esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Quantification Model
### Model Formulation  
A model based on uncertainty quantification (UQ) employs a discrete method that involves approximations by analyzing expressions that depend on the joint distribution of the pair \(X\), \(Q\). Here, \(Q\) represents the uncertainties within the system, modeled as a random vector, while \(X\) represents the system's response. We assume that \(X\) is a function dependent of \(Q\), as \(X=X(Q)\). Statistical information can be obtained from a sample of the pair \((X,Q)\). As \(X\) is a function of \(Q\), denoted as \(X=X(Q)\), we can adopt a Hilbertian approach [16; 34]:  
We assume that \(=X(Q)\in H\), where \(H\) is a separable Hilbert space; we choose an adequate Hilbert basis \(\{\varphi_{i}\}_{i\kappa N}*\):  
\[X=\sum_{i=1}^{+\infty}x_{i}\varphi_{i}(Q) \tag{1}\]  
We consider \(X\) and \(Q\) as two random variables on the probability space \((\Psi,P)\). Let \(H\) be a subspace of functions of \(X\). Find an element \(PX\) from \(H\) such that \(PX\) provides the best approximation of \(X\) within \(W\). It is anticipated that \(PX\to X\) for \(n\to+\infty\). The primary objective of a representation is to employ \(PX\) instead of \(X\) when assessing the probability of significant events. This ensures that \(PX\) serves as a practical approximation of \(X\), such that the probabilities obtained with \(PX\) are close to the actual values when compared to those obtained with \(X\). In general, achieving this objective entirely is not always feasible, so we seek an approximation where \(PX\) can be used as a replacement for \(X\) in practical scenarios. This implies that the difference denoted as \(X-PX\), should be small enough, aligning with our specific goals and requirements. If \(\{\varphi_{i}\}_{i\kappa N}*\) is not orthonormal, it is necessary to obtain a sequence of orthogonal projections.  
\[X\approx PX=\sum_{i=1}^{k}x_{i}\varphi_{i}(Q)\in H;\ x=(x_{1},\ x_{2},\ \ldots\ x_{k})^{t} \tag{2}\]  
where \(x\in R^{k}\) denotes the unknown coefficients of the expression of \(PX\) which describes the evolution of the response of the system and \(\{\varphi_{i}\}_{i\kappa N}*\) is a Hilbert basis.
## 3 Results
### Influence of Preheating before Readout on TL Signals and Signal Loss in Time
#### 3.4.2 Effect of Preheating on Signal Loss in Time  
Another important aspect in radiation dosimetry is the stability of signal generated upon irradiation within the detector used. To investigate the signal loss in time, samples of PPG_Tb30 were irradiated using LINAC with dose of 3.65 kGy. After irradiation, samples  
Figure 13: Dependencies of TL signal integral vs. dose absorbed for preheated PPG_Tb30 glass-dosimeter irradiated with beta radiation (4 \(\upmu\)s pulse duration).  
were stored in dark and preheated right before readout. To compare the obtained results in a quantitative way, Equation (12) was used:  
\[\mathrm{SL}=\frac{\mathrm{N}_{\mathrm{int}}-\mathrm{N}_{\mathrm{int}}(\mathrm{t }_{0})}{\mathrm{N}_{\mathrm{int}}(\mathrm{t}_{0})}\cdot\mathrm{100\%} \tag{12}\]  
where SL is the TL signal loss in time, in %, and \(\mathrm{N}_{\mathrm{int}}\) and \(\mathrm{N}_{\mathrm{int}(\mathrm{t}_{0})}\) are the integrated TL signal intensities at a given time and just after irradiation.  
As one can find from data presented in Figure 14, for the non-preheated samples, nearly 40% of the dosimetric signal is lost during first 24 h after irradiation. Finally, at the end of the monitored period of time, the signal loss after ca. 160 h (7 days) is roughly 63%. For the samples preheated at 120 \({}^{\circ}\)C, one observes significant improvement, and the observed signal loss can be estimated for ca. 20-30% during first 160 h. What is interesting, for the samples preheated at highest temperature (200 \({}^{\circ}\)C), the observed signal loss is the slightest and during 340 h (14 days) was found to be of ca. 19%. Moreover, when analyzing the shape of signal loss vs. time dependency, one can find that it exhibits a purely linear character. This observation confirms that for the signal loss only recombination of the lower energy traps are responsible, after the emptying of which the main contributors to the TL signal are relatively stable traps activated at higher temperatures. Both, the dose-dependence of the signal and the decrease of the TL signal intensity over time indicate that the most optimal method of reading samples is their preheating at 200 \({}^{\circ}\)C, which provides signal linearity for high doses and relative stability of the dosimetric signal over time. Additionally, in case of the readout method based on preheating at 200 \({}^{\circ}\)C, one can easily apply the time-related correction of the dose measurement.
## 2 Methodology
### Harvesting Scenarios  
Base cation budgets were compared for three harvesting scenarios: Conventional harvesting (CH), Whole-tree harvesting with brown brash (WTHB) and Whole-tree harvesting with green brash (WTHG), for a single rotation of YC 6-24 Sitka spruce growing on each of the main forest soil types. Sitka spruce was chosen over the other two studied tree species since this is the main commercial tree species in UK forestry [43]. Separate budgets are provided for peaty gley soils on Ca-rich vs. acid geologies.  
Base cation partitioning in different biomass compartments of Sitka spruce is shown in Figure 2. An adjustment was made for the different nutrient contents in the harvesting scenarios of WTHG and WTHB based on biomass of foliage and branches modelled by the BSort model. Reflecting past forest practice, it was assumed that first rotation Sitka spruce crops on shallow and deep peats would have received a single application of rock phosphate (an input of 166 kg Ca ha\({}^{-1}\)), while deep peats would also have received a single application of murine of potash (an input of 100 kg K ha\({}^{-1}\)) [36]. Inputs from previous vegetation (rough pasture) amounting to 71 kg Ca ha\({}^{-1}\), 17 kg Mg ha\({}^{-1}\) and 39 kg K ha\({}^{-1}\) were also included in all scenarios. Atmospheric deposition of base cation input for the harvesting scenarios uses averaged measured base cation deposition from the three Sitka spruce Level II sites, which is representative of UK upland forests. Base cation inputs by weathering and outputs by leaching use averaged data for the six main soil types covered by the 10 ICP forest monitoring network sites. For example, for gleys, averaged data from Alice Holt and Savernake were used, for brown earths--data from Grizedale, for podzols--averaged data from Theftord, Rogate and Sherwood, for peaty gleys on Ca-rich geology--Coalburn data, and for peaty gleys on acid geology--Llyn Brianne data. In addition, an allowance was made for 20% greater base cation losses by leaching due to disturbance caused by clear-felling (for a four-year period) [8; 44; 45].  
Figure 2: Base cation (Ca, Mg and K) stocks in the biomass of the different tree components for Sitka spruce. Biomass is modelled by the BSort biomass partitioning model and cation concentration measured from the ICP Forest Level II monitoring network’s three Sitka spruce sites.
## 2 Materials and Methods  
### Satellite Data  
The research analysis was based on the C61 version of the MODIS product, which is similar in format to the C6 product. However, the C61 version has been improved by satellite calibration and polarization methods, making it more advanced than the previous C6 version. Our analysis relied on LAI from the MOD15A2H dataset provided by NASA EOSDIS LP DAAC ([[https://lpdaac.usgs.gov/products/mod15a2](https://lpdaac.usgs.gov/products/mod15a2) hv061]([https://lpdaac.usgs.gov/products/mod15a2](https://lpdaac.usgs.gov/products/mod15a2) hv061), accessed on 14 February 2023). This dataset provides eight-day maximum synthesis of LAI and FPAR products from 2000 years ago to the present, with a spatial resolution of 500 m [56; 57]. The sources of uncertainty in the MOD15A2H dataset are mainly reflectance data, canopy aggregation effects, and inversion models [58]. However, it has undergone more comprehensive and extensive validation among many LAI products, and its use to study LAI time series over a 22-year period is motivated by considerations of ensuring data consistency and continuity [59; 60]. Land cover datasets come from MCD12Q1 provided by NASA EOSDIS LP DAAC ([[https://lpdaac.usgs.gov/products/mcd12q1v061](https://lpdaac.usgs.gov/products/mcd12q1v061)]([https://lpdaac.usgs.gov/products/mcd12q1v061](https://lpdaac.usgs.gov/products/mcd12q1v061)), accessed on 16 February 2023), and CLCD (Annual China Land Cover Dataset) were provided by the team of professors [PERSON] and [PERSON] of Wuhan University ([[https://zenodo.org/record/5816591](https://zenodo.org/record/5816591)]([https://zenodo.org/record/5816591](https://zenodo.org/record/5816591)), accessed on 21 February 2023). MCD12Q1 provides a global land cover map with a spatial resolution of 500 m for six different land cover legends for each year from 2001 to the present [41]. The annual LAI classification scheme specified in the LC type3 band was used to divide the land cover into 8 types, which is commonly used with MOD15A2H [61; 62]. In this study, only natural ecosystems with an unchanged vegetation type between 2001 and 2022 were preserved (MCD-unchanged) as the object cut by CVFZ. Changes in vegetation type can significantly affect the degree of response of plants to climate change [63]; therefore, preserving the unchanged vegetation type is important. CLCD provides year-to-year land cover data from 1985 to the present, with a spatial resolution of 30 m [64]. CLCD from 2011 (CLCD-2011), MCD12Q1 from 2011 (MCD-2011), and the MCD-unchanged were used in cross-evaluation with CVFZ. All rasters were resampled to 2000 m and reprojected to the same coordinate system to ensure they were of the same size, which was 2884 \(\times\) 3743.
## 2 Materials and Methods
### Simulation of the Temperature Distribution and the Local Overheating  
The modeling and simulation of the temperature distribution inside materials with defects require theoretical developments that will provide information related to temperatures at different points inside the raw samples. The boundary conditions for the MHH process are as follows:  
* Taking into consideration the irregular shape of the holes inside the samples, for the temperature distribution model, it is considered that holes can be approximated as spheres containing air.  
* The holes in the Barium Ferrite W-type sample act as thermal concentration points where the temperature can be higher than the average values of the sample temperatures due to the reflection coefficient of microwaves and local absorption.
* The temperatures change in time and the distribution of absorbed power is uniform with a conversion rate of 60% of the microwave injected power.
* The thermal transfer in ceramic materials can be evaluated using the thermal diffusion equation presented in Equation (2) [21]: \[\rho\cdot c_{p}\frac{dT}{dt}=k\cdot\
abla^{2}\cdot\Delta T+P_{abs/vol}\] (2) where the Barium Ferrite W-type properties are as follows [22; 23]: \(\rho=5000\) kg/m\({}^{3}\) is the density, \(c_{p}=800\) J/kgK is the specific heat, \(k=3\) W/mK is the thermal conductivity, \(\
abla\) is the Laplacian, \(\Delta T\) (\({}^{\circ}\)C) is the difference between the sample temperature and room temperature and \(P_{abs/vol}\) (W/m\({}^{3}\)) is the absorbed power per volume unit. Considering a uniform distribution of the microwave power, the equation can be expressed as Equation (3): \[\rho\cdot c_{p}\cdot\frac{dT}{dt}=P_{abs/vol}-h\cdot\frac{(T_{h}-T_{c})}{V}\] (3) where \(h=20\) W/m\({}^{2}\)K is the convection coefficient (usually 10-50 W/m\({}^{2}\)K for ceramic materials) [24], \(T_{c}=25\)\({}^{\circ}\)C is the room temperature, \(T_{h}\) (\({}^{\circ}\)C) represents the temperature in the material and \(V\) (m\({}^{3}\)) is the total volume of the samples. For the simulation process, the thermal model from SolidWorks 2016 software was used, using the 3D tetrahedral mesh recommended by application.
# Pseudorange Measurement Method Based on AIS Signals
## 5 Optimal Estimation of Timestamps
### Algorithm Performance Comparison  
In [23], according to the bit error rate (BER) in the AWGN channel and the characteristics of GMSK, the authors proposed a threshold of transmission delay estimation based on modified Cramer-Rao Bound. The conclusion is  
\[\sigma_{GMSK,bit\ edge}\geq\sqrt{MCRB_{GMSK}(\tau)}=\frac{0.12}{\sqrt{L_{0}}10^{ \frac{\Delta}{\Delta}}} \tag{23}\]  
where \(s\) is signal power in dBm; \(L_{0}\) is message frame length in bits; \(MCRB_{GMSK}(\tau)\) is the modified Cramer-Rao Bound function, \(\tau\) represents the transmission delay; \(\sigma_{GMSK,bit\ edge}\) is a signal transmission delay measurement with its unit in nanoseconds.  
It should be noted that the literature only gives the theoretical derivation process and conclusions based on the Cramer-Rao Bound theory, but does not give the actual measurement method, and the derivation of the document requires AIS message content as a fixed message, which greatly occupies the bandwidth of the AIS system.  
The length of the AIS message frame at 1280 bits in the five slots mentioned in [23], the estimation result obtained by Equation (23) and the estimation result by the measurement method proposed in this paper were compared. The corresponding conclusion is shown in Figure 13.  
The transmission delay threshold given in [23] was concerned with the signal power, and the measurement method and optimal estimation algorithms presented here focus on the SNR of the baseband signal. Both are descriptions of received signal power. It can be seen in Figure 13, that the performance of the MMSE-EA algorithm (based on the zero-crossing detection) is in agreement with the theoretical threshold characteristic given by Equation (23). The OA-aDF algorithm proposed in this paper can effectively improve the accuracy of pseudorange estimation under low SNR conditions.  
Through the comparison of the MCRB and MMSE-EA curves in Figure 13, it was determined that the noise intensity of the baseband signal corresponding to the MCRB curve was \(-107\) dBm, while the channel noise given in [23] was \(-117\) dBm, with a 10 dB difference between them. This is because [23] focused on radio frequency (RF) signal noise, and this paper is concerned with baseband signal noise, so in the actual measurement circuit, the noise filter performance of the RF circuit and demodulation module will affect final measurement accuracy.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 5 Performance Evaluation  
In this section, we discuss the evaluation strategy of our proposal and the obtained results. For the evaluation, we use a typical search and rescue scenario where some UAVs perform a scanning search in a specific area. For each protocol configuration, we perform 100 runs so as to avoid any bias and evaluate the goodness of the following three metrics:  
* Packet delivery ratio: computed as the ratio between the number of packets actually received and the number of packets sent from a source.
* Overhead ratio: the amount of extra packets needed to deliver the packet to its destination. The metric is computed as: (number of relayed packets \(-\) number of delivered packets)/number of delivered packets.
* Latency: the time taken by all packets to reach the destination.  
These three metrics are studied by varying the number of UAVs, to evaluate the performance trend of the proposal under varying network density. Along with the average values, we compute the confidence interval over the 100 data samples.  
To asses the protocol, we conduct two set of comparisons: The first set considers our protocol compared against the other traditional DTN routing protocols (Epidemic, Spray and Wait, First Contact, Max Prop). In particular, for Spray and Wait, the chosen number of allowed copies is set to 5. The second comparison considers different parameter combinations of the proposed protocol.  
Figure 2: A flow chart illustration of the forwarding process of GeoSaW.
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## 3 Results
### Performance of the Uncalibrated Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**Simulation Settings**} & \multicolumn{3}{c}{**B-K Sub-region**} & \multicolumn{2}{c}{**1 AF Region**} \\ \hline \multirow{2}{*}{**Phenology**} & \multirow{2}{*}{**VI**} & \multirow{2}{*}{**Yield**} & \multirow{2}{*}{**Sowing**} & \multirow{2}{*}{**Full**} & \multirow{2}{*}{**Purestand**} & \multirow{2}{*}{**Division**} & \multirow{2}{*}{**District**} \\  & & & **Estimates** & & & & & \\ \hline Default & GCVI & Yields & Wide & 0.018 & 0.038 & 0.277 ** & 0.426 ** \\ Default & GCVI & Yields & Narrow & 0.009 & 0 & 0.279 ** & 0.424 ** \\ Default & GCVI & Biomass\(\times\)HI & Wide & 0.007 & 0.02 & 0.285 ** & 0.423 ** \\ Default & GCVI & Biomass\(\times\)HI & Narrow & 0 & 0 & 0.281 ** & 0.42 ** \\ Default & MTCI & Yields & Wide & 0.016 & 0.238 ** & 0.335 ** & 0.525 ** \\ Default & MTCI & Yields & Narrow & 0.013 & 0.208 * & 0.349 ** & 0.531 ** \\ Default & MTCI & Biomass\(\times\)HI & Wide & 0.007 & 0.204 * & 0.355 ** & 0.51 ** \\ Default & MTCI & Biomass\(\times\)HI & Narrow & 0 & 0.118 & 0.349 ** & 0.509 ** \\ Updated & GCVI & Yields & Wide & 0.015 & 0 & 0.116 ** & 0.112 ** \\ Updated & GCVI & Yields & Narrow & 0.11 * & 0.25 ** & 0.21 ** & 0.291 ** \\ Updated & GCVI & Biomass\(\times\)HI & Wide & 0 & 0 & 0.284 ** & 0.413 ** \\ Updated & GCVI & Biomass\(\times\)HI & Narrow & 0.172* & 0.169* & 0.289 ** & 0.421 ** \\ Updated & MTCI & Yields & Wide & 0.006 & 0.115 & 0.109 ** & 0.116 ** \\ Updated & MTCI & Yields & Narrow & 0.06 & 0.222 ** & 0.19 ** & 0.255 ** \\ Updated & MTCI & Biomass\(\times\)HI & Wide & 0 & 0.102 & 0.293 ** & 0.404 ** \\ Updated & MTCI & Biomass\(\times\)HI & Narrow & 0.147 * & 0.363 ** & 0.316 ** & 0.44 ** \\ \hline \end{tabular} Note: Significance level: ** for \(p\)-value < 0.001, * for \(p\)-value < 0.01, * for \(p\)-value < 0.05.  
\end{table} 
Table 4: Adjusted coefficient of determination (R2) of uncalibrated regression model that was trained using crop model simulations. HI = harvest index. Figure 6: An example of yield estimates for the 1 AF region in 2016 using the uncalibrated model and Sentinel-2 data. The inset panel is an example of finer scale yield estimates.
###### Abstract  
With the evolution of cellular networks and wireless-local-area-network-based communication technologies, services for smart device users have appeared. With the popularity of 4G and 5G, smart device users can now consume larger bandwidths than before. Consequently, the demand for various services, such as streaming, online games, and video conferences, has increased. For improved quality of experience (QoE), streaming services utilize adaptive bitrate (ABR) algorithms to handle network bandwidth variations. ABR algorithms use network bandwidth history for future network bandwidth prediction, allowing them to perform efficiently when network bandwidth fluctuations are minor. However, in environments with frequent network bandwidth changes, such as wireless networks, the QoE of video streaming often degrades because of inaccurate predictions of future network bandwidth. To address this issue, we utilize the gated recurrent unit, a time series prediction model, to predict the network bandwidth accurately. We then propose a buffer-based ABR streaming technique that selects optimized video-quality settings on the basis of the predicted bandwidth. The proposed algorithm was evaluated on a dataset provided by Zeondo by categorizing instances of user mobility into walking, bus, and train scenarios. The proposed algorithm improved the QoE by approximately 11% compared with the existing buffer-based ABR algorithm in various environments.  
buffer-based ABR; quality of experience; bandwidth prediction; gated recurrent unit; video streaming +
Footnote †: journal: Applied sciences
## 3 Experiments, Results and Discussion  
### Experimental Equipment  
In order to study the influences of different magnetic particle concentrations on the development of magnetic particle indication images, a test bench was designed for the evaluation of magnetic particle indications (Figure 8). The test bench consisted of a magnetization system UniMT-4000, a high-definition camera system, a magnetic particle suspension spray system, a computer and image processing system, a large-area uniform 365 nm ultraviolet lamp, a crack test piece, fluorescent magnetic particles, and related accessories.  
Figure 8: Schematic diagram (**a**) and photo (**b**) of the evaluation test bench for magnetic particle indications.  
Figure 7: Effect of magnetic particle concentration on the number of particles.  
In the performed experiment, the control variable method was employed. Under the same magnetization conditions and considering no impurity doping in the magnetic particle suspension, a batch of magnetic particle suspensions with different concentrations was prepared to obtain crack images. The captured images of magnetic particle indications were preprocessed to extract different evaluation parameters, such as indication length, indication length/crack length ratio, gray value, and contrast [19; 23].  
A UniMT-4000 fluorescent magnetic particle inspection system was used to perform fluorescent MPI on the cracked test piece. This equipment used a programmable logic controller (PLC) to control the sequence of magnetic particle suspension spraying, magnetization, and image acquisition, to ensure consistent inspection conditions. The cracked test piece was made according to the high-sensitivity test piece specified in the GB/T 23907-2009 \"Nondestructive Testing-Shims for magnetic particle testing\" standard. The test piece material was annealed ferromagnetic pure iron with a side length of 20 mm and a thickness of 100 \(\upmu\)m. The size of the crack was 6 mm (length) \(\times\) 30 \(\upmu\)m (depth) \(\times\) 60 \(\upmu\)m (width). According to the requirements of the ISO 9934.1-2016 \"Nondestructive Testing Magnetic Particle Testing Part 1: General Principles\" standard, the magnetic field strength on the workpiece surface was set to \(>\)2000 A/m. The magnetizing current of the UniMT-4000 fluorescent MPI equipment was measured by a Tesla meter placed on the surface of the cracked test piece, so that the magnetic field strength at the crack was measured to ensure that it meets the magnetization requirements of magnetic particle inspection.