esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 3 Hierarchical Task Assignment and Path Finding
### Problem Description
Communication is simulated by means of a realistic model for WiFi networks [38; 39; 40], whereby the received power (in dBm) from robot \(r_{j}\) when robot \(r_{i}\) is transmitting can be computed as:  
\[P_{r}(i,j)=P_{0}-10\eta\log(d(i,j))-nW(i,j)\cdot W+\mathcal{N}(0,\sigma), \tag{1}\]  
where \(P_{0}\) is the signal strength at 1 m, \(\eta\) is the path loss, and \(d(i,j)\) computes the distance in meters between the two robots. Moreover, \(nW(i,j)\) computes the number of walls in the direct line between the two robots, \(W\) is the wall attenuation, and \(\sigma\) the noise variance. Data is exchanged in packets (or frames), and the frame error rate is defined as:  
\[\text{FER}(P_{r}(i,j))=\text{FER}_{S}\cdot e^{\gamma(S-(P_{r}(i,j)-N_{b})-N_{b })}, \tag{2}\]  
where \(S\) is the receiver sensitivity, \(\text{FER}_{S}\) and \(\gamma\) are hardware specific constants, \(N_{b}\) is the background noise, and \(N_{th}\) the thermal noise. In this work, we exploit the receiver sensitivity \(S\) as a parameter to determine local and realistic communication abilities. The above communication model is relevant also for discovering tasks. Indeed, we consider tasks as being issued by mobile devices equipped with the same WiFi communication system as the robots (e.g., tablets or smartphones). Hence, robots might have limited perception of the available tasks and the existence of a task is not known by all the robots at the same time. We assume, unless otherwise stated, that robots do not share task-related information.
## 3 Results and Discussion
### Rheology of the Binder  
Frequency Sweep Testevident in Figure 6 that modification of the binder with GNPs has led to an increase in the complex modulus. The increment in the complex modulus was more pronounced when 4% of GNPs were added in comparison to 2%. Figure 7 shows a decrement in the phase angle, which is more prominent at a low frequency/high temperature. A maximum decrease in the phase angle was observed for the higher weight content of the GNPs. The value of the Superpave rutting factor also increased massively at a low frequency upon modification of the binder. This showed the same trend as the Complex Shear Modulus.  
The reason behind the increase in the complex modulus can be attributed to the high elasticity and the large surface area of the nanoparticles, which gives them a higher affinity to bond with the functional groups of the binder [21]. A cover is created, which helps in preventing the viscous nature of bitumen and causes a delay in the conversion of the elastic behavior to the viscosity at a high temperature [22]. The downward shift of the phase angle can also be attributed to the elasticity of the GNPs. The elastic behavior of the asphalt binder contributes to the rutting resistance. Increasing the GNP content intensifies the increase in the complex modulus and the decrease in the phase angle.  
Figure 6: The Master Curve for the Complex Modulus at 50 \({}^{\circ}\)C. GNPs = Graphene Nano-Platelets.  
Figure 7: The Master Curve for the Phase Angle at 50 \({}^{\circ}\)C. GNPs = Graphene Nano-Platelets.  
The increase in the Superpave rutting factor can be explained by the fact that the extremely small size of the GNPs leads to an increase in Van der Waals interaction energy between the GNPs and the asphalt binder [23]. This results in a more stable asphalt binder. The enhancement in the Superpave rutting factor due to the addition of the GNPs validates the improvement in the deformation resistance at a high temperature.
## 3 Results and Discussion
### Recommendations for Future Work  
The targeting of mitochondria and the subsequent induction of mitochondria-mediated apoptosis is an emerging pharmacological target. The mitochondrion is considered to be the 'powerhouse' of the cell; it generates cellular energy, is required for oxidative phosphorylation, adenosine triphosphate (ATP) production, ROS production, and cell cycle control. Consequently, the dysfunction of mitochondria disrupts these crucial energetic processes and causes cell apoptosis, autophagy, and necrosis [3,66]. Mitochondria-targeting nanocarriers may eliminate the need for photothermal therapy in addition to chemotherapy in the future which is already limited due to the hypoxic environment of cancer cells and its need for oxygen.  
As discussed previously, of the publications analysed, very few clearly investigated the subcellular localisation of the drug-loaded nanocarrier after cellular uptake; althoughmitochondria-targeting is an emerging area of interest in cancer therapy, only one publication highlighted its relevance and introduced a mitochondria-targeting ligand. Further, the localisation of graphene and its derivatives within the cell is still unknown. Future research should identify the subcellular localisation of graphene nanocarriers and aim to incorporate mitochondria-targeting ligands in order to induce cancerous cell apoptosis more efficiently, safely, and with less toxic effects to normal cells [3].  
It would also be beneficial for future work to have standardisation for the minimal information needed to be recorded to eliminate incomplete data reporting [83]. In addition, standardisation of synthesis routes, raw materials and chemicals used in the synthesis, and the concentrations of both DOX and graphene used, should be considered. Future research may also benefit from the identification of whether the drug-loaded graphene nanocarriers adhere to standardised parameters, such as those defined by [PERSON] in 2001. [PERSON] defined five parameters for drug-like compounds that describe ideal physiochemical properties. These guidelines state that poor absorption or permeation are more likely to occur when the molecular mass is over 500 Da, there are more than five hydrogen-bond donors, more than ten hydrogen-bond acceptors, and the water-octanol partition coefficient (logP) is over five. Biological substrates are defined as exceptions to the rule [3,101].  
Another major limitation to this research is that the long-term biodistribution and clearance pathways of the nanocarriers have not been clearly highlighted throughout the analysed publications. It is important to understand the clearance pathway of the nanocarrier system as well as to evaluate its long-term biocompatibility and toxicity. Most of the publications analysed only basic in vitro experiments; more complicated in vivo studies have not yet been reached. To understand the clearance pathways of the nanocarrier from a living system, in vivo or human studies must take place.
## 2 Materials and Methods
### Satellite-Based SIF Dataset  
_SIF_ retrievals during 2007-2018 were utilized in this study. The _SIF_ data came from an 8-day spatially downscaled gridded GOME-2 _SIF_ dataset with resolution of 0.05\({}^{\circ}\)[47]. It is based on robust and cloud-insensitive GOME-2 _SIF_ retrieval at 740 nm wavelength proposed by [PERSON] et al. (2015) and shows high agreement with the observations of TROPOMI after bias correction. As the original retrieved _SIF_ represents the instantaneous value at its observation time, daily correction was performed to convert the instantaneous _SIF_ to the daily average _SIF_. The _SIF_ values in [PERSON]'s dataset were converted to daily average via the method proposed by [PERSON] et al. [23], and are therefore comparable with daily gross primary production (GPP). In addition, rigorous quality control was performed on this dataset, and the impacts of bidirectional reflectance and weather conditions were minimized. However, it should be noted that since day correction was performed usingthe cosine of the solar zenith angle (SZA), variations in weather during the day are not taken into account in this dataset. Therefore, the so-called daily correction is actually a correction for the difference in solar zenith angle and day length caused by latitudes, and all of the _SIF_ data used in this research can be seen as clear sky observations. In this study, instantaneous _SIF_ at 09:30 was obtained by conducting the inverse operation of previous steps for the calculation of instantaneous \(SIF_{yield}\).  
Although GOME-2 provides global satellite-based _SIF_ data covering a long period of time, the radiometric degradation of the instrument is problematic and may lead to inconsistency in interannual analysis [48; 49]. In order to address that, a pseudo-invariant method was employed to correct for this degradation in a dataset shared in ZENODO repository [50].
## 4 WiFi 6/6E: IEEE 802.11 ax
### Multiuser MIMO (MU-MIMO)  
MIMO is typically used to increase the data rate between an AP and a user device by adding multiple spatial streams. However, most of the devices found in WLANs have only one or two spatial streams, making MIMO not very effective. On the other hand, MU-MIMO allows for the transmission of multiple spatial streams between AP and multiple users. MU-MIMO was first introduced in WiFi 5 to enable simultaneous downlink transmissions. However, WiFi 6 features both uplink and downlink MU-MIMO for up to eight users. As such, MU-MIMO capability is quite useful in serving upcoming 6G applications where an AP has to serve multiple high-data-rate application simultaneously.
## 2 Experimental Study on Adhesion Properties of Raw Coal  
### Sample Raw Coal and Testing Equipment  
(1) Raw coal parameters  
The coal samples selected for this experiment were obtained from the raw coal bunker of the Taoyuan Coal Mine II 1011 mining roadway in the Huaibei mining area of China. An industrial composition analysis of the coal samples was conducted, with the results presented in Table 1. It was determined that the moisture content (\(w\)) of the raw coal is approximately 3%. The raw coal was screened to determine its particle size distribution, and the resulting distribution curve is shown in Figure 1. The curve indicates that more than 97% of the raw coal particles have a size range of 0-15 mm. Therefore, particles within this size range were selected for the experimental study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Item** & **M\({}_{\text{ad}}\) (\%)** & **A\({}_{\text{ad}}\) (\%)** & **V\({}_{\text{df}}\) (\%)** & **S\({}_{\text{ld}}\) (\%)** & **FC\({}_{\text{ad}}\) (\%)** \\ \hline Raw coal & 2.94 & 24.18 & 38.73 & 0.23 & 33.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Industrial composition analysis of raw coal.  
Figure 1: Particle size distribution of raw coal.  
2. Testing equipment Based on the Jenike shear test principles [29; 30], a hydraulic servo-controlled testing machine was employed, for which a specific raw coal shear test mold was designed. This setup formed the raw coal shear testing system used in this experiment. The testing machine consists of a bidirectional independent hydraulic loading servo control system and a data acquisition system. The servo-loading control system includes two independent loading and unloading subsystems that are perpendicular to each other, both utilizing servo-controlled rigid loading methods. The data acquisition system is equipped with stress and displacement sensors, with measurement accuracies of 0.01 kN and 0.002 mm, respectively. The raw coal shear testing device comprises upper and lower L-shaped compression plates, a shear box, and a normal load application plate. Each L-shaped plate has a 90\({}^{\circ}\) wedge that ensures the shear plane is fixed at the midpoint of the shear box. The upper L-shaped plate has a reserved hole through which the normal load application plate can pass to exert a normal compaction force on the raw coal inside the shear box. To investigate different shear interfaces (coal-coal interface, coal-wall interface), we filled the lower shear box with either raw coal or cast C30 concrete (identical to the material of the actual coal bunker walls), respectively. By shearing these different interfaces, we can obtain the adhesion parameters between raw coal particles and between raw coal particles and the bunker wall, as depicted in Figure 2. The raw coal particles were placed in the shear box, and a normal stress was applied with the normal stress subsystem of the testing machine. The L-shaped compression plate, driven by the tangential subsystem, was used to perform horizontal shearing of the raw coal.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 5 Experimental Results
### Simulation Setup  
The performance of routing protocols somehow depends on the underlying MAC protocol. Similar to the majority of studies in this field, we use the Carrier Sense Multiple Access (CSMA) MAC protocol without using its RTS/CTS (Request to Send/Clear to Send) and ACK mechanism. In this way, a forwarding node can broadcast a packet if the channel is free; otherwise, it will back off. The packet will be discarded if the forwarding node backs off five times. For suppressing the redundant transmissions, nodes can simply listen to the channel and drop the packet if it is relayed by other candidates.  
In our simulations, we consider the channel model described in Section 3.2 to simulate a lossy underwater environment. The transmission power is set to 95 dB \(\,\mathrm{re}\,\mu\,\mathrm{Pa}\), and the transmission range for all nodes is considered as 100 m. The data generation rate is set to one packet per second, which can effectively prevent the interference of two continuous packets. The channel bit rate is 10 kbps, and the propagation speed of the acoustic signal in the underwater environment is 1500 m/s. The size of packets varies by changing the number of forwarding candidates, but its average value is less than 150 B. The coefficients of EPA and energy (\(\mu\),\(\rho\)) in Equation (11) are considered equal to balance energy and routing efficiency. We set \(T_{Delay}\) in Equation (12) as one second based on our model. In the beaconing procedure, \(T_{update}\) is set to 30 seconds, and \(T_{invalid}\) is considered as 75 seconds (considering the random jitters to prevent synchronization).  
The relay nodes (ranging from 400 to 1200) are randomly deployed in a 500 m \(\times\) 500 m \(\times\) 1000 m 3D field. Relay nodes can move horizontally at the speed of 2 m/s by following a random walk 2D mobility model (moving in the X-Y plane), which is mostly used by the underwater routing protocols (e.g., VBF, HHVBF, AHH-VBF, DBR) [22; 26; 27; 28]. Furthermore, we consider a single sink at location (100; 100; 0) to collect the information and a source node at location (400; 400; 1000) to generate the packets to be transferred to the sink node. The sink and source are intentionally placed at opposite corners of the field to have better assessment of the routing protocols. We consider the maximum pipeline radius for VBF and HHVBF as 100 m (equal to the transmission range of nodes) in which they have the highest performance in packet delivery. All of the results are averaged over 20 runs for randomly-generated topologies with the 95% confidence interval. The simulation time for each run is set to 1000 s. An example of a network topology is presented in Figure 10.  
Figure 10: Underwater 3D environment.
## 2 Specimens and Experiment Setup
### Finite Element Analysis  
We employed the commercial finite element software package ABAQUS v6.13.2 (Hibbit, Karlsson, and Sorensen, Inc., Pawtucket, RI, USA) to analyze the problem of solid-fluid coupling. A 20-node three-dimensional (3D) solid piezoelectric element (C3D20 RE) was used to simulate piezoceramic materials, whereas a 20-node 3D solid element (C3D20R) was used for the simulation of brass. Each layer included 448 elements and 3359 nodes, and a surface-based tie constraint was used for the solid-fluid interface. The frequency range of the resonant analysis was 1 to 6000 Hz. It was assumed that the direction of bimorph polarization was parallel, as indicated in Figure 1. The upper and lower electrode surfaces of the two piezoceramic plates and the middle brass layer were assumed to be conductive regions.  
Table 2 lists the material properties of the fluids used for acoustic coupling analysis in this study, including air, water, and glycerine. The acoustic coupling element (AC3D20) was used for fluid simulation. Figure 5 presents the full model used in the analysis of structure-fluid interactions. It was assumed that the material was elastic, compressible, non-viscous, and capable of bearing hydrostatic stress, which was proportional to the volume strain, as follows:  
\[\sigma=K_{f}\epsilon_{V} \tag{2}\]  
where \(K_{f}\) is the bulk modulus, \(\epsilon_{V}=\epsilon_{11}+\epsilon_{22}+\epsilon_{33}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Constants** & **Air** & **Water** & **Glycerine** \\ \hline Bulk Modulus & 1.42 \(\times\)10\({}^{6}\) N/m\({}^{2}\) & 2.2 \(\times\)10\({}^{9}\) N/m\({}^{2}\) & 4.39 \(\times\)10\({}^{9}\) N/m\({}^{2}\) \\ Density & 1.2 Kg/m\({}^{3}\) & 1000 Kg/m\({}^{3}\) & 1260 Kg/m\({}^{3}\) \\ Wave Velocity & 344 m/s & 1483 m/s & 1875 m/s \\ Viscosity & 0.00001 Pa\(\cdot\)s & 0.001 Pa\(\cdot\)s & 1.2 Pa\(\cdot\)s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of fluids.  
The equilibrium equation is as follows:  
\[\frac{\partial p}{\partial x}+\gamma\dot{u}^{f}+\rho_{f}\dot{u}^{f}=0 \tag{3}\]  
where \(p\) is the pressure above the static pressure, \(x\) is the spatial position of a fluid particle, \(\dot{u}^{f}\) is the velocity of the fluid particle, \(\dot{u}^{f}\) is the acceleration of the fluid particle, \(\rho_{f}\) is the density of the fluid, and \(\gamma\) is the volumetric drag. In this acoustic analysis, we assumed a steady-state configuration, which means that only the bulk modulus and density have an impact on computation; i.e., volumetric drag is disregarded.
## 2 Materials and Methods
### Experimental Design  
_S. vicifolia_ seeds, selected for uniform size and fullness, were cleansed with distilled water, sterilized using 75% alcohol for 30 s, and subsequently rinsed 3-5 times with distilled water. Excess surface moisture was removed with double-layer filter paper. Using sterile tweezers, the sterilized seeds were placed in 9 cm diameter Petri dishes at a density of 50 seeds per dish with four replicates per treatment, moistened with 8 mL of the respective treatment solution. The experiment incorporated five ABA concentration treatments: 0 (CK,with distilled water), 10, 50, 100, 200 mg\(\cdot\)L\({}^{-1}\). Additionally, the experiment incorporated two levels of PEG-6000-induced drought conditions: mild drought (5% PEG) and severe drought (20% PEG). These employed a randomized block design (Table 1).  
The Petri dishes containing the seeds were situated in a constant temperature light incubator set at a light intensity of 3000 lx, a photoperiod of 12 h, and a temperature of 25 \({}^{\circ}\)C. Seed germination was monitored daily throughout the experiment, with ABA solution of the respective concentrations applied as needed to maintain moisture in the filter paper. A seed was considered to have germinated when the emerging radicle elongated to 2 mm from the seed coat [11]. From the onset of germination, the count of germinated seeds was recorded daily across a 30-day germination cycle. The germination rate and potential were calculated once there was no increase in the number of germinated seeds for three consecutive days.
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
## 3 Results and Discussion
### Interior Orientation Determination Accuracy Evaluation  
To verify the intra-image accuracy of the OHS basic product, the artificial puncture method was used to obtain control points from three scene images of 2019-01-13-OHS-Neimeng, 2019-01-17-OHS-Henan, and 2019-01-17-OHS-Hebei. The control points are 33, 33, and 31, as shown in Figure 13.  
Figure 12: Overlay display schematic map of different spectrum segments in Gansu Province.  
The positioning accuracies of all the images were evaluated based on the geometric control points (GCPs), where an image affine model based on the RPC defined by Equation (5) is used as the exterior orientation model [17; 18; 19].  
\[\begin{split} x+a_{0}+a_{1}x+a_{2}y&=RPC_{x}(lat, lon,h)\\ y+b_{0}+b_{1}x+b_{2}y&=RPC_{y}(lat,lon,h)\end{split} \tag{5}\]  
The image-based affine model based on RPC can eliminate the error of the attitude and orbit system. The accuracy after adjusting the control point mainly depends on the random error of the attitude and orbit. Because the accuracy of orbit determination is high, the accuracy shown in Table 4 mainly depends on the attitude random error, including errors caused by attitude measurement random error and platform stability. According to Table 1, the random error of the attitude measurement is 15\" (\(3\sigma\)) and the geometric positioning error is 1.2 pixels (\(1\sigma\)). In fact, because the imaging time of the OHS basic product standard scene is only 7 s, which is very short, and the attitude measurement error in the time is mainly systematic, the random error may be less than 15\" (\(3\sigma\)) and the resulting positioning error should be less than 1.2 pixels. Considering the fact that the attitude downlink frequency is only 1 Hz, under the condition that the attitude angular velocity is constant within 1 s, the attitude of the arbitrary imaging time is interpolated based on the measurement attitude of the adjacent 1 s during ground processing. However, since the stability of the OHS platform is only 0.002\({}^{\circ}\)/s (\(1\sigma\)), the angular velocity of the attitude within 1 s is not constant; hence, the random error caused by the stability of the platform does not exceed 0.002\({}^{\circ}\), and the geometric positioning error does not exceed 1.7 pixels (\(1\sigma\)). In summary, the effect of the attitude random error on the internal accuracy should theoretically not exceed 2 pixels. Table 4 shows that the orientation accuracy of the three basic products is better than 1.5 pixels, and Figure 14 shows the positioning residuals are random, which is consistent with the theoretical concept of \"not exceeding 2 pixels\".