esa-sceva/satcom-chunk-collection huggingface dataset:
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems
### Polyanhydrides  
Polyanhydride biopolymers were introduced and developed with the motivation of achieving more stable degradation rates compared with other biopolymers. The anhydride bond linkages on the surface are very water sensitive but the polymer matrix is hydrophobic, preventing water penetration into the bulk, and so the predominant effect is surface erosion with a stable rate achieved [27]. In brief, the structure of the poly(anhydride) is comprised of two main components, an aliphatic segment, normally based on sebacic acid (SA) and an aromatic part, typically based on p-(carboxyphenyloxy)-propane (CPP) and p-(carboxyphenyloxy) hexane (CPH). The aliphatic poly(anhydride) degrades within days, whereas the aromatic poly(anhydride) degrades within years [28], and hence the degradation rate can be adjusted by varying the ratio of polymer units in the material.  
The degradation product of polyanhydrides is a non-toxic diacid that can be readily metabolized and/or eliminated from the body, and therefore polyanhydrides demonstrate excellent biocompatibility [29]. This led to development of a polyanhydride controlled release product containing the chemotherapeutic agent, carmustine (Gliadel(r)), which was approved by the FDA for the treatment of brain cancer in 1996 [30]. Additionally, a polyanhydride implant loaded with gentamicin sulphate (Septacin(r)) [31] was developed for sustained local delivery for the treatment of osteomyelitisitia [14].  
Apart from the classic CPP-SA or CPH-SA polyanhydrides, alternatives of CPP and/or CPH anhydrides with poly fatty acid dimers have been developed. As the fatty acid dimers deposit onto the surface of poly matrices in parallel with the degradation process, they can impede the diffusion of small molecule drugs and achieve a longer release period [32]. Furthermore, modification of the anhydride group by linking with imines and/or esters is potentially beneficial [33].
## 3 Results
### Relationships between Spring EVI and C Fluxes  
Spring EVI was demonstrated to have a relatively consistent positive impact on C flux for all sites, although the level differed among the sites (Figure 6). High R\({}^{2}\) values of 0.66 (\(p=0.008\)) and 0.61 (\(p=0.013\)) were observed for spring EVI and GPP, and NEP for the SK-OA site. However, no significant correlation (R\({}^{2}=0.26\), \(p=0.168\)) was found between spring EVI and GPP for the SK-OBS site. For SK-OJP, spring EVI was consistently correlated with annual GPP and NEP, with R\({}^{2}\) values of 0.40 (\(p=0.048\)) and 0.51 (\(p=0.020\)), respectively. At the deciduous site, SK-OA, spring EVI is a direct measure of canopy greenness and thus showed close relationships with both GPP and NEP. In contrast,  
Figure 5: Relationships between spring temperature and (**a**) GPP of SK-OA, (**b**) NEP of SK-OA, (**c**) GPP of SK-OBS, (**d**) NEP of SK-OBS, (**e**) GPP of SK-OJP, and (**f**) NEP of SK-OJP. NS indicates no significant correlation. The solid and dashed lines represent regression and its 95% confidence of mean prediction.  
SK-OBS showed limited variation in canopy greenness, and thus GPP showed low sensitivity to spring EVI changes. Interestingly, the NEP of the two evergreen sites showed high correlation with spring EVI. Although the GPP of SK-OBS was independent of spring EVI, our results suggest that spring variation in canopy greenness may have potential as an indicator of the annual NEP of both evergreen and deciduous sites in boreal regions. This would be of great significance for the remote evaluation of C sequestration among plant functional types, given the convenient acquisition of global EVI data from satellite observations.  
Figure 6: Relationships among spring enhanced vegetation index (EVI) and (**a**) GPP of SK-OA, (**b**) NEP of SK-OA, (**c**) GPP of SK-OBS, (**d**) NEP of SK-OBS, (**e**) GPP of SK-OJP, and (**f**) NEP of SK-OJP. NS indicates no significant correlation. The solid and dashed lines represent regression and its 95% confidence of mean prediction.
## 2 Materials and Methods
### Model Validation  
We chose Hong Kong's Kowloon Peninsula as the study area to validate the estimated FVC based on the corrected reflectance using Equation (9). The effectiveness of the correction was evaluated by introducing the \"true FVC\" and comparing the RMSE (Equation (7)) and BIAS (Equation (8)) of the \"original FVC\" and \"modified FVC\" (Section 3.4). The three key FVCs were calculated as follows:  
1. True FVC: the L3C class image product with a spatial resolution of 0.78 m from Jilin-1 ([[https://www.jil1](https://www.jil1) mall.com/]([https://www.jil1](https://www.jil1) mall.com/), accessed on 15 October 2024) on 10 May 2020 was selected for analysis. The FVC values were calculated and aggregated to 30 m.
2. Original FVC: the Level2 Tier1 class image product with a spatial resolution of 30 m from Landsat 8 ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)), accessed on 15 October 2024) on 17 May 2020 was used as the source data for calculating the original FVC values.
3. Modified FVC: The SVF was calculated using DSM datasets ([[https://portal.csdi.gov.hk/geoportal/?lang=en&datasetId=cedd_rcd_162926720523_87895](https://portal.csdi.gov.hk/geoportal/?lang=en&datasetId=cedd_rcd_162926720523_87895)]([https://portal.csdi.gov.hk/geoportal/?lang=en&datasetId=cedd_rcd_162926720523_87895](https://portal.csdi.gov.hk/geoportal/?lang=en&datasetId=cedd_rcd_162926720523_87895)), accessed on 15 October 2024) with a spatial resolution of 0.5 m in 2020 and resampled to 30 m. The reflectance in the red and near-infrared bands of the Landsat 8 image was corrected using Equation (9), after which the modified FVC values could be calculated.
## 6 Performance Analysis and Implementation Results
### Implementation Results of Scalar Multiplication
Compared to the previous fastest implementation, namely FourQ[24], which require 6,561,500, 4,280,400, and 469,500 cycles on AVR, MSP430, and ARM, respectively, our implementation requires 4.49% more cycles on AVR, but saves 2.85% and 4.61% cycles on MSP430X and ARM processors, respectively. Compared to \(\mu\)Kummer [47], which requires 9,513,536 cycles on AVR, our implementation saves 27.93% cycles. It also saves 50.68% and 47.58% cycles than [PERSON] et al. 's Curve25519 implementation [45], which requires 13,900,397 and 7,933,296 cycles on AVR and MSP430, respectively. It saves 69.92% cycles compared to the NaCl library [48], which requires 22,791,579 cycles on AVR. It saves 54.50% cycles than [PERSON] et al. 's Curve25519 implementation [44], which requires 9,139,739 cycles on MSP430. Additionally, it saves 68.54% cycles compared to the method in [46], which requires 1,423,667 cycles on the ARM Cortex-M4 processor. The memory of embedded processors is very constrained, meaning the memory usage of various implementations is important. In the case of the 8-bit AVR, \(\mu\)Kummer [47] requires the lowest memory usage in the recently proposed results, which requires 9490 bytes of code size and 99 bytes of stack memories. [PERSON] et al. 's and [PERSON] et al. 's implementations [43; 45] require the lowest code size and stack memories on MSP430, which require 8378 bytes of code size and 384 bytes of stack memories. In the 32-bit ARM, Ref. [46] require 3750 bytes of code size and 740 bytes of stack memories. FourQ[24] reported the memory usage of ECDH and signature operations, but did not report the memory usage of single scalar multiplication. Our implementations for curve Ted127-glv4 requires 13,891, 9098, and 7532 bytes of code size and 2539, 2568, and 2792 bytes of stack memories on AVR, MSP430, and ARM Cortex-M4, respectively. FourQ and curve Ted127-glv4, which utilize the four-dimensional decompositions, precompute eight points, meaning they require more stack memory than other implementations. However, the performance of four-dimensional scalar multiplication is significantly faster than other implementations.
# Performance Evaluation of the Current Birmingham PEPT Cameras
## 5 Results
### SuperPEPT  
Figure 6 shows the data rates measured from SuperPEPT using a central \({}^{11}\)C source of varying activity. The kink at around 13 MBq is due to the bandwidth of the data acquisition, which can record a maximum data rate of 3.1 MHz (sum of prompt and delayed coincidences). (In fact, the true limit is actually lower: the system can buffer approximately 1.5 \(\times\) 10\({}^{9}\) events at 3.1 MHz, but thereafter can only record at a little over 2 MHz).  
At low activity, the prompt coincidence rate is consistent with a sensitivity of 22% for a central source. Combining this with the total angular acceptance of 0.67 implies an average detector efficiency of approximately 57%. This figure represents an average over the mix of HR+ and EXACT31 blocks. Discarding events involving either of the end sections, the sensitivity measured using just the central section is approximately 16% and combining this with the angular acceptance of 0.5 for this central section again implies an effective detector efficiency of 57% for this central section. The actual efficiency of the HR+ blocks operated with a LLD value of 150 keV is somewhat higher than this. So as to achieve the internal diameter of 40 cm the HR+ are mounted with gaps of approximately 4 mm between them, and no allowance was made for these gaps in calculating the angular acceptance of this cylindrical section.  
As the activity increases, the effective sensitivity drops due to increasing dead time. Note that the important rate is that of true coincidences--fortuitously, when considering the total prompt coincidence rate the effect of dead-time is almost balanced by the increasing contribution of random coincidences. Although its presence is clear, dead time is actually quite modest--at an activity of 10 MBq the true coincidence rate (approximately 1.87 MHz)  
Figure 5: Coincidence data rate recorded from the ADAC Forte for a central \({}^{11}\)C source of varying activity. The dotted line corresponds to a sensitivity of 1.4%.  
is only 15% lower than that predicted on the basis of 22% sensitivity. Moreover, at this activity, random coincidences contribute only 11% of the total prompt coincidence rate.  
Using the Birmingham PEPT algorithm to process the point source data measured at low activity, optimum precision of location was found with a value \(f\approx 0.6\), and corresponds to an effective value of \(w\approx 6\) mm. Almost identical results were found even at activities up to 12 MBq (data rates well over 2 MHz).  
Using the formulae derived in Section 2, these values imply that a 10 MBq tracer can be located with an uncertainty of around 0.3 mm every 0.3 ms if moving at 1 m/s, and with an uncertainty of around 0.65 mm every 6.5 ms if moving at 10 m/s.
## 4 Calculation of the Structure Relaxation Time at the Glass Transition Temperature  
The temperature at which the glass transition of the liquid occurs depends on the melt cooling rate \(q\), although this dependence is weak. The standard glass transition temperature \(T_{g}\) is considered to be the temperature at which the limiting viscosity \(\eta_{g}\approx 10^{12}\) Pa\(\cdot\)s is reached. It corresponds to the cooling rate  
\[q=3\ {\rm K}/{\rm min}=0.05\ {\rm K}/{\rm s}, \tag{16}\]  
which is considered a standard cooling rate [3; 10; 32; 33]. In the dilatometry of glasses and polymers, approximately the same cooling rate is almost universally used (16). In view of the weak (logarithmic) dependence of \(T_{g}\) on \(q\), small fluctuations of \(q\) around the standard value (16) do not particularly affect the value of \(T_{g}\). Therefore, it is generally assumed that most available data on \(T_{g}\) actually refer to the standard cooling rate. There are little data for other rates \(q\).  
According to Formula (11), at a standard cooling rate \(q=0.05\ {\rm K}/{\rm s}\), according to the data on the WLF equation parameters \(C_{1}\) and \(C_{2}\), the structural relaxation time \(\tau_{g}\) was calculated at the glass transition temperature (Table 1) As-S: 135-277 s; Se-Ge: 71-325 s; As-Se: 71-267 s; polyvinyl acetate: 61 s; natural rubber: 57 s; polymethyl methacrylate: 22 s; polyurethane: 18 s; polyvinyl chloroacetate: 20 s; polyisobutylene: 54 s.  
For comparison, we can present the results of calculating the relaxation time of the structure for oxide systems using the well-known Maxwell formula: \(\tau=\eta/G\), where \(G\) is the shear modulus. For most oxide inorganic glasses, the instantaneous shear modulus \(G_{\infty}\) is about \(G_{\infty}=(20\)-\(25)\cdot 10^{9}\) Pa, which changes little with temperature. For a large set of oxide inorganic glasses at a standard cooling rate \(q=0.05\ {\rm K}/{\rm s}\), the logarithm  
rate, the approximate constancy of viscosity at the glass transition temperature can be represented as \(\eta_{g}\approx 10^{12}\)-\(10^{13}\) Pa-s. From these data on the quantities \(G_{\infty}\) and \(\eta_{g}\), at the level of qualitative estimates, we obtain [10; 31; 32; 34; 35]:  
\[\tau_{g}=\frac{\eta_{g}}{G_{\infty}}\approx 10^{2}\;\mathrm{s}, \tag{17}\]  
which coincides in order of magnitude with the results of calculation by Formula (11), according to the data on the parameters of the WLF equation \(C_{1}\) and \(C_{2}\).
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 3 Methods
### Data Collection  
The data from the floating buoy were collected by an installation on the buoy and were transmitted to a computer and stored in a database for further analysis, as shown in Figure 3. The data consist of  
Figure 1: Schematic diagram of nearshore WEC configuration [13].  
Figure 2: Working principle of the nearshore WEC [13] (**a**) when the buoy rises and (**b**) when the buoy descends.  
three accelerations and three angular velocities along the \(x,y\) and \(z\) axes, respectively. Thus, the dataset is composed of six time series data.  
Wireless data transmission is accomplished with the transmitter and receiver module. The transmitter is composed of an MPU6050 tri-axial gyro-accelerometer sensor, an NRF2401 2.4-GHz transceiver and an Arduino microcontroller. The MPU6050 senses the accelerations and the angular velocities, and these data are transmitted by the NRF2401 transceiver shown in the left of Figure 4. Both are controlled by an Arduino microcontroller. The receiver module has an NRF2401 transceiver moderated by an Arduino. The gyro-accelerometer sensor, MPU6050, provides six time series data that represent the orientation of the floating buoy at a specific time. This dataset includes linear acceleration and angular velocity, as previously noted. The NRF2401 transceiver enables the data to be transmitted from the water side to the computer side. The transceiver can cover more than one hundred meters, which makes it suitable for nearshore applications [14]. Figure 4 shows the data collection module.
## 4 Experiments and Discussion
### Data Set  
We comprehensively evaluated the MFDGCN model on the real road network data set PeMS_BAY [8], which is shown in Figure 7. This data set contains the data of 325 sensors in the highway network collected by CalTrans Performance Measurement System (PeMS). We selected data at 30 s intervals for 6 months from 1 January 2017 to 31 May 2017. Before the experiment, we aggregated 30 s of data into a time step of 5 min as the traffic flow input feature \(X\) of the model and generated an adjacency matrix \(A_{s}\) based on the distance and traffic similarity of each sensor. For the traffic flow data, we used 70% of the data for training, 20% for testing, and 10% for validation. In our work, the number of time steps in an hour is set to 12, and on this basis, we sample features x and labels y by a sliding window with a step size of 1.
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
###### Abstract  
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.  
unscented Kalman filter (UKF); unscented transformation (UT); maximum correntropy criterion (MCC) +
Footnote †: journal: Sensors
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 3 Mathematical Model
### Extension of the Objective Function for Fairness
#### 3.2.2 Bottleneck Flow Fair Allocation (BFF)  
The Bottleneck Flow Fair (BFF) allocation method assigns the same capacity to all flows on a shared bottleneck link, irrespective of the fact if more the flow occupies the link with more than one subflows, which was explained in Definition 7. In other words, BFF ensures that there is no advantage for multipath flows over single path flows competing for the same link. An example for a network where BFF is applied was already given in Figure 3.  
As it was shown for BSF, there are two ways to specify the objective function for BFF, namely maximizing the aggregated allocation or supporting a high amount of flows using multipath transport. This results in the objective functions BFF-I (Equation (10)) and BFF-II (Equation (11)) which are identical to the ones specified for BSF:  
_BFF-I_  
\[\text{maximize}:\quad Alloc, \tag{10}\]
## 4 Dynamic Characteristics Analysis
### Steady Convergent Process  
As shown in Figure 20, after entering the steady convergent process, the end states of all the curves from different initial states are located on a straight line in the three-dimensional space expanded by \(y-\psi-\Delta\omega\), and this straight line is in the plane \(\psi=0\). This finding validates Equation (47) deduced in Section 2.5. The straight line in Figure 20 can be calculated through the linear fitting method, which is \(\Delta\omega=13.49y+1.33\times 10^{-9}\). According to Equation (47), the theoretical function of the straight line is \(\Delta\omega=12.5y\), and the relative error is 7.9%. Due to some nonlinear issues of the wheel-rail contact, the equivalent wheel-rail tread conicity is not always equal to the wheel geometry tread conicity 0.1 but varies with the lateral displacement. Thus, the simulation results are a little different from the theoretical functions.  
Figure 21 shows the whole process with a simulation duration of 500 s from only one initial state. The first stage of the entire motion is the dynamic process. In this stage, the IRW moves from the initial state to another on the straight line in plane \(\psi=0\). Once the state converges to this straight line, the motion will enter the second stage, the steady convergent process. In this stage, the IRW converges to \((y=0,\psi=0,\Delta\omega=0)\) along the straight line following the law depicted by Equation (47).  
Figure 19: \(y-\psi\) phase diagram (\(v=40\) m/s) with different yaw suspension configurations.  
Under the interactions among the gravitational restoring forces, gyroscopic moment, and yaw suspension, the dynamics of the IRW are complicated, making the IRW technology hard to apply to urban railway vehicles. Three types of dynamic motions exist: derailment, hunting, and offset running. Only at high speed and with a yaw damper can the IRW run along the center of the rail track. Under other conditions, the dynamic motions of the IRW are unwanted in practical applications. In addition, with proper yaw suspension coefficients, IRW's state can converge to zero eventually, but the steady convergent process takes a long time, usually hundreds of seconds, which is unacceptable. Thus, self-guidance control is still essential for the IRW, especially at low speeds.  
Although the dynamic characteristics and the steady convergent process are verified by simulation results, due to considerations of inevitable irregularities input from the rail track and time-varying nonlinear frictions generated by bearings, the experimental verification illustrated in the next section is strongly necessary.
## 3 Theory
### Summary  
By knowing \(T_{g}\), \(T_{ng}\), \(\Delta N_{g}\), and \(\Delta N_{ng}\), the coefficient \(C\) in the basic formulas in Section 3.1 can be calculated. It is given by  
\[C=\frac{T_{g}\Delta N_{g}}{T_{ng}\Delta N_{ng}} \tag{40}\]  
It is possible to calculate the distance \(r\) between the sound source and the receiver for the actual positions, but in our calculations a representative distance was used given by  
\[r=\frac{1}{2}\sqrt{L_{y}^{2}+L_{z}^{2}} \tag{41}\]  
where \(L_{y}\) and \(L_{z}\) are the width and length of the rectangular room, respectively.  
Thus, all parameters are given and can be inserted into Equations (16)-(18) for further calculation of \(C_{50}\), and \(G\). \(T_{20}\) is calculated using the logarithmic version of Equation (8).  
It should also be mentioned that, as the number of grazing and non-grazing modes are mainly related to the floor area and the volume of the room, it is of interest to investigate the model's applicability for other room shapes than rectangular, as long as the ceiling absorber is parallel to the floor.