conditions or device aging [9]. These strategies, while effective, add additional layers of complexity that must be balanced against already-constrained resources [59].