esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles of the Sensor Probes
The output voltages of a Hall sensor (\(V_{H}(t)\)) is proportional to the z-component of the total magnetic field which is perpendicular to the sensor surface, as expressed in Equation (4); where, \(k\) is the Hall constant and \(I_{H}\) is the current supply to the Hall sensor. In the signal processing system circuits, we use a root-mean-square (RMS) circuit for each Hall sensor to convert the time-varying signal to a DC signal  
Figure 1: Electromagnetic testing of the moisture separator reheater (MSR)-finned tube. (**a**) Magnetic flux leakage testing (MFLT); (**b**) eddy current testing (ECT) and (**c**) partial saturation eddy current testing (PSECT). before the analog-to-digital converter (ADC). Thus, the final obtained signal is the RMS of the Hall voltage output, as expressed in Equation (5). \[ \begin{split} B(t)=B_{M}+B_{V}+B_{D}+B_{B0}\sin(2\pi ft)+\sum B_{C0 }Sin(2\pi ft+\Delta\alpha_{i})\\ +\sum B_{F0}Sin\Big{(}2\pi ft+\Delta\beta_{j}\Big{)}\\ V_{H}(t)=k\times I_{H}\times B_{r}(t)\\ V_{RMS}=\sqrt{\frac{1}{T}\int_{0}^{T}[V_{H}(t)]^{2}dt}\end{split}  \tag{3}\]
## 3 Technology
### State of the Art  
The possibility to increase the measurement dynamic range by fast SPAD gating opened the way to previously unthinkable applications not only in the biomedical field but also in other fields, which will be discussed in the next section. In parallel, the technique was further developed by increasing the gating rate, the speed of the gating transitions [93] and the resulting timing resolution [94], thus allowing the fabrication of commercial TG SPAD modules [95]. On the other hand, at the research level, the effort was devoted to miniaturization of prototypes as the first gated SPADs, as well as the above-mentioned commercial modules, were devices of a volume of few \(\mathrm{dm^{3}}\) and a weight \(>\) 1 kg, preventing from envisaging both wearable diffuse optical systems and dense arrangements of detection points to further increase the signal or for a step towards tomographic systems. In 2015, a first compact probe embedding a TG SPAD (featuring 200 \(\upmu\)m active area diameter) and related circuits for quenching and restoring the detector (with also a probe-hosted pulsed laser diode to fabricate a complete TD-DO optode) was demonstrated. It allowed the use in direct contact with the tissue under investigation, avoiding the use of cumbersome optical fibers and increasing the signal collection thanks to the large numerical aperture of the detector (approaching 1) [61]. In the following years, other researchers in the world started to work on similar concepts to develop their own compact TG devices and optodes based on dummy signal generation [96; 97; 98; 99; 100]. In these cases, the TG detector active area has a diameter of few \(\sim\)10 \(\upmu\)m, limiting the collection capability of diffused light [100].
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 5 Structural Analysis
### Simulation Results and Discussion
#### 5.2.2 Displacement and Area of Plate  
The relationship between plate area and displacement is significant since the displacement of the supports or structures attached to the floor or blast wall due to the blast pressure is influenced by the plate area. Figure 18a presents the location of measurement for displacement, for different area sizes. Explosion for case-1 is selected to investigate the relationship. One side of each area is fixed, and the dimension of the other side is changed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load Case**} & \multicolumn{4}{c}{**Material**} \\ \cline{2-5}  & & **S235** & **S355** & **S460** \\ \hline Overall Peak & Min & \(-\)0.0852 & \(-\)0.0842 & \(-\)0.0842 \\ Case-1 & Max & 0.0882 & 0.0885 & 0.0887 \\ \hline Overall Average & Min & \(-\)0.0485 & \(-\)0.0485 & \(-\)0.0485 \\ Case-2 & Max & 0.0487 & 0.0488 & 0.0488 \\ \hline Group Peak & Min & \(-\)0.0814 & \(-\)0.0810 & \(-\)0.0809 \\ Case-3 & Max & 0.0824 & 0.0828 & 0.0831 \\ \hline Proposed & Min & \(-\)0.0556 & \(-\)0.0554 & \(-\)0.0554 \\ Case-4 & Max & 0.0398 & 0.0403 & 0.0404 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Minimum and maximum displacements of frame (m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load Case**} & \multicolumn{4}{c}{**Material**} \\ \cline{2-5}  & & **S235** & **S355** & **S460** \\ \hline Overall Peak & Min & \(-\)0.090 & \(-\)0.089 & \(-\)0.089 \\ Case-1 & Max & 0.072 & 0.073 & 0.073 \\ \hline Overall Average & Min & \(-\)0.090 & \(-\)0.057 & \(-\)0.057 \\ Case-2 & Max & 0.072 & 0.045 & 0.045 \\ \hline Group Peak & Min & \(-\)0.088 & \(-\)0.086 & \(-\)0.086 \\ Case-3 & Max & 0.070 & 0.069 & 0.069 \\ \hline Proposed & Min & \(-\)0.100 & \(-\)0.099 & \(-\)0.099 \\ Case-4 & Max & 0.047 & 0.047 & 0.047 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Minimum and maximum displacements of plate (m).  
Figure 18b presents the amplitude of displacement of the plate. The displacement of the middle point of each area is investigated. The size of each area is shown in Table 10. The magnitude of displacement increases according to the size of the plate; however, it is not exactly a proportional relation.  
The ratio of displacement increases until the ratio of the two sides of the plate is 1:1, i.e., Area-1 to Area-2 is larger than the ratio of increase in displacement after the ratio of the two sides of the plate is 1:1 (i.e., Area-2 to Area-3). The displacements for S355 and S460 are similar to that for S235, as summarized in Table 10.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 2 Materials and Methods
### Detection of Angiogenesis  
In order to investigate the effects of PAM on the angiogenesis of HUVECs, we conducted in vitro matrigel angiogenesis experiment, which was relatively simple, easy to control, easy to quantify, and could well simulate the process of angiogenesis in vivo. Firstly, 60 uL matrix glue (Matrigel, Corning, NY, USA) was added to each well of a precooled 96-well plate (Corning, NY, USA) mixed with serum-free basal medium at 1:1, and incubated in an incubator at 37 \({}^{\circ}\)C for 30 min. After the matrix glue solidified, the cells in logarithmic growth phase were digested and collected. In the control group, fresh complete culture medium was added to resuscitate the cells to a cell density of 4 \(\times\) 10\({}^{5}\) cells/mL, while in the other groups, the cells were resuspended with PAM for 15 s, 30 s, 45 s, 60 s, and 75 s to a cell density of 4 \(\times\) 10\({}^{5}\) cells/mL. The solidified 96-well plate was taken out and 50 uL cell suspension mixed with fresh complete culture medium or PAM was added to each well. After static settlement for a while, when all cells sank on the substrate surface glue, the plate was put into the incubator again. At 2 h, 4 h, 6 h, 8 h, 10 h, the images were collected, and the ImageJ software was used to record and analyze the number of nodes, junctions, blood vessels, number of vessels, and length of major vessels.
## 6 Results and Discussions
### Case 1  
The system performances with the proposed CDM + LQG controller, only CDM, and the conventional integral controller are tested at nominal parameters and load change in area-1 (\(\Delta\text{PL}_{\text{L1}}\) assumed to be 0.02 pu at \(t=30\) s). Figure 6 shows the simulation results in this case. Figure 6a illustrates the frequency deviation for each area, and Figure 6b shows the tie-line power change for each area. It is obtained that the systems with both CDM + LQG and only CDM controllers are more stable and fast when compared to the system with a traditional integrator.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Area** & \(K\) **(s)** & _D_**(pu/Hz)** & _2\(H\) **(pu/s)** & \(R\) **(Hz/pu)** & _T_**\({}_{\text{g}}\) **(s)** & _T_\({}_{\text{t}}\) **(s)** & _T_\({}_{\text{g}}\) \\ \hline Area-1 & \(-0.3\)/s & 0.015 & 0.1667 & 3.00 & 0.08 & 0.40 & \(T_{13}=0.25\) \\ \hline Area-2 & \(-0.2\)/s & 0.016 & 0.2017 & 2.73 & 0.06 & 0.44 & \(T_{21}=0.20\) \\ \hline Area-3 & \(-0.4\)/s & 0.015 & 0.1247 & 2.82 & 0.07 & 0.3 & \(T_{31}=0.25\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and data of a practical three-control-area power system (\((P_{e})_{\text{Base}}=800\) MVA).  
Figure 6: _Cont._
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## Materials and Methods
### Ultra-Bright LED White Light Source  
For illumination, a phosphor based ultra-bright LED white light source (CBT-90 White LED, Luminus Inc., Sunnyvale, CA, USA) is employed. The source is phosphor based and converts blue light from an LED-chip into the yellow spectral range. The emitted light mixture exhibits a white spectrum, which is generally more continuous compared to a mixture of red, green, and blue (RGB) LED chips.  
The source has a color rendering index (CRI) of 76 (for comparison: lighting in surgical rooms requires a CRI \(>\) 85 [16]). In general, the closer the CRI of a light source is to the value of 100 the better the source can render the colors of real-world objects, i.e., skin lesion in our case. This then corresponds to the color perception during daylight.  
The LED is mounted on a self-made reflector that was simulated and designed with OpticStudio (Zemax LLT, Kirkland, WA, USA) aiming at homogeneous illumination of a target at the highest possible intensity in the distance of interest (ca. 45 cm from the liquid lens). The bright illumination has the advantage, that the illumination of the target is well-defined and less influenced by the surrounding light. This is important for the color adjustment of the camera, which is of interest as the color of a lesion is a relevant parameter in dermatoscopy. For example, it is also taken into account in the ABCDE rule of dermatoscopy [6; 7]. Also, it is of interest to evaluate the temporal evolution of a lesion. Furthermore, in the field of inflammatory skin diseases, different color variations need to be identified, e.g., shades of red. Another useful application area could be the color identification of hematoma allowing to predict the age of injuries of patients [17; 18]. For detection of changes of the colors of lesions, the comparability of images taken at different examination sessions and different times must be ensured. Also, different disease stages could be assigned by evaluating the color (e.g., redness in the PASI as well as the Eczema Area and Severity Index (EASI), respectively). To fulfill these requirements, in particular, to allow for a more natural color representation comparable to the perception of a physician, color calibration is important. This ultimately also increases the acceptance of the system by the dermatologists.
## 3 Ticket Categorization and Visualization Method
### Ticket Feature Visualization  
Our method creates a plot and heatmap to show the keywords included in each ticket category and the relationships among categories. First, the relevance of each ticket category is visualized by principal component analysis of the term frequency as follows:  
* Step 4.1. To obtain the features of ticket categories, calculate the average of the term frequency vectors of the tickets per category. For example, the term frequency of category \(C_{1}\) is \(\{T_{button},T_{dialog},T_{ui},T_{feature},T_{item}\}=(\{0.30,0.20,0,0,0\}+\{0,0.2 0,0.30,0,0\})/2=\{0.15,0.20,0.15,0,0\}\) (Figure 2).
* Step 4.2. To simply visualize the relationships among categories, compress every term frequency vector into a two-dimensional vector by PCA and plot the results in two-dimensional space. For example, four categories are mapped in Figure 2. Categories \(C_{1}\) and \(C_{3}\) have similar features. Then our method creates a heatmap, which indicates the kind of terms appearing in each ticket category using the following steps:
* Step 4.3. To make lists of keywords, select the top clustering terms from each ticket category and manually construct collocations from the extracted terms. It should be noted that terms are not limited to nouns but can include all terms. Then group the collocations and terms. For example, two keywords, \"add button\" and \"sort items\", are constructed, and keyword \"UI\" and \"dialog\" are classified into the same group (Figure 2).
* Step 4.4. To visualize the category features independent of the difference in the number of tickets included in the categories, calculate the density of keywords appearing in each group. The keyword density of a category is calculated as follows:First, count the number of keywords in all text data of tickets in the category. For example, the keyword \"add button\" appears once in the title and once in the description of ticket \(I_{1}\). Hence, the number of keywords in category \(C_{1}\) is two (Figure 2).
* Then multiply the number of keywords by the length of the keyword and divide by the sum length of the text data in the tickets for the category. The length of ticket \(I_{1}\) is nine and that of ticket \(I_{2}\) is eight. Hence, the density of keyword \"add button\" is \(2\times 2/17\approx 0.24\).
* Step 4.5. To visualize the features of categories quickly, create a heatmap using the keyword density of all categories.
## 4 Numerical Results and Discussion
### Free Vibration of a Two-DOF System
#### 4.1.2 Efficiency Comparison  
Computational cost is also a crucial issue for a method. Assume the time domain is [0, 2 \(\times\) 10\({}^{3}\)] in this case. In order to achieve the same accuracy of RelErr = \(10^{-6}\), \(h=5\)\(\times\)\(10^{-2}\) and \(h=1\)\(\times\)must be used for RKM and NMA, respectively; that is, RKM needs 4 \(\times\) 10\({}^{5}\) time steps and NMA needs 2 \(\times\) 10\({}^{6}\) time steps. However, we can achieve the same accuracy by using 1588 elements and each element of size \(h\) = 1.26 \(\approx\) 2.828/\(\omega_{2}\) has only three Gauss-Lobatto-Legendre sampling points (\(N\) = 3 is the least number of time sampling points in an element; see Table 2). If \(N>3\), the accuracy of DQTFEM is more than 10\({}^{-6}\). For example, if \(N=20\) in an element of size \(h\) = 11 \(\approx\) 25.11/\(\omega_{2}\), the accuracy will be 10\({}^{-9}\). In other words, the lowest accuracy of DQTFEM is 10\({}^{-6}\). This is the reason why the data corresponding to RelErr = 10\({}^{-2}\) are not provided in Table 3, which indicates that DQTFEM is much more efficient than the other two methods, especially if high accuracy is required. Another observation is that RKM is more efficient than NMA.  
NMA is a symplectic method of second order, in which the amplitude can be rigorously preserved for linear systems, but its phase error is larger than that of RKM. It is well known that dynamic responses must be represented by amplitude and phase together; therefore, RKM is more efficient than NMA for achieving the same accuracy. However, the weakness of RKM is its numerical dissipation and the weakness of NMA is its larger phase error. For long-time kinematics simulations in which only amplitude is taken into account, NMA is a better choice.
## 3 Artery Deformation Modelling
### Fluid-Structure Interaction (FSI) Modelling  
FSI coupling was performed by constructing two separate models that comprised the fluid and the structural domain. In solving the FSI problems, neither Eulerian nor the Lagrangian formulations were primes to both the domain. Lagrangian equation cannot handle large deformations for fluid systems, whereas Eulerian formulation compromises accuracy when applied to the solid domain. Generally, the FSI was resolved using a standard arbitrary Lagrangian-Eulerian (ALE) formulation [37]. The fluid domain is permitted to deform arbitrarily in the ALE method so that its boundaries follow the structural domain's deformation [38]. These two methods were combined and used to solve the structural problems by Lagrangian formulation. The fluid domain and structural domain two-way coupling were attained by using commercial computational software ANSYS FLUENT and ANSYS Structural (ANSYS Inc., Canonsburg, PA, USA) of version 2020R1. ANSYS Mechanics uses a finite-element method to solve the structural domain, and ANSYS FLUENT is finite-volume-based software for computational fluid mechanics analysis. In this research, the fluid and solid are both coupled in a two-way system and solved iteratively within each specified time step of 0.005 by applying appropriate conditions at the fluid-structure interface until the system's residual is lower than a specified tolerance.  
The FSI problems have the conservation of mass and momentum along with the interfaces [39]. These values are content of the displacement compatibility and traction equilibrium at \(\Gamma_{f,\mathrm{S}}\),  
\[\overset{\rightarrow}{u_{f,\mathrm{T}}}=\overset{\rightarrow}{u_{s,\mathrm{ T}}} \tag{31}\]  
\[\overset{\rightarrow}{t_{f,\mathrm{T}}}=\overset{\rightarrow}{t_{s,\mathrm{ T}}} \tag{32}\]  
In the above equations, where \(\overset{\rightarrow}{t_{f,\mathrm{T}}}\) is defines the fluid displacement at the interface and \(\overset{\rightarrow}{u_{s,\mathrm{T}}}\) define the solid displacement at the interface in Equations (31) and (32). Similarly, the forces of the fluid and solid on the interfaces is defined as \(\overset{\rightarrow}{t_{f,\mathrm{T}}}\) and \(\overset{\rightarrow}{t_{s,\mathrm{T}}}\) respectively in Equation (33) is frequently transcribed in stress tensor as,  
\[\overline{\overline{\sigma}}\cdot\overset{\rightarrow}{n_{s,\mathrm{T}}}=- \overline{\tau}\cdot\overset{\rightarrow}{n_{f,\mathrm{T}}} \tag{33}\]  
where, \(\overline{\overline{\sigma}}\) is the stress tensor, \(\overset{\rightarrow}{n_{f,\mathrm{T}}}\) and \(\overset{\rightarrow}{n_{s,\mathrm{T}}}\) are the fluid and solid normal interfaces respectively. Equations (27)-(33) have been adapted from previously published articles [20; 40].
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 3 Targeted Scenario
We assume a TDD massive MIMO system, and so, thanks to channel reciprocity, if a device is allocated a pilot in a given time slot, it can both transmit and receive in that slot. A scheduling algorithm determines which devices are allocated pilots in each time slot. The scheduler runs on edge or local cloud computing resources, i.e., in either the second or third layers in Figure 1, depending on how much computational resources are available at each of these layers. Figures 2 and 3 show how the scheduling works. A number of end devices are served by a number of CSPs (Figure 2). In each scheduling window, the scheduler calculates a schedule that covers a certain number of time slots, which collectively form a scheduling frame (Figure 3). The frame then repeats for the duration of the scheduling window. The time slots are closely related to the wireless channel properties since we consider each time slot as one coherence interval, and thus, the length of a slot depends on the environment as well as the mobility of the users. In cases where the channel coherence time is long, it would also be possible to break each coherence interval into multiple time slots; however, we do not consider this case in this paper. Figure 2: End devices communicate wirelessly with coherently operated antennas, either in a centralized array (base station) or distributed over multiple contact service points in large intelligent surfaces (LIS), as shown here. The antennas are, in turn, connected to an edge computing unit where the scheduler runs, assigning pilot signals to end devices in each time slot.
## 2 Materials and Methods
### WioU Bounding Box Loss Function  
During the manual labeling process of the Apple dataset, human factors may introduce low-quality anchor frames, potentially impacting the IoU loss function's efficacy during model training. The IoU loss function measures the similarity between predicted and actual bounding boxes by calculating the ratio of their intersection area to the area of their combined region, as depicted in Figure 12.  
Figure 11: Network structure of VEW-YOLOv8n.  
Figure 10: Improvement of the neck network.  
The GioU [31] loss function extends the IoU by including the minimum external rectangle, including both the predicted and actual boxes. It accounts for overlapping and non-overlapping regions, effectively addressing gradient disappearance issues when boxes do not intersect. The GioU loss function reverts to the IoU loss function when the real and predicted boxes overlap or contain each other within the same dimension. The DioU [32] loss function further expands on GioU by considering the Euclidean distance between centroids of the predicted and real boxes, as well as the diagonal distance of their minimum external rectangle. However, it does not account for aspect ratio relationships. The CioU loss function, used in YOLOv8n's bounding box loss function, builds upon DioU by adding aspect ratio consistency between the predicted and real boxes, including an aspect ratio penalty term. Although CioU addresses several shortcomings, it does not provide uniform gradient signs for anchor box widths and heights and involves a complex computational process that increases computational volume and training time. The SioU [33] loss function combines angle, distance, and shape costs but does not consider dynamic gradient updating. The WioU loss function addresses these issues by weighting the IoU based on the predicted and real frame region, introducing a dynamic non-monotonic focusing mechanism. It uses \"outlier\" as a new quality assessment criterion for anchor frames and dynamically updates the gradient gain allocation strategy.
## 3 FEM Model of the Rotating Target
### Numerical Investigations on Rotating Target  
Boundary and load conditions previously described were implemented in the FEM model for studying the thermo-mechanical behaviour of the target.  
In Figure 14, the temperature field on the target surface is reported. The upper region of the target is characterised by higher values of temperature. The temperature decreased towards the condenser region due to the presence of the external cooling system.  
Figure 14: Target temperature field in \({}^{\circ}\)C.  
Figure 13: Comparison in terms of von Mises stresses for case (a) and case (b) for the different number of cycles.  
The Von Mises stress map is given in Figure 15. Higher mechanical stress is in the central region between the evaporator and the condenser (maximum is 111 MPa), with a corresponding temperature of 62 \({}^{\circ}\)C.  
Figure 15: (a) Von Mises stress distribution in MPa. (b) Von Mises stress distribution in MPa (detail of the evaporator). (c) Von Mises stress distribution in MPa (detail of the condenser).  
Moreover, it can be clearly observed that the maximum von Mises stress in the target surface did not exceed the yield strength of the material, and thus the global structural integrity is assured. Furthermore, the conical structure was chosen to reduce thermal effects.  
Furthermore, the presence of cyclic thermal stresses due to temperature changes can lead to the failure of the rotating target following the formation of macroscopic cracks [20]. Thermal fatigue may occur also without mechanical loads. The cracks, in fact, could develop after many cycles of heating and cooling. For this reason, thermal fatigue life represents the major issue for the lifetime of the target.  
An estimation of the fatigue effect can be carried out by comparing the stress amplitude with the allowable limit.  
The allowable limit is often proportional to the material strength. The proportionality factor for metal and alloy is in the range of 0.35 and 0.5 [21]. The allowable limit turns out to be equal to around 90 MPa. On the other hand, from the numerical simulation performed, the stress amplitude results are equal to 14.85 MPa in the region of the evaporator (Figure 13). From the comparison, the stress amplitude is lower than the fatigue limit for a safety coefficient of about 5.  
Finally, in Figure 16, the displacement field is shown: the maximum displacement reached was 1.164 mm, in the evaporator region. Lower displacements were exhibited in the condenser.