esa-sceva/satcom-chunk-collection huggingface dataset:
# Morphological Dynamics of Leukemia Cells on TiO\({}_{2}\) Nanoparticle Coatings Studied by AFM
Briefly, the adhesion of leukemic cells on TiO\({}_{2}\)-m coatings is a fast dynamic process involving expansion and contraction of the membrane induced by surface functional groups, particularly hydroxyl groups, which act as chemotactic factors. Additionally, the development of membrane structures such as blebs and pseudopods, characteristic of amoeboid locomotion, was identified by scanning electron microscopy (SEM). These structures were observed to envelop TiO\({}_{2}\)-m nanoparticle aggregates that protrude from the coating surface to internalize them into the cytoplasm, indicating that nanoparticle internalization was triggered by TiO\({}_{2}\)-m. Amoeboid-like locomotion thus appears to be an integral part of the interaction of leukemic cells with TiO\({}_{2}\)-m coatings. In sum, these observations show that TiO\({}_{2}\)-m nanoparticle coatings have an enormous potential for ex vivo PDT of non-adherent cancer cells since these coatings have been shown to efficiently and selectively trap acute lymphoblastic leukemia cells. However, the detailed morphological changes and the variation of cellular properties during the adhesion of leukemia cells to these TiO\({}_{2}\)-m coatings are still largely unknown. Atomic force microscopy (AFM) is a powerful tool for the imaging and characterizing of various biological entities such as biomolecules, cells, and tissues [16; 17; 18; 19; 20]. It provides three-dimensional surface topographic information and thus enables the quantification of morphological cell properties such as height, volume, and membrane roughness [21; 22; 23; 24; 25]. Because of that, AFM has become an important technique in investigating the complex behaviors of cells and cellular components, most importantly the cell membrane. The cell membrane plays a key role in numerous physiological processes such as cell adhesion, cell-surface recognition, and migration [26; 27; 28; 29]. Therefore, the aim of this work was to characterize the initial stages of leukemia cell adhesion to the TiO\({}_{2}\)-m coatings by AFM. For that, leukemia cells were cultured on TiO\({}_{2}\)-m-coated glass substrates for times ranging from 10 to 60 min. Resulting changes in the morphology of the trapped cells were quantified by extracting various morphological parameters from the recorded AFM images, i.e., mean cell height, cell volume, area of contact with the substrate surface, and membrane roughness. We found that leukemia cell adhesion to TiO\({}_{2}\)-m nanoparticle coatings starts with rapid movements of expansion and contraction, after which the cells seem to enter an immobile state with their cell membranes being pinned by TiO\({}_{2}\)-m nanoparticle aggregates protruding from the coating surface.
## 3 Results and Discussion
### Statistical Evaluation of RTN Characteristics
Figure 10 shows the Gumbel plot of V\({}_{\text{RMS}}\) for various Halo implantation concentrations [52]. The number of MOSFETs with large RTN increases with an increase in Halo implantation concentration. This indicates that the high dose in the channel region or near the source/drain region results in high RTN because of channel percolation enhancement. Figure 11 shows the energy band diagrams and energy distribution of traps causing RTN for V\({}_{\text{gs}}\) of 0.57, 0.53, and 0.46 V, respectively [41; 42]. The difference between E\({}_{\text{T}}\) and E\({}_{\text{F}}\) for electrons is calculated using Equation (11). The blue shading and red solid bars in Figure 11 show the energy distribution of traps causing RTN in each measurement condition and common traps in all conditions, respectively. Although the shape of the distribution of each V\({}_{\text{gs}}\) is almost the same, and the energy of common traps in all V\({}_{\text{gs}}\) increases with decreasing V\({}_{\text{gs}}\). The conduction band edge (E\({}_{\text{C}}\)), the bottom sub-band energy (E\({}_{\text{sub}}\)), and 2 nd sub-band energy (E\({}_{\text{2 nd}}\)) in the inversion layer and E\({}_{\text{F}}\) are indicated in Figure 11 [50]. The energy levels of sub-bands were calculated using Equation (14) [50]. \[\text{E}_{\text{j}}=\left[\frac{3\text{h}\text{d}\text{E}_{\text{s}}}{4\sqrt{ 2\text{m}_{\text{x}}}}\left(\text{j}+\frac{3}{4}\right)\right]^{\frac{2}{3}}, \text{j}=0,1, \tag{14}\]  
where E\({}_{\text{s}}\) is the electric field, h and m\({}_{\text{x}}\) represent [PERSON]'s constant and effective mass of electrons, respectively. E\({}_{\text{j}}\) is jth sub-band energy, and E\({}_{\text{sub}}\) and E\({}_{\text{2 nd}}\) represent E\({}_{0}\) and E\({}_{1}\)  
Figure 10: Gumbel plot of the RTN amplitude for MOSFETs with varying channel doping. The channel doping concentrations are varied \(2.3\times 10^{17}\), \(3.6\times 10^{17}\), \(5.2\times 10^{17}\) cm\({}^{-2}\), respectively. Figure 9: Gumbel plot of the RTN amplitude for MOSFETs with varying channel doping. The channel doping concentrations are varied \(2.3\times 10^{17}\), \(3.6\times 10^{17}\), \(5.2\times 10^{17}\) cm\({}^{-3}\), respectively. respectively. The main energy distribution for each V\({}_{\rm gs}\) locates higher energy than the conduction band edge. It is considered that the energy level of traps is widely distributed, and the energy of the detected traps is determined by the electron energy in E\({}_{\rm sub}\) and E\({}_{\rm 2 nd}\). Conversely, the energy of common traps in all V\({}_{\rm gs}\) increases with decreasing V\({}_{\rm gs}\) because the influence of trap energy on V\({}_{\rm gs}\) is larger than that of electron energy in the channel.
# Underestimation of Dry Matter of Anaerobic Media with High Bcarbonate Concentration
## 3 Results and Discussion
### Bicarbonate Interference
#### 3.2.4 Impact of Thermal Bicarbonate Decomposition on OM Values  
The organic matter content (OM) is not intrinsically affected by bicarbonate decomposition, all the organic materials were burned between 105 and 550 \({}^{\circ}\)C [2,5], and as carbonates are stable at 550 \({}^{\circ}\)C, the decomposition of bicarbonate below 100 \({}^{\circ}\)C does not directly impact the OM determination. However, OM content is usually expressed as a percentage of DM and therefore it is affected through a ripple effect.  
The Figure 1 below illustrates this paradox. Considering an anaerobic medium that contains 120 g DM, 72 g OM, and 10 g bicarbonate par liter. Weighing 10 mL in a crucible, i.e., 10 g (assuming the density of 1 g/mL), 1.2 g DM should remain in the crucible after drying at 105 \({}^{\circ}\)C, and 0.48 g OM should be weighted after burning at 550 \({}^{\circ}\)C, as shown in the blue boxes of Figure 1. Finally, the organic matter represents 7.2% of fresh matter (FM) and 60% of dry matter (DM). Considering now that 37% of the bicarbonate is lost during the drying (i.e., 0.037 g), as shown in the green boxes in Figure 1, for the same amount of 10 g in the crucible, 1.163 g DM will remain in the crucible after drying at 105 \({}^{\circ}\)C, and 0.44 g OM will be weighted after burning at 550 \({}^{\circ}\)C, giving an OM content of 7.2% FM and 61.9% DM.  
The balances corresponding to the green boxes show that the experimental DM content is underestimated, and the OM content, expressed as % of DM, is inversely overestimated.
## 5 Experimental Results and Discussions
### Results  
In the following presentation of results, the estimated input forces are plotted together with the raw, measured input forces, not the filtered ones, for a more genuine comparison, although the identification used the filtered, measured input forces. Table 1 shows the estimates of the dynamic parameters, which consisted of the barycentric inertial parameters and the friction parameters. It can be seen that the estimated inertial parameters were larger than their design values. This was expected since the actual masses should be larger than their design values due to the attached additional parts, such as the nuts, cables, etc. The plots of the estimated and measured input forces created using the Coulomb-viscous friction and Stribeck friction models are shown in Figure 12a,b, respectively. It is shown that the latter friction model provided a better fit, whereas the former friction model was not adequate to represent the real dynamics. This justified the use of the Stribeck friction model instead of the Coulomb-viscous friction model.  
The variation of the absolute values of the vertical reaction forces \(R_{y}\) on the three prismatic joints is shown in Figure 13. The absolute values are plotted here instead of the signed values to show the variation of the values more clearly. The variation of these normal forces resulted in the variation of the friction forces. The solution obtained by considering the varying friction, as shown in Table 1, was validated by applying some other exciting trajectories that were different from the one used in the identification. Figure 14 shows a full-circle trajectory executed for the validation  
Figure 12: Comparison between the estimated and measured input forces: (**a**) using the Coulomb-viscous friction model and (**b**) using the Stribeck friction model.  
Figure 14: Full-circle validation trajectory.  
Figure 13: Variation of the absolute values of the vertical reaction forces on the three prismatic joints during the identification motion.  
Figure 16: Comparison between the estimated friction forces and the estimated input forces.  
Figure 15: Comparison between the estimated and measured input forces in the validation motion.
### Proposed Solution  
The main goals of this paper are to establish a reasonable and effective FSCM (fluid-structure coupling model) for the arch dam and to study the influence of the evolution of structural states and material properties on the OSP of arch dams; the OSP method that is most suitable for the model in this paper is optimized. Therefore, this paper considers the impacts of reservoir compressibility and structural states, as well as the material properties' evolution on the dynamic characteristics of arch dams. It improves and prefers the IAHA, proposing an optimal sensor quantity determination method based on MAC, fitness values, and S criteria. It establishes a concrete arch dam OSP framework based on IAHA, considering the effects of cracks, contraction joints, and elastic modulus zoning degradation of the dam body. The feasibility of the above method is validated through a concrete arch dam with cracks, aiming to provide technical support and new research ideas for the OSP of dams. The final sensor placement is carried out for the current state of this arch dam after 45 years of operation; the ONS is selected, and the results of the spatial location of the sensors are given. The primary contributions and innovations of this paper are as follows:  
1. Based on FSCM and considering the compressibility of reservoir water, an arch-dam-reservoir-foundation model is established to calculate the modal data required for the OSP of an arch dam. This method considers the damping effect brought by the complex coupling motion between the reservoir water and the dam body and can provide more reasonable and realistic modal data for arch dam OSP. This investigation includes the effects of horizontal cracks, vertical cracks, contraction joints, dam body elastic modulus zoning, dam body elastic modulus zoning degradation, and combinations of these factors on the OSP of the arch dam.
2. An IAHA is proposed and compared with five intelligent algorithms to verify its superiority in performance and sensor layout effectiveness, providing a new intelligent algorithm for the OSP problem of hydraulic structures. A method for selecting the ONS based on MAC, fitness values, and S criteria is proposed that can accurately determine the ONS under different working conditions and is worth promoting in the field of OSP.
3. In the current state of 45 years of actual operation of the arch dam, considering factors such as cracks, contraction joints, and elastic modulus zoning degradation of the dam body, it is finally determined that the number of sensors required to be placed for the arch dam is 36, and the overall spatial position of the sensor layout is uniform and reasonable.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Acoustic Emission Sensors in Port Crane Maintenance
#### 4.2.2 Types of Acoustic Emission Sensors  
Piezoelectric transducers are widely used in AE testing, owing to their high sensitivity, wide frequency response, and good stability. They convert mechanical stress into electrical signals via the piezoelectric effect, typically using ceramic materials such as lead zirconate titanate or quartz crystals [106; 107]. These sensors are widely used in AE systems owing to their high sensitivity, broad frequency response, and fast reaction time.  
Fiber-optic sensors offer several advantages over piezoelectric sensors, particularly in environments with high electromagnetic interference (EMI). These sensors operate based on fundamental optical principles, typically interferometry or intensity modulation, whereby AEs induce strain or pressure variations in the optical fiber. These disturbances alter the phase, amplitude, or polarization of light propagating through the fiber, which can be measured and converted into an electrical signal by photodetectors.  
Common configurations include Fabry-Perot interferometers and fiber Bragg gratings (FBGs), which are highly sensitive to dynamic strain and pressure changes [108; 109; 110]. In the case of FBG-based AE sensors, an acoustic wave causes a shift in the Bragg wavelength, which can be monitored for real-time damage assessment. These systems are inherently immune to EMI, chemically inert, lightweight, and capable of multiplexing multiple sensors along a single optical line [111; 112]. This makes them ideal for harsh or constrained environments such as shipyards, offshore platforms, nuclear plants, and high-voltage port crane systems.  
Their high bandwidth and spatial resolution enable early detection of damage phenomena such as crack propagation or delamination in complex structures, which may otherwise go unnoticed using conventional sensors.  
As mentioned in a study by [PERSON] and [PERSON] [113], the integration of AE testing with IoT enables continuous real-time data collection and monitoring. Pressure vessels, for example, can be embedded with sensors that provide continuous real-time data, alerting operators to minute stress changes indicative of crack initiation or wall thinning [113].
## 3 Vehicle Clustering Algorithms  
### LEACH Routing Protocol  
The LEACH protocol is a clustering-based routing algorithm. Its basic idea is to reduce the number of direct communications with the base station (RSU) by periodically rotating the election of cluster heads with equal probability. In each round, new cluster heads are selected, and the energy load of the entire network is evenly distributed among each node, thereby reducing the network communication energy loss and extending the overall network cycle. Each cycle of the algorithm includes phases of cluster-head election, broadcasting, cluster establishment, and stable data transmission. In the initial stage of cluster-head election, each network node determines whether it is selected as a cluster head based on the initial percentage of cluster-head election and the number of times it has been elected as a cluster head. Each node generates a random number between 0 and 1, and if the number is less than the threshold, the node is selected as a cluster head for the current round. The threshold is defined as:  
\[T(n)=\left\{\begin{array}{l}\frac{p}{1-p\times(\text{mod}\frac{1}{p})},n\in G \\ 0,\ else\end{array}\right. \tag{10}\]  
Among them, \(p\) represents the predetermined percentage of cluster heads; \(G\) denotes the set of nodes that have not been selected as cluster heads in the past \(1/p\) rounds; \(r\) represents the current round number, and \(n\) represents the current node.  
The energy consumed by a vehicle node when transmitting \(k\) bits of data to an adjacent vehicle node at a distance \(d\) is given by the following equation:  
\[ETx(k,d)=\left\{\begin{array}{l}E_{elec}\cdot k+\varepsilon_{fs}\cdot k\cdot d ^{2},d<d_{0}\\ E_{elec}\cdot k+\varepsilon_{mp}\cdot k\cdot d^{4},d\geq d_{0}\end{array}\right. \tag{11}\]  
The energy consumed by a vehicle node when receiving \(k\) bits of data is given by the following equation:  
\[E_{Rx}(k)=E_{elec}\cdot k \tag{12}\]  
where \(E_{elec}\) is the energy required by a vehicle node for data processing, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the communication energy parameters, and \(d_{0}\) is the threshold. If the distance \(d\) between nodes is less than \(d_{0}\), the the Free Space Communication (FSC) model is used. Otherwise, the the Multipath Fading Channel (MFC) model is used. The calculation method for the threshold \(d_{0}\) is given by the following equation:  
\[d_{0}=\sqrt{\frac{\epsilon_{fs}}{\epsilon_{mp}}} \tag{13}\]
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 5 Simulation and Performance Evaluation
### PPO for Task Offloading Strategy
#### 5.2.2 Performance Evaluation
Where (a) shows the impact of the transfer speed on the average offload delay, (b) shows the impact of the the impact of the task size on the average offload delay, (c) shows the impact of the remaining computing loads, (d) shows the impact of the computation task complexity. When the data transmission speed of the RSU and BS is low, it has a significant impact on the total offloading delay. However, when the data transmission speed of the RSU and BS reaches a certain level, its impact on the total offloading delay becomes smaller. When the task generation size is large, it will have a significant impact on both transmission delay and computation delay. On the other hand, when the task complexity is high, it only affects the computation delay. Both of these variations will result in different degrees of impact on the total offloading delay. Therefore, when the task generation size and task complexity are  
Figure 6: Average offloading completion times (delay) for different offloading strategies. Figure 7: Average reward comparison of vehicle offloading strategies under different conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **PPO** & **“load\_com”** & **“pos\_com”** & **“speed\_com”** \\ \hline Average value & 20,141.9 & 8313.9 & 16,206.6 & 19,783.9269 \\ Standard deviation & 565.5 & 267.4 & 404.3 & 591.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Stability analysis of strategy under different conditions (100–300 epochs). large, the total offloading delay will increase. Moreover, when the remaining computing resources of the offloading target are extremely limited, the total offloading delay increases significantly. However, when the remaining computing resources reach a certain value, their impact on the total offloading delay decreases. In summary, it can be considered that the training model in this paper can appropriately adjust the task offloading strategy in response to changes in specific influencing factors to reduce the offloading delay generated by vehicles in different scenarios.
## 3 Experimental Section
### Field Treatments and Sampling  
Three attenuated UV treatments, together with the control, were carried out. Polyester films A and B (Luckyfilm Co., Ltd., Baoding, China) were used to absorb solar UV components. A total of 60 grapevines were chosen for three replicates with each consisting of 20 plots planted with single trees. During the experiments, each grapevine contained a main vine with 14-15 fruiting branches. On each vine, two or three bunches were naturally exposed throughout the whole development (the control), and others were wrapped with film A or film B at specific developmental stages (treatments). As shown in Figure 7A, there were three types of attenuated UV radiation treatments: UV1 (polyester film was applied from 3 weeks after flowering (waf) to harvest), UV2 (from 3 waf to onset of veraison), and UV3 (from the onset of veraison to harvest). Film was rolled into an inverted funnel shape, opening on both ends, to assure similar temperature and humidity inside and outside. The polyester film was fixed on a fruit stem with a colored rope. Film A cut off 89.51% invisible sunlight of wavelengths below 380 nm (including majority of UV-A and total UV-B), whereas film B removed 98.97% invisible sunlight (including almost all UV-A and UV-B irradiation) correspondingly (Figure 7B).  
In the year of 2009, we applied the UV1 treatments with film A and film B with the objective of assessing the effect of attenuated UV radiation throughout grape development on grape volatile profiling. In 2010, three treatments (UV1, UV2 and UV3) with film A or film B were carried out to assess whether the effect of attenuated UV radiation was depended on development stages. Monthly solar radiation and UV radiation during fruit growing season in both of years, were provided by the China Meteorological Data Sharing Service System. From Figure 7B, we found that monthly solar radiation declined from June to August but UV radiation raised by approximately 7.2% of solar radiation in this region. There were no large differences in monthly UV radiation between both of vintages. For each treatment about 100-150 fruits per replicate were collected from different vines and bunches. Sampling was done at about 10 am at the indicated date. In the present study, veraison began at 9 waf in 2009 and at 8 waf in 2010, respectively, and fruit harvest was at 17 waf in both of vintages. The samples were transported to the laboratory on ice and immediately frozen in liquid nitrogen prior to storage at -80 \({}^{\circ}\)C.
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### Cluster Management  
Urban environments are characterized by high vehicle mobility and density. In such environments, if all vehicles request or disseminate messages, excessive transmission delays may occur due to the broadcast storm phenomenon. Therefore, to efficiently disseminate safety messages, it is necessary for each vehicle to form a cluster and perform distinct roles. Here, a cluster management scheme is needed to configure and maintain clusters in a way that minimizes cluster management costs and allows clusters to last longer. Cluster management can be divided into the cluster subscription algorithm and the CH candidate algorithm for cluster maintenance.  
Figure 2 shows the overall process of the proposed cluster management technique. The initial state of all vehicles is IN. When a vehicle in the IN state receives a CHA from nearby vehicles, it subscribes to the cluster. Once a vehicle subscribes to a cluster, it becomes a CM. If the vehicle does not subscribe to any cluster within a certain period, it declares itself as a CH and broadcasts a CHA. All vehicles subscribed to a cluster perform V2V communication. Finally, when a vehicle departs from the cluster, it returns to the IN state.  
Figure 1: Proposed safe message dissemination environment.