esa-sceva/satcom-chunk-collection huggingface dataset:
# Education and Training in Applied Remote Sensing in Africa: The ARCSSTE-E Experience
###### Abstract  
In Africa, there is growing knowledge regarding the use of data obtained by remote sensing and analysed while using Geographic Information Systems for solving myriad problems. The awareness has largely arisen through the efforts of the Programme on Space Applications (PSA) of the United Nations Office for Outer Space Affairs (UNOOSA), and the subsequent UN resolutions for the establishment of Regional Centres for Space Science and Technology Education, to train scientists and researchers in different thematic areas of space, including Remote Sensing/Geographic Information Systems (RS/GIS). The African Regional Centre for Space Science and Technology Education in English (ARCSSTE-E) is one of these regional centres. The Centre has successfully trained 474 professionals from 18 countries since its inception in 1998; about 14% of these trainees have been female. This paper highlights the training programmes of ARCSSTE-E from its inception, and discusses the potential areas of improvement with a focus on the RS/GIS area. In 2019, a survey was conducted on alumni of the Postgraduate Diploma (PGD) programme of ARCSSTE-E. Based on the analysis of their responses and the progression of the PGD programme to a new Masters programme in RS/GIS at the university, there is clear evidence regarding the impact of the UNOOSA-assisted capacity building programme on the work and career of alumni, which has already produced an appreciable number of trained personnel in developing countries in Africa.  
remote sensing; Geographic Information Systems; capacity building; education; training 1
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Goal Oriented Error Estimates
#### 4.1.1 Time-Integration Error Estimate  
Estimating the time-integration error in Equation (18) would require error estimates for all evaluation points of the quadrature formula. Instead, we consider the following quadrature independent estimate:  
\[e_{n}^{\mathcal{J}_{h}} :=\Delta t_{n}\Big{|}j(t_{n+1},\mathbf{u}_{n+1})-j(t_{n+1},\mathbf{u}(t_{n +1}))\Big{|}\] \[\leq\Delta t_{n}\underbrace{\Big{|}j\left(t_{n+1},\mathcal{M}^{t_ {n},\Delta t_{n}}\mathbf{u}_{n}\right)-j\left(t_{n+1},\mathcal{M}^{t_{n},\Delta t _{n}}\mathbf{u}(t_{n})\right)\Big{|}}_{\text{global error propagation}}+ \tag{19}\] \[\Delta t_{n}\underbrace{\Big{|}j\left(t_{n+1},\mathcal{N}^{t_{n},\Delta t_{n}}\mathbf{u}_{n}\right)-j\left(t_{n+1},\mathcal{M}^{t_{n},\Delta t_{ n}}\mathbf{u}_{n}\right)\Big{|}}_{\text{global error increment}}. \tag{20}\]  
Again, the global error propagation dynamics are unknown, but we can estimate and control the global error increment. Using the same principle technique as in Section 3.2, we estimate the global error increment in Equation (20) by  
\[\hat{t}_{n}^{\mathcal{J}_{h}}:=j\left(t_{n+1},\mathcal{N}^{t_{n},\Delta t_{n }}_{-}\mathbf{u}_{n}\right)-j\left(t_{n+1},\mathcal{N}^{t_{n},\Delta t_{n}}_{+} \mathbf{u}_{n}\right). \tag{21}\]
# Mathematical Description of Changes of Dried Apple Characteristics during Their Rehydration
###### Abstract  
The mathematical description of changes of dried apples characteristics (mass gain, volume increase, dry matter loss, rehydration indices, and colour) during their rehydration was performed. The effect of conditions of both processes on model parameters were also considered[PERSON] Apple slices (3 and 10 mm) and cubes (10 mm) were dried in natural convection (drying air velocity 0.01 m/s), forced convection (0.5 and 2 m/s), and fluidisation (6 m/s)[PERSON] Drying air temperatures (\(T_{d}\)) were equal to 50, 60, and 70 \({}^{\circ}\)C. The rehydration process was carried out in distilled water at the temperatures (\(T_{r}\)) of 20, 45, 70, and 95 \({}^{\circ}\)C. Mass gain, volume increase, and dry matter loss were modelled using the following empirical models: [PERSON], [PERSON], Singh and Kulshrestha, [PERSON] ([PERSON]), [PERSON], [PERSON], and modified Page. Colour changes were described through applying the first-order model. Artificial neural networks (feedforward multilayer perceptron) were applied to make the rehydration indices and colour variations (\(\Delta E\)) dependent on characteristic dimension, \(T_{d}\), drying air velocity, and \(T_{r}\). The Page and the modified Page models can be considered to be the most appropriate in order to characterise the mass gain (RMSE = 0.0143-0.0619) and the volume increase (RMSE = 0.0142-0.1130), whereas the [PERSON], Pilosof-Boquet-Batholomai, and Singh and Kulshrestha models were found to be the most appropriate to characterise dry matter loss (RMSE = 0.0116-0.0454). The ANNs described rehydration indices and \(\Delta E\) satisfactorily (RMSE = 0.0567-0.0802). Both considered process conditions influenced (although in different degree) the changes of the considered dried apple characteristics during their rehydration.  
rehydration; apple; quality; mathematical model; ANN +
Footnote †: journal: Physics Letters A
## 5 Test Systems
### 69-Node Test Feeder  
This test system comprises 69 nodes and 68 branches with an operating voltage of 12.66 kV. The slack node is located at node 1, and its configuration is depicted in Figure 6. This feeder has a total active and reactive power demand of 3890.7 kW and 2693.6 kVAr, respectively. The initial active power losses of this system are equal to 225.072 kW. Here, we assumed that the voltage and power base values were 12.66 kV and 1000 kW, respectively.  
The information of all the branches as well as the load consumption of the 69-node test feeder are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Node \(i\)** & **Node \(j\)** & \(R_{ij}\) [\(\Omega\)] & \(X_{ij}\) [\(\Omega\)] & \(P_{j}\) [kW] & \(Q_{j}\) [kW] & **Node \(i\)** & **Node \(j\)** & \(R_{ij}\) [\(\Omega\)] & \(X_{ij}\) [\(\Omega\)] & \(P_{j}\) [kW] & \(Q_{j}\) [kW] \\ \hline
1 & 2 & 0.0922 & 0.0477 & 100 & 60 & 17 & 18 & 0.7320 & 0.5740 & 90 & 40 \\
2 & 3 & 0.4930 & 0.2511 & 90 & 40 & 2 & 19 & 0.1640 & 0.1565 & 90 & 40 \\
3 & 4 & 0.3660 & 0.1864 & 120 & 80 & 19 & 20 & 1.5042 & 1.3554 & 90 & 40 \\
4 & 5 & 0.3811 & 0.1941 & 60 & 30 & 20 & 21 & 0.4095 & 0.4784 & 90 & 40 \\
5 & 6 & 0.8190 & 0.7070 & 60 & 20 & 21 & 22 & 0.7089 & 0.9373 & 90 & 40 \\
6 & 7 & 0.1872 & 0.6188 & 200 & 100 & 3 & 23 & 0.4512 & 0.3083 & 90 & 50 \\
7 & 8 & 1.7114 & 1.2351 & 200 & 100 & 23 & 24 & 0.8980 & 0.7091 & 420 & 200 \\
8 & 9 & 1.0300 & 0.7400 & 60 & 20 & 24 & 25 & 0.8960 & 0.7011 & 420 & 200 \\
9 & 10 & 1.0400 & 0.7400 & 60 & 20 & 6 & 26 & 0.2030 & 0.1034 & 60 & 25 \\
10 & 11 & 0.1966 & 0.0650 & 45 & 30 & 26 & 27 & 0.2842 & 0.1447 & 60 & 25 \\
11 & 12 & 0.3744 & 0.1238 & 60 & 35 & 27 & 28 & 1.0590 & 0.9337 & 60 & 20 \\
12 & 13 & 1.4680 & 1.1550 & 60 & 35 & 28 & 29 & 0.8042 & 0.7006 & 120 & 70 \\
13 & 14 & 0.5416 & 0.7129 & 120 & 80 & 29 & 30 & 0.5075 & 0.2585 & 200 & 600 \\
14 & 15 & 0.5910 & 0.5260 & 60 & 10 & 30 & 31 & 0.9744 & 0.9630 & 150 & 70 \\
15 & 16 & 0.7463 & 0.5450 & 60 & 20 & 31 & 32 & 0.3105 & 0.3619 & 210 & 100 \\
16 & 17 & 1.2890 & 1.7210 & 60 & 20 & 32 & 33 & 0.3410 & 0.5302 & 60 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electrical parameters of the 33-node test feeder.  
Figure 6: Electrical configuration of the 69-node test system.
## 4 Simulation Study on Foam Acoustic Parameters
### Effect of Foam Static Flow Resistivity on Acoustic Radiation Performance  
The static flow resistivity of porous materials is a parameter that characterizes the magnitude of fluid flow resistance within porous materials. It may be determined by  
Figure 10: Comparison of experimental and simulated sound power level curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Static Flow Resistivity** & **Porosity** & **Viscous Characteristic** & **Thermal Characteristic** \\ (**Pa**\(\cdot\)**s/m\({}^{2}\))** & **Factor** & **Length (mm)** & **Length (mm)** \\ \hline
87,000 & 0.97 & 2.52 & 0.037 & 0.119 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Acoustic characterization parameters of polyurethane foam.  
dividing the pressure difference across the porous material by the volumetric velocity of fluid passing through the porous material, as demonstrated in Equation (1).  
\[R=\frac{\triangle p}{vd}, \tag{1}\]  
here \(\triangle p\) represents the pressure difference between the two sides of the porous medium, \(v\) denotes the air average flow velocity per unit area of the material, and \(d\) stands for the thickness of the sample.  
Keeping other parameters constant, the static flow resistivity of the foam is set to its original value, 1.2 times the original value and 0.8 times the original value, which are 87,000 Pa\(\cdot\)s/m\({}^{2}\), 104,400 Pa\(\cdot\)s/m\({}^{2}\), and 69,600 Pa\(\cdot\)s/m\({}^{2}\), respectively. These values are inserted into the calculations to obtain the radiated sound power at different static flow resistances, as shown in Figure 11.  
From Figure 11, it can be seen that as the static flow resistance of the foam increases, the radiated sound power gradually decreases. According to Table 4, at 854 Hz, the peak sound power levels corresponding to increasing static flow resistivity are 41.88 dB, 40.43 dB, and 39.27 dB, respectively. At 1467 Hz, they are 18.85 dB, 17.75 dB, and 16.82 dB and at 1588 Hz they are 43.61 dB, 42.41 dB, and 41.32 dB, respectively. This is because as the static flow resistance increases, so does the acoustic impedance during sound propagation.  
Calculations show that as the static flow resistance of the foam increases, the effectiveness of the unit static flow resistance in suppressing radiated sound power gradually decreases. The reductions are 1.44 dB to 1.17 dB, 1.07 dB to 0.96 dB, and 1.19 dB to 1.10 dB, respectively, showing a non-linear relationship. Comparing the numerical values at different frequencies, it can be seen that the suppression effect of unit static flow resistance is significantly better at peak frequencies than at non-peak frequencies, and the suppression effect is more pronounced at larger peak frequencies than at smaller peak frequencies.  
Figure 11: Comparative graph of radiated sound power level curves of foam-filled panels at various static flow resistivity, porosities, and tortuosity factors.
# Improvement of Mesh Atomizer Performance by Electrolytic Polishing
## 1 Introduction
However, the influence of the dynamic conical angle effect and the machining performance of the micro-tapered hole, as the core component of an atomizer, on the atomization effect has rarely been mentioned by researchers, let alone deeply studied. In this study, the influence of laser processing technology on the morphology of micro-tapered holes and on atomization was studied. At present, the micro-tapered hole group of industrial metal sheets is made by laser thermal processing technology, so the molten metal fragments will gather near the micro-tapered holes and form uneven burrs, which will affect the atomization amount of the metal sheets [20]. To solve the influence of burr caused by micro-tapered hole machining on the atomization effect, electrolytic polishing technology is proposed to treat metal sheets. First, a theoretical model of atomization amount and electrolytic polishing time was established to provide theoretical guidance for finding the appropriate electrolytic process time. The diameter and burr height of the metal sheets after different electrolysis time were then measured using an ultra-depth-of-field 3D microscope to verify the effectiveness of electrolytic polishing on the removal of burr of metal sheets. Lastly, the relationship between electrolytic time and atomization amount was determined through an atomization amount experiment, which proved that the electrolytic process had the effect of improving the atomization performance and guided the revision of the theoretical model. In sum, the electrolytic process was put forward to remove the burr of micro-tapered holes, and the relationship between electrolytic time and atomization amount was established, which provided a theoretical basis for improving the processing quality of industrial atomizers and further enhanced the atomization effect of micro-tapered hole piezoelectric atomizers.
## 4 Machine Learning and LoRaWAN
### Ensemble Learning, ML, and AI Based Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (s)** & **Year** & **Technique Used** & **Problem Addressed** & **Gap Identified** \\ \hline [PERSON] et al. [36] & 2020 & Deep Reinforcement Learning (DRL) & Optimal transmission parameter distribution in LoRaWAN & Existing ADR MAX not achieving best throughput \\ [PERSON] et al. [49] & 2020 & Deep Reinforcement Learning, Q-learning & Resource allocation in dense LoRa networks & Existing allocation strategies not supporting device mobility \\ [PERSON] et al. [31] & 2020 & Machine Learning, LSTM, Decision Trees & Scalability and network performance in LoRaWAN & High error rates in devices not addressed effectively \\ [PERSON], et al. [43] & 2021 & Support Vector Regression (SVR), DNN & Power consumption in IoT devices & Traditional methods not extending battery life sufficiently \\ [PERSON] et al. [37] & 2021 & Reinforcement Learning (RL) & Reliability in high node density LoRaWAN & Traditional ADR not optimizing SF and transmission power effectively \\ [PERSON] et al. [38] & 2021 & Deep Q-Network (DQN), Reinforcement Learning & Node durability in LoRaWAN & Current methods do not effectively extend node lifespan \\ [PERSON] et al. [33] & 2022 & Deep Learning, Gated Recurrent Unit (GRU) & Resource management in LoRaWAN & Inadequate SF allocation causing packet loss \\ [PERSON] et al. [34] & 2023 & Deep Neural Networks (DNN) & Packet loss due to inefficient SF utilization & Insufficiency of existing ADR and BADR techniques \\ [PERSON] et al. [35] & 2023 & Neural Networks (NN) & Indoor localization accuracy in LoRaWAN & Environmental factors causing variability in signals \\ [PERSON] et al. [39] & 2024 & Federated Reinforcement Learning (FRL) & Network reliability for LoRaWAN connected objects & Existing models do not optimize device throughput efficiently \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the Literature Review.
## 4 Discussion  
As shown in Figure 1c,d, the shark-fin antenna is equipped with a system for long-term evolutionary communication in addition to the monopole WAVE antenna and another WAVE antenna for multiple input-multiple output communication [16]. As various antennas are installed on a single common ground in a shark-fin module, the isolation characteristics of each component are important. For stable communication among vehicles on the road and between vehicles and base stations, isolation from each system is very important. Isolation characteristics are possible by evaluating the mutual coupling (S21) characteristics of each system. All the S21 characteristics between the WAVE antennas  
Figure 10: Simulated (blue color) and measured (black color) return loss of the monopole antenna with quadruple closed-loop devices.  
Figure 9: Characterization results of beam patterns radiated from WAVE antenna with/without quadruple closed-loop devices. [PERSON] and black colors denote simulation results obtained from the monopole-only and monopole with quadruple closed-loop devices, respectively. Blue color denotes the measured characteristics of the monopole with quadruple closed-loop devices.  
have a value of \(-30\) dB or less at the operating frequency. The acceptable isolation value of the proposed WAVE antennas was successfully achieved without additional isolation techniques. These performances make them highly suitable for efficient integration into the shark-fin vehicle environment.  
In Table 1, we have compared simulated and measured results of WAVE antenna. The simulated and measured return losses of the proposed antenna have values of \(-14\) and \(-17\) dB, respectively, or less at the operating frequency. The simulated and measured average gains were \(-0.28\) dBi and \(1.57\) dBi, respectively, and the simulated and measured peak gains were \(4.62\) dBi and \(6.29\) dBi, respectively.  
By installing a closed-loop device around the monopole WAVE antenna, the gain was increased, which made it possible to realize a quarter-wave monopole WAVE antenna with omnidirectional characteristics. The gain characteristic was improved as the designed closed-loop device reflected the signal radiated from the monopole antenna and concentrated the surface current around the antenna.
## 4 Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles
### Water Vapor and Refractivity Comparisons with Different SNR Groups
As follows from Figure 11, RO refractivity is negatively biased compared to \(N_{RAOB}\) in the low troposphere from the surface up to 3-5 km altitudes. One can note that the altitude and SNR dependence of N-residual follow the same pattern as for water vapor (compare Figures 10 and 11) in the troposphere, up to an altitude of about 12 km. Maximum N-bias is near the surface and can reach \(-\)7% for SNR bin 300-500 \(V/V\), but decreases to \(-\)2% with increasing SNR and becomes statistically insignificant above an altitude of 5 km. The N-residual standard deviation is 4-5% near the surface, decreasing to about 1% above the troposphere. For the altitudes above 12 km, the temperature differences between RO and RAOB cause unbiased variations of refractivity of 1%. \begin{table}
\begin{tabular}{c c} \hline
**L1 SNR Range** & **Mean Difference (STDs) (g/kg)** \\ \hline
300–500 \(V/V\) & \(-\)0.76 (1.14) \\ \hline
500–1000 \(V/V\) & \(-\)0.31 (1.02) \\ \hline
1000–1500 \(V/V\) & \(-\)0.30 (1.13) \\ \hline
1500–2000 \(V/V\) & \(-\)0.44 (1.24) \\ \hline \(>\)2000 \(V/V\) & \(-\)0.22 (1.16) \\ \hline \end{tabular}
\end{table} 
Table 1: Statistics of (RO-RAOB) specific humidity difference from the surface to 12 km altitudes for five SNR groups.
## 6 Discussion and Conclusions
### Theoretical Contribution
The model was validated using ML techniques by comparing MSE, and R\({}^{2}\) from SEM results, with ML results. Hence, the model can predict the value with high accuracy, 98%, when using random forest and bagging REPTree. The other models' accuracy reached 92%.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Implementation Details
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Description** & **Mathematical Expression** \\ \hline Accuracy & Degree of true value (correct identification of all abnormal acoustic events) measurements against all the evaluated instances. & \(Acc=\frac{TP+TN}{TP+FN+FP+TN}\) \\ \hline Macro Precision & The statistical average of the ratio of true positive abnormal sounds (k class events) against the actual positive number. & \(Pre=\frac{1}{k}\sum\limits_{i=1}^{k}\frac{TP}{TP+FP_{i}}\) \\ \hline Macro Recall & The statistical average of the ratio of true positive abnormal sounds (k class events) against predicted positive number. & \(Rec=\frac{1}{k}\sum\limits_{i=1}^{k}\frac{TP}{TP+FN}\) \\ \hline Macro Specificity & The statistical average of the ratio of true negative abnormal sounds (k class events) against predicted negative number. & \(Spc=\frac{1}{k}\sum\limits_{i=1}^{k}\frac{TN_{i}}{TN_{i}+FP_{i}}\) \\ \hline Macro F1-Score & The weighted average of Macro Precision and Macro Recall. & F1 = \(\frac{2 Pre\_Rec}{Pre+Rec}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the evaluation metrics.
# Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band
## 3 LTE-U Coexistence Mechanism
### Efficiency of Spectrum Utilization  
Once a fair coexistence of Wi-Fi and LTE-U is accomplished, we can concentrate on an efficient spectrum utilization of the unlicensed spectrum. To obtain the maximum throughput, each time slot should be assigned to the system that makes best use of it. In an LTE-U scenario, the LTE-U achieves a superior performance over Wi-Fi in terms of spectrum utilization owing to the advantage of its scheduler when compared to contention-based access. Hence, to increase the efficiency of the spectrum, LTE-U must be given greater access opportunity. However, this decision can significantly disturb the fairness criteria of a coexisting system. In this study, we combat this tradeoff through switching to most uncongested or free channels. The achieved throughput performance greatly relies on the number of users connected to the channel and the interference. Thus, operating in the cleanest channel provides more access opportunities for LTE-U users, increasing the throughput performance of the network. In addition, it helps Wi-Fi STAs deliver more access opportunities under low load conditions by deterring an LTE-U BS from keeping a channel busy for a longer period, i.e., if an LTE-U UE cannot finish its transmission during its ON period, it will not stay on the same channel to transmit during the next frame. Instead, it switches its existing transmission from the present channel to any other free channel. By doing so, Wi-Fi STAs in the current channel will acquire the opportunity to access the channel. Moreover, by switching the LTE-U channel after its transmission, the muting period of the LTE-U is highly reduced, contributing to an increase in spectrum utilization. We calculate the network efficiency as the ratio of total achieved network throughput by the proposed method to the maximum achievable network throughput (\(Th_{\max}^{k}\) ) as follows:  
\[\textit{Network~{}ef~{}ficiency}~{}(\eta_{k})=~{}\frac{Th^{\textit{wifi}}_{k} ~{}+~{}Th^{LTE-U}_{k}}{Th_{max}^{k}} \tag{12}\]