esa-sceva/satcom-chunk-collection huggingface dataset:
# Behavior of Astrocytes Derived from Human Neural Stem Cells Flown onto Space and Their Progenies
## 3 Results  
### Space Flown NSCs Give Rise to Astrocytes  
After 39.3 days in space and three weeks post-flight in NSCs culture medium, we cultured NSCs and their progenies derived from those flown to space in DF-10% FCS to direct their specification to the astrocyte phenotype (Figure 2).  
Figure 1: (**A**) A total of 0.7 \(\times\) 10\({}^{6}\) neural stem cells (NSCs) were seeded onto the mesh carrier (proprietary composition). (**B**) Example of NSCs growing on the mesh carriers, they grow across the mesh fibers. (**C**) Passive units containing 4 mesh carriers (2 mm \(\times\) 2 mm) with the cells, were flown onto space onboard SpaceX-16. This experiment was designed to mimic the trajectory astronauts’ brains undergo during space flight (i.e., launch, stay in space and splash down when returning to Earth). NSCs were seeded on mesh-carrier to which cells adhere strongly, this feature was necessary to ensure that cells would not detach and die during launching or while returning to earth. Pre-launch and while in the ISS, cells were maintained at 37 \({}^{\circ}\)C and they were at this temperature with slow cool down during splash-down. Upon splash-down they were transferred to the Long Beach airport and brought to the laboratory at 37 \({}^{\circ}\)C. Upon arrival cells were seeded onto flasks or flaskettes and allowed to recover during six hours in the incubator prior to setting them in the imaging system.  
In time cells grew healthy and slowly started to acquire the astroglial typical morphology. They were becoming flat and with numerous cell processes (Figure 3). We waited 26 days because we monitored the composition of our cultures at earlier time points by immunocytochemistry for NSCs and astrocytes and cells were still transitioning given rise to subpopulations at earlier time points, where nestin and vimentin positive cells were still present in these cultures. Therefore, we had to allow enough time for astroglial specification in order to work with a homogeneous population of astrocytes. For comparison of Naive or SPC-NSCs derived astrocytes see Supplemental Figure S1.  
Figure 3: Induction of NSCs to the astroglial phenotype. NSCs were cultured in 10% FCS supplemented culture medium to instruct them towards the astrocyte phenotype (**A**,**C**). Five days after being in DF-10% medium, most cells were round and developed thin processes. At higher magnification (**C**), it was easier to see cells that had started to adopt the typical flat morphology. (**B**) and (**D**) are another example where most cells did not yet display the astrocyte morphology. View of cells at higher magnification that were becoming flat and connected with neighboring cells via their cell processes (**D**). Bars = 50 μm.  
Figure 2: NSCs-derived astrocytes timeline. SPC-flowon NSCs were harvested and cultured on poly-d-lysine for 21 days. On post-flight day 22, NSCs were switched to a culture medium containing 10% fetal calf serum (FCS).
## 2 Materials and Methods
### Characterization Techniques  
Nanoparticle size measurement was conducted using a Malvern Zetasizer Nano-ZS ZEN 3600 device (Software: v7.13, Worcestershire, UK) in accordance with the reference [39]. The study was performed for all three syntheses, both before and after ITZ coating. The measurement was repeated 20 times.  
Fourier transform infrared (FTIR) spectroscopy analyses were conducted utilizing a Nicole Nexus 2002 FTIR spectrometer across a spectral range of 4000 to 400 cm\({}^{-1}\), achieving a resolution of 2 cm\({}^{-1}\). The samples comprising ITZ and ITZ with AuNPs were fabricated in tablet form, appropriately consisting of ITZ or ITZ with AuNPs, and potassium bromide (KBr).  
An X-ray diffraction study was performed using a D2 Phaser X-ray diffractometer (Bruker) on samples that were micronized in an agate mortar immediately prior to application to an aluminum plate. The tested sample was exposed to X-rays generated by a ceramic-sheathed Cu lamp powered by a 30 kV, 10 mA generator. The test was performed in the range from 5 to 36\({}^{\circ}\) 2\(\Theta\) with measurement every 0.02\({}^{\circ}\) 2\(\Theta\) for 1 s.  
A scanning electron microscope (SEM) with energy dispersive X-ray spectrometry (EDS) experiment of the samples was carried out on a Hitachi model TM 3000 electron microscope (Hitachi High-Technologies Corporation, Tokyo, Japan). The samples were fixed on an aluminum sample stub and coated with silver by conventional sputtering techniques. The employed accelerating voltage was 5-15 kV for SEM.  
The Brookfield DV3T rheometer was employed to ascertain the apparent viscosity of the obtained gels. The study was conducted at room temperature, with two studies performed for each gel using two spindles (LV05 and LV04). Each study consisted of five measurements, comprising two stages. The stages lasted 60 s and differed in rotation speed. In the first stage, the spindle rotated at 10 rpm/min, and in the second at 50 rpm/min.
# Average Slope vs. Cmax: Which Truly Reflects the Drug-Absorption Rate?
## 3 Results
### Comparative Performance between AS and Cmax
#### 3.2.2 Random Forest  
For the purposes of further investigating the correlations between the PK parameters described above using the PCA algorithm, the RF approach was used to identify the contribution of each PK parameter to Tmax. As RF is a supervised algorithm requiring the response variable in the ordinal scale before it is applied in the model, Tmax values were transformed into this scale. In this scope, Tmax was divided into quartiles, resulting in four groups. These quartiles corresponded to low, moderate, high, and very high values of Tmax.  
In Figure 3, the results from the RF analysis of the PK variables of isdexamfetamine revealed that the primary contributor to Tmax was AS with a variable importance score of 49.19%, followed by Cmax, AUC, and, lastly, AUCInf. Notably, Cmax, which is commonly utilized to characterize the absorption rate, exhibited a lower contribution to Tmax with a variable importance score of 23.32%, less than half the importance score of AS. An evaluation of the confusion matrix results indicated a prediction accuracy of 79.70% for the RF model.  
A similar analysis was performed for the other four drugs of the study (Figure 4). Notably, in Figure 4, AS contributed more to Tmax in comparison to the other PK parameters, regardless of the kinetic profile of each drug, with variable importance scores varying from 53.41% (methylphenidate) to 43.93% (nintedanib). AS was followed by either Cmax or AUCinf, suggesting that there is no distinct superior contributor to Tmax when comparing traditional PK parameters commonly used in BE studies. An evaluation of the confusion matrix results indicated a prediction accuracy of 75.15% for the RF model of nintedanib, 91.21% for methylphenidate, 97.27% for nitrofurantoin, and 89.13% for theophylline.  
Figure 3: A random forest classifier model when Tmax (in ordinal scale) was used as the response variable for isdexamfetamine. The left panel shows the variable importance scores, while the right panel displays the confusion matrix.
## 4 Results and Discussion
### PCA Processing for IR Images  
From the analysis of IR images above, it is obvious that ECPT can be used to detect the presence of the surface cracks on PDC bits in a successive scanning mode. However, due to the effects of a low SNR (signal-to-noise ratio) and non-uniform heating, it is difficult to make a precise characterization of the crack geometry. For example, the shape of each segment of Crack I is hard to identify in Figure 5a-f. As discussed in Section 2.2, PCA is effective to eliminate effects of noise and non-uniform heating in the raw IR images to enhance the features of the cracks. Hence, PCA is introduced as an image processing method to improve the detection result of ECPT for the cracks on tungsten carbide matrix of PDC bit. Here, we take the condition of the coil located at Position D as an example to illustrate the effectiveness of image processing with PCA.  
There are several suggestions for selection of the IR image frames as the input of PCA method. [PERSON] et al. found that the IR image sequences before the time of when the thermal responses of the defects and the intact areas appear in inversion pattern are suitable for PCA analysis [10]. According to this finding, IR image sequences from frame 10 to 30 (400-1200 ms) were selected as the inputs for PCA processing because the cracks show higher thermal response than those of surrounding sound areas in these IR image frames. Generally, the first four principal components obtained by PCA carry most of information regarding the original data, which can be used for image reconstruction and defect feature enhancement [21].  
Figure 7: IR images recorded when the coil is placed at (**a**) Position A, (**b**) Position B, (**c**) Position C, (**d**) Position D, and (**e**) Position E. Positions A–E is corresponding to Figure 4b.  
The reconstructed images by the first two principal components are shown in Figure 8, which shows the enhanced features of the cracks. Compared to the original IR image in Figure 7d, the background noise has been greatly eliminated in Figure 8a, but the location and geometry of the segments of Crack I and Crack II are not greatly enhanced. The main reason may come from the remaining effect of non-uniform heating along the annular coil. However, from Figure 8b, we can see that Crack I and Crack II are well separated from the surrounding areas because the effect of non-uniform heating is almost eliminated. Hence, it is easy to characterize the important features of the segments of the two cracks from Figure 8b. Besides, with the help of the PCA method, the crack information can be automatically obtained from an IR image sequence to avoid the trouble of manually identifying defect information from a large number of IR image frames. Therefore, the PCA method is proven as one of the effective methods to improve the detectability of ECPT for surface cracks on matrix of PDC bit by greatly enhancing the features of cracks from the image reconstructed with the second principal component.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 3 Results and Discussion
### Evaluation Analysis of Selected Wavelength with Diffuser Using Square LED Arrangement  
In this experiment, the selected wavelength is 850 nm, and the square LED arrangement was used as it has a better area of illumination based on the findings in Section 1. After the image processing for both methods was performed as shown in Figure 13, the  
Figure 12: Number of vein counts vs LEDs arrangement for arm area.  
shape of the vein in both arm and palm appears as a Type II human vein arrangement [34], where the NIR light was filtered with diffuser paper HA85M16W and CA85M16W. Overall, the addition of a diffuser was found not to be one of the crucial parameters for enhancing the vein visualization system.  
Our findings showed that the number of vein counts for both ROIs was similar with or without the diffuser. The only drawback of the absence of a diffuser is the lack of uniformity of the light source [33]. This can be easily fixed by adding layers of diffusers in front of the LEDs to achieve more constant illumination. The diffuser scatters the light from the LEDs, diminishing the radiation intensity. This study proved that a diffuser paper could attenuate the IR source so that the radiation can be distributed more uniformly [27] even though there is no significant difference in the results.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### Control Method of Visual Guidance Based on the Aerial Manipulator  
The complete process for implementing the visual-guided aerial manipulator's control method is below (Figure 9).  
Figure 8: Visual guidance algorithm process.  
Step 1: The quad-rotor UAV takes off and uses LiDAR for SLAM positioning and map construction. It performs path searching and planning for the target object while the aerial manipulator remains in the initial folded posture.  
Step 2: The UAV searches for the target object within the working range of the manipulator using a depth camera. It utilizes OpenCV to recognize and detect the two-dimensional coordinate information of the target object. The UAV performs a small-scale iterative search if the target object is undetected. If the target object is detected, the UAV lowers its height and utilizes the No. 2 TFmini for real-time detection of the vertical distance from the depth camera to the target object. Based on the coordinate transformation, the three-dimensional position information of the target object is obtained.  
Step 3: The target object's positional information is continuously updated in real time until it reaches the operational grasping range. The manipulator's pose is adjusted, with the arm hanging down to enter the pre-grasping pose. Considering the relative position of the UAV to the target object, as well as the shape and size of the object to be grasped, the grasping range of the manipulator is set. The aerial manipulator enters the grasping range, and the trajectory planning and control system outputs the desired motion angles for each servo joint involved in the grasping process to the manipulator's servo controller. Finally, the aerial manipulator completes the grasping of the target object.  
Step 4: After the UAV completes the grasping process, it continues to climb. During its ascent, the real-time height information from the No. 2 TFmini (\(d2\)) is collected. If \(d2\) fluctuates within a range of 3 cm above or below a reference value (\(d1\)), the target object is within the grasp range of the mechanical claw, and the grasping is successful. In such a case, the UAV flies towards the designated target delivery point. However, if \(d2\) is significantly larger than \(d1\), the grasping has failed, and the UAV returns to searching for the target object.  
Step 5: The UAV proceeds toward the designated target delivery point for searching. If the search is successful, the UAV descends to the predetermined placement height to deliver the target object. If the search is unsuccessful, the UAV continues with a small-scale iterative search. By maintaining a constant UAV altitude and implementing a delayed hovering approach, a certain buffer time is provided to the manipulator control system to make angular adjustments, ensuring synchronization between the manipulator control system and the flight control system.  
Step 6: After the aerial manipulator successfully delivers the target object, the manipulator returns to its initial state and proceeds to the landing point.
## 2 Materials and Methods
### Variation in pH, TA, DIC, and Sea Surface pCO\({}_{2}\) within 24 Hours  
In the near-shore region, the surface daily averages (standard deviations in brackets) of pH increased from 7.92 (\(\pm\)0.02) to 7.95 (\(\pm\)0.02) at the bottom (Figure 4a), TA increased from 1936.06 (\(\pm\)40.21) to 1993.43 (\(\pm\)15.01) \(\upmu\)mol kg\({}^{-1}\) at the bottom (Figure 4d), DIC increased from 1889.75 (\(\pm\)28.04) to 1919.82 (\(\pm\)9.35) \(\upmu\)mol kg\({}^{-1}\) at the bottom (Figure 4g), and \(p\)CO\({}_{2}\) decreased from 996 (\(\pm\)71) to 868 (\(\pm\)53) \(\upmu\)atm at the bottom (Figure 4j).  
In the front region, the surface daily averages (standard deviations in brackets) of pH decreased from 8.33 (\(\pm\)0.11) to 7.94 (\(\pm\)0.03) at the bottom (Figure 4b). TA increased from 2157.80 (\(\pm\)34.17) to 2244.12 (\(\pm\)0.68) \(\upmu\)mol kg\({}^{-1}\) at 30 m and then decreased to 2244.06 (\(\pm\)0.50) \(\upmu\)mol kg\({}^{-1}\) at the bottom (Figure 4e). DIC increased from 1833.97 (\(\pm\)68.63) to 2102.68 (\(\pm\)12.88) \(\upmu\)mol kg\({}^{-1}\) at the bottom (Figure 4h), and \(p\)CO\({}_{2}\) increased from 283 (\(\pm\)87) to 735 (\(\pm\)59) \(\upmu\)atm at the bottom (Figure 4k).  
In the offshore region, the surface daily averages (standard deviations in brackets) of pH decreased from 8.38 (\(\pm\)0.03) to 8.05 (\(\pm\)0.02) at the bottom (Figure 4c), TA increased from 2229.48 (\(\pm\)4.72) to 2240.15 (\(\pm\)0.53) \(\upmu\)mol kg\({}^{-1}\) at the bottom (Figure 4f), and DIC increased from 1791.73 (\(\pm\)24.87) to 2018.65 (\(\pm\)13.21) \(\upmu\)mol kg\({}^{-1}\) at the bottom (Figure 4i). Daily average \(p\)CO\({}_{2}\) was 227 (\(\pm\)23) \(\upmu\)atm at the surface, and it decreased to 226 (\(\pm\)32) \(\upmu\)atm at 5 m and then increased to 566 (\(\pm\)38) \(\upmu\)atm at the bottom (Figure 4l).  
Overall, from the vertical distribution of the water column, pH was generally highest at the surface and lowest at the bottom. On the contrary, TA, DIC, and \(p\)CO\({}_{2}\) were generally lowest at the surface and highest at the bottom. Spatially, pH and TA generally increased from the near-shore to the offshore region. On the contrary, DIC and \(p\)CO\({}_{2}\) generally decreased from the near-shore to the offshore region.  
Figure 3: Twenty-four-hour variations in temperature in the near-shore (**a**), front (**b**), and offshore (**c**) regions and salinity in the near-shore (**d**), front (**e**), and offshore (**f**) regions in summer.  
Figure 4: Twenty-four hour variations in pH in the near-shore (**a**), front (**b**), and offshore (**c**) regions; TA in the near-shore (**d**), front (**e**), and offshore (**f**) regions; DIC in the near-shore (**g**), front (**h**), and offshore (**i**) regions; and sea surface \(p\)CO\({}_{2}\) in the near-shore (**j**), front (**k**), and offshore (**l**) regions in summer.
## 2 Problem Statement
### Secure Communication  
Let \((x_{i},y_{i})\) be the position of the ground node \(i\), and \(i=0,\ldots,N\). When \(i=0\), the node refers to the intended ground node, while when \(i=1,...N\), the node is an eavesdropper. The intended node is a legitimate node, and it makes sense for the UAV to know its location. For the locations of eavesdroppers, following [33], we assume that they can be measured by an optical camera or a synthetic aperture radar. Here, \(N\) is the number of eavesdroppers. Let \(d_{i}(t)\) denote the Euclidean distance between the UAV and node \(i\) at time \(t\):  
\[d_{i}(t)=\sqrt{(x(t)-x_{i})^{2}+(y(t)-y_{i})^{2}+z(t)^{2}}.\]  
Denote \(\kappa(d)\) as a function measuring the communication performance. In the free space, \(\kappa(d)\) is a decreasing function of \(d\). In the well-known Friis' formula [34], when the transmitter and the receiver are \(d\) apart from each other with LoS, the received signal-to-noise ratio (SNR) is \(\kappa(d)=\frac{d}{d^{2}}\), where \(a\) is a given positive constant. Let the binary variable \(l_{i}(t)\) indicate whether the UAV has LoS with node \(i\) at \(t\): \(l_{i}(t)=1\), if they have LoS; \(l_{i}(t)=0\), otherwise. Let \(\kappa(d_{i}(t),l_{i}(t))\) denote the SNR between the UAV and node \(i\). \(\kappa(d_{i}(t),l_{i}(t))\) is defined as follows:  
\[\kappa(d_{i}(t),l_{i}(t))=\frac{al_{i}(t)}{d_{i}^{2}(t)}. \tag{11}\]  
To secure the wireless communication between the UAV and the intended node, a set of constraints are considered below. The first requirement is that the received SNR at the intended node is no smaller than a threshold \(\gamma_{c}\):  
\[\kappa(d_{0}(t),l_{0}(t))\geq\gamma_{c}. \tag{12}\]  
This is to ensure that the node can successfully recover the received data at time \(t\). Secondly, the received SNR at each eavesdropper must be no larger than a threshold \(\gamma_{e}\) (\(\gamma_{e}<\gamma_{c}\)):  
\[\kappa(d_{i}(t),l_{i}(t))\leq\gamma_{e}, \tag{13}\]  
for all \(i=1,\ldots,N\). This is to ensure that any eavesdropper \(i\) cannot recover the captured data.  
In general, the constraint (12) requires the UAV to be close enough to the intended node and have LoS with the node, whereas the constraint (13) requires the UAV to be far enough from the eavesdroppers or have no LoS with the eavesdropper. We note that, in cases where several eavesdroppers are located closely, they may block the path for the UAV, and there may not exist a feasible path satisfying (12) and (13). To avoid being eavesdropped, the UAV stops sending data in these cases. We also note that the buildings can reflect wireless signals. This can make the received SNR larger than the value computed by (11). Since the Non-LoS propagation model is complex and out of the scope of this paper, we do not go far in this direction. It is worth pointing out that, if such a model is available, our method can be extended straightforwardly by updating Equation (11).
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 3 Hardware Design of RFID Reader/Router
### Hardware Design of Wireless RFID Reader/Router
#### 3.2.2 Circuit Design of ZigBee Sensor Nodes  
ZigBee sensor node primarily controls the data exchange between RF module and electronic tags. The system uses CC2530 low-power RF chip which is made by TI/Chipcon. It integrates the IEEE 802.15.4/ ZigBee RF transceiver and industrial standard enhanced 8051 Micro Controller Unit (MCU) kernel, the core of the control circuit is shown in Figure 4b.  
Data read operation: The mainframe sends the highest bit 1 and pulls IC_CS low at the same time. The 8-bit address of the read data is encoded in the prescribed format, and the data is sent to the MOSI line from high to low after 8 cycles. After receiving a data of reading instruction address, the corresponding data will be sent back to the mainframe by MF RC522. The mainframe will read out the data on MISO after eight cycles and store on a temporary variable, pull IC_CS up at the same time. That indicates the completing of reading operation of one byte.  
Data write operation: The mainframe sends the highest bit 0, and pulls IC_CS low at the same time. According to the way of sending address in read data operation, the target address is sent out after eight cycles. Then the written data is sent out through MOSI after another eight cycles. That indicates the completing of the corresponding address update in MF RC522.  
The system uses 32 MHz crystal oscillator clock signal as the core controller, and Serial Peripheral Interface (SPI) way of communication is used between the CC2530 and RF module. P1_7 connects SDA end of RF chip MF RC522 that is used as a signal of the control line. P0_1, P1_3 and P0_4 connect respectively the RF chip MF RC522 D5, D6 and D7, registers associated with MF RC522 are controlled to read and write by SPI mode. P0_5 connects reset pin of MF RC522 for resetting the controller. Figure 5 shows I/O interface connection of CC2530 and MF RC522.