esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 4 Experimental Tests of the Monitoring System
### Lost/Wrong Packages at Several Distances  
This test is aimed at obtaining the error rate at different distances. This test was carried out for a duration of 20 min with a sampling time of 60 ms. The total number of sent frames was of 20,000 frames for this test. In addition, the number of lost/wrong frames when a wall of brick was in the middle was checked. Table 7 shows the experimental results, including the total number of lost/wrong frames and the percentage over the total number of frames. It can be appreciated that this value is remarkably low, even when there is a wall of bricks between the Coordinator Node and the Smart Measuring Node.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sampling Period (ms)** & **Jitter (ms)** \\ \hline
60 & 0.8 \\
40 & 0.9 \\
30 & 1.2 \\
24 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Jitter for the measuring instant at multiple sampling operations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance (m)** & **Lost/Wrong Frames** & **Percentage** \\ \hline
0 & 4 & 0.02\% \\
1 & 2 & 0.01\% \\
5 & 2 & 0.01\% \\
8 & 3 & 0.02\% \\
12 & 13 & 0.07\% \\
1 (with brick wall) & 0 & 0\% \\
5 (with brick wall) & 4 & 0.02\% \\
8 (with brick wall) & 5 & 0.03\% \\
12 (with brick wall) & 24 & 0.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Lost/wrong frames at different distances even with a brick wall in between.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Values Measured** & **Minimum (ms)** & **Maximum (ms)** & **Jitter (ms)** \\ \hline
1 (23 bytes) & 11.44 & 11.94 & 0.5 \\
4 (29 bytes) & 12.68 & 13.28 & 0.6 \\
8 (37 bytes) & 14.5 & 15.34 & 0.84 \\
16 (53 bytes) & 17.7 & 18.7 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reception timestamps of Smart Measuring Nodes at the Coordinator Node (in ms).
# Modal Decomposition of the Precessing Vortex Core in a Hydro Turbine Model
## 1 Introduction
A promising theoretical framework to analyze and control the PVC is based on global linear hydrodynamic stability analysis (LSA). Within this framework, the PVC is associated with an unstable global mode of the swirling mean flow [39; 40]. LSA can be used to identify the region where the PVC is generated, and it provides deeper comprehension of flow control methods for mitigating the PVC [41; 42]. To validate the results from LSA, detailed experimental information is required regarding the onset of the instability and its mode shape. It should be noted that most experimental data available in the literature are generally limited to single operating conditions at either the part load or the BEP regimes. This study extends the previous observations and focuses on the evolution of the PVC, as the operating conditions are varied in a wide range (flow rates). This allows to track the PVC formation from deep part-load to over-load regimes (including BEP). To characterize the PVC evolution, we perform the modal decomposition of experimental data gathered in the cone of a hydro turbine model. The analysis based on velocity and pressure data shows for the first time that the PVC dynamics consist of a \(m=1\) single-helical part as well as a \(m=2\) double-helical part, where \(m\) is the azimuthal wave number. This information is quite important for developing concepts of PVC mitigation based on active flow control. The paper is outlined as follows. In Section 2, the experimental methods are described, including the test setup, PIV and pressure fluctuation measurements. The post-processing methods are described in Section 3. The results are provided and discussed in Section 4. Finally, the main observations are summarized in Section 5.
# Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
## 2 Heterogeneous Agent Models as Optimal Control Problems
### Approaches Used in Economics to Handle High-Dimensional Discrete Time Model Problems  
We refer to [17] for a broad overview of computational methods for solving high-dimensional discrete time economic models. Let us briefly summarize the most important approaches and additionally reference some more recent works.  
Conventional numerical methods to solve dynamic economic models do not allow feasible or accurate computations in higher dimensions. Stochastic simulation algorithms build on Monte Carlo integration and least square learning. When the former does not achieve a high accuracy, the latter may become unstable. Further, projection methods build on tensor product constructions and are thus not feasible in high dimensions. Last but not least, perturbation methods that solve for a steady state by using Taylor expansions have uncertain accuracy.  
To overcome the above described issues, the approaches were adapted to handle high-dimensional problems. In [18], a generalized stochastic simulation approach is proposed that replaces the Monte Carlo integration with a deterministic one, and the least squares learning with numerically stable regression methods. In [19], sparse grids are used to replace the expensive tensor product grids. For perturbation methods that are feasible in higher dimensions, see [20; 21].  
Sparse grids in combination with a fixed point iteration on the Euler equation are proposed in [22] to solve a multi-country model featuring up to twenty state variables. Combining it with a simulation to determine the high probability area and then using a principal components transformation allows it to focus the computation on the relevant do main. Parallel adaptive sparse grids were used in [23] to solve high-dimensional stochastic dynamic models where functions are interpolated on a sparse grid either within time or value iterations. Further, in [24], dynamic portfolio choice models are solved with adaptive sparse grids, where in addition to an adaptive approximation of the value function, separate adaptive sparse grids for policy functions are also computed. A recent review on sparse grids for dynamic economic models can be found in [25]. The finite difference operators on sparse grids from [5] were recently adapted for economic applications in [26].  
For a general overview of stochastic optimal control in the discrete time case, we refer to [27] and the references therein.
## 4 Experimental Verification
### Calibration of Combined Strain Influence Line  
For the 47 sets of strain responses collected from the calibration vehicle test, the GA-proposed optimization method was used to determine the optimal combination of strain responses. The result of the optimization iteration is shown in Figure 13.  
Figure 12: Part of the strain response when the vehicle passes on the model from the transverse position of x = \(-\)1.5 cm: (**a**) signal collected by FAD sensors and (**b**) signal collected by axle weight sensors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Testing** & **Speed (cm/s)** & \
\begin{tabular}{c} **Practical** \\ **Wheelbase (cm)** \\ \end{tabular} &
\begin{tabular}{c} **Identifying** \\ **Wheelbase (cm)** \\ \end{tabular} & **Error (\%)** \\ \hline
1 & 12.26 & & 37.68 & \(-\)0.84 \\
2 & 12.34 & & 38.25 & 0.66 \\
3 & 12.34 & & 38.13 & 0.34 \\
4 & 12.44 & & 37.82 & \(-\)0.47 \\
5 & 12.37 & 38 & 37.98 & \(-\)0.05 \\
6 & 12.44 & & 37.82 & \(-\)0.47 \\
7 & 12.40 & & 38.07 & 0.18 \\
8 & 12.47 & & 37.91 & \(-\)0.24 \\ Mean & 12.38 & & 37.96 & 0.41 \\ \hline \hline \end{tabular} Note: The mean is the average of the absolute error.  
\end{table}
Table 8: Part of identification results of axle information.  
The minimum variation coefficient after GA global optimization is 0.03467, and the optimal combinations coefficients of six measurement points are \(k_{i}\) = [9.645, 4.017, 1.255, 2.071, 14.709, 8.456].  
The optimal combined strain response is used to identify the optimal combined strain influence line under the vehicle load at different transverse positions. The combined strain influence line takes the length of the middle three diaphragms from the second diaphragm on the left to the fifth diaphragm in Figure 9, and the position of the second diaphragm is defined as zero along the longitudinal bridge direction. The identified results are shown in Figure 14.  
It can be seen that the combined strain influence line has good stability when the calibration vehicle is in different positions in the transverse direction. To simplify the axle weight load identification, the influence of the transverse position of the vehicle load can be disregarded. In order to reduce noise interferences, the combined influence lines at multiple transverse positions are averaged for the subsequent axle weight load identification, and the averaged result is shown in Figure 15.  
Figure 14: The identified combined strain influence line under vehicle loads at different transverse position.  
Figure 13: Convergence curve of the optimal solution.  
As shown in the figure, the strain influence line is incomplete, symmetric, and smooth. The main reason is that the steel plate of the model is too thin (2 mm), so the welding leads to some deformations and local unevenness in the model. In addition, the vibration noise of the vehicle traveling on the uneven deck also introduces some identified errors.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 3 Results  
### The Fuzzy Control System Design  
As observed in Figure 3, the design of the fuzzy based air quality index prediction model is designed using the Fuzzy Toolbox and the Mamdani FIS, integrated within the MATLAB environment. It illustrates how different air pollutants affect the Air Quality Index.
## 4 Simulation Results
### Optimal Solutions Obtained by Proposed Method  
The proposed method is applied to find the optimal unit commitment and demand response considering the data described above. The convergence criterion is that 10 consecutive iterations yield the same solution. The studied problem consists of 216 unknown binary variables and 144 unknown random variables.  
The optimal solution takes 46 master-slave iterations and 106.99 s to converge. The expected value of total cost is $23,737.68. Table 5 shows the expected values of optimal power injections obtained by the interior point method on the slave level. Table 6 illustrates the expected values of optimal demand responses that are obtained on the master level. The following comments can be made.  
(a) A positive expected value of power injection at the swing bus (\(\widetilde{P_{sw}}(t)\)) indicates that the factory purchases power (\(\widetilde{P_{p}}(t)\)) while a negative sign implies that the factory sells power (\(-\widetilde{P_{s}}(t)\)) to the utility. The factory sells power to the utility at hours 8:00 a.m.-2:00 p.m., 8:00 p.m. and 9:00 p.m. because the tariff during these periods is high (1315/MWh). In total, 46.1 MWh from the factory is sold to the utility.  
(b) Most of the generated PV power is consumed in the factory rather than imported into the utility power system.  
(c) Since the tariff for purchasing power from the utility is low during 1:00-8:00 a.m., the elastic loads consume almost all of energy during this period. To fulfill the total demand constraints (75 MWh), the production line at bus 6 also consumes energy during 3:00-7:00 p.m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bus No.**} & \multirow{2}{*}{**P\({}_{\text{min}}\) (MW)**} & \multirow{2}{*}{**P\({}_{\text{max}}\) (MW)**} & \multicolumn{3}{c}{**Cost Coefficients**} \\ \cline{4-5}  & & & \(\mathbf{a_{i}}\) (\$/h) & \(\mathbf{b_{i}}\) (\$/MWh) & \(\mathbf{c_{i}}\) (\$/MWh2) \\ \hline
3 & 0.5 & 4 & 151.28 & 87.87 & 0.14 \\
4 & 0.6 & 3.5 & 125.21 & 67.82 & 0.65 \\
5 & 0.9 & 4.5 & 89.21 & 31.37 & 1.1 \\
6 & 1.1 & 3 & 35.48 & 17.6 & 0.1416 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost coefficients and MW limits of generators.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Periods** & **Prices (\$/MWh)** \\ \hline
0:00 a.m.–8:59 a.m. & 65 \\
9:00 a.m.–8:59 p.m. & 131 \\
9:00 p.m.–11:59 p.m. & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of elastic loads.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bus No.** & **Ramp Rate (MW/h)** & **Start Up Cost (\$)** & **Minimum Up Time, \(M_{i}^{min}\) (h)** & **Minimum Down Time, \(M_{i}^{eff}\) (h)** \\ \hline
3 & 2.5 & 161.84 & 4 & 2 \\
4 & 2.0 & 122.53 & 3 & 2 \\
5 & 3.5 & 175.34 & 3 & 1 \\
6 & 2.0 & 148.63 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operational parameters of generators.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bus** & **Must-Run Hours** & **Maximum Loads (MW)** \\ \hline
6 & 10 & 7.5 \\
16 & 6 & 2.0 \\
18 & 7 & 2.5 \\
20 & 6 & 2.5 \\
26 & 5 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time-of-use pricing.
## 3 Proposed Framework
### Data Pre-Processing  
Social media data are raw and noisy data at first when considering processing in machine learning algorithms; these raw data need pre-processing and cleaning so that they can be fed into machine learning algorithms, so we pre-processed and cleaned the collected tweets using the Natural Language Toolkit (NLTK) in Python [49] and also utilized the Spacy function with the core English model for lemmatization ([[https://spacy.io/usage/models](https://spacy.io/usage/models)]([https://spacy.io/usage/models](https://spacy.io/usage/models)) (accessed on 1 June 2020)). Pre-processing involves different steps such as removal of stop words from the tweet text, punctuation removal such as commas, exclamations, full stops and question marks as they do not have any significance in the machine learning context. Removal of stop words such as, 'the', 'is', 'at', 'on', etc. Furthermore, URLs were removed as we did not need that in our dataset. As numbers such as 22, 456, and 12 hold no meaning in the tweet text, so we removed these types of numbers. The next step was to remove the special symbols from texts such as '@', '$, '\', etc. Then all the tweets were converted into lower-case letters; next, we checked the tweets for miss-spellings and auto-corrected the spelling mistakes in tweets expressed in the English language through lemmatization. After doing the above pre-processing steps we had 69k tweets that we used for annotation and classification of situational information further.
## 2 Related Work  
Simulating LoRa devices in a real-world (smart city) scenario is very difficult, as the effects of screening, thermal noise, large-scale fading, and small-scale fading are random in nature and contribute in a significant manner towards collisions, re-transmissions, and further collisions as the number of devices is increased. Ref. [17] in 2016 estimated that a maximum of 120 devices can coexist simultaneously in a given area based on a practical matrix called data extraction rate (DER). The work considered LoRa to have imperfect orthogonality and did not consider re-transmission success. Ref. [18] published on LoRa scalability used packet delivery rate (PDR) and bit error rate (BER) to determine success of transmission; however, Ref. [19] described packet error rate (PER) as the factual criteria for determining success but did not take re-transmissions into consideration; Ref. [20] worked on a dense urban scenario of Sigfox nodes based on the signal-to-noise ratio (SNR) and BER to measure success probability. Work in [21] carried out extensive trials over a campus with LoRa while studying different propagation models. Authors in [22] used NS3 and Matlab to carry out simulation for 100 devices with faster run-time in comparison to previous work. In [23; 24], work was carried on for city-based use cases along with gateway location while using the Ericsson propagation model. [PERSON]. [19] presented experimental work on a number of Sigfox devices that can coexist without collision in a smart city environment, while the same was substantiated by [25] and described PDR as correct matrices for determining successful delivery of transmission. PDR can be calculated as the ratio of the total number of up-link messages to received messages. In [25], the author also established that PDR decreases as distance increases and that LoRa gives a higher performance in terms of PDR in comparison to Sigfox, however work used probability of outage as major criteria for deriving packet loss and did not consider dense deployment and thus arrived at a lower PER/PDR value. Previous works have not considered the effects of path loss in urban environments and have not carried out practical comparisons of LoRa and Sigfox to arrive at a number of nodes that can coexist.
###### Abstract  
Two medium-low temperature coal tars (MLCTs) derived from the pyrolysis of low-grade bituminous coal were separated into 11 narrow fractions by true boiling distillation. The primary property and chemical composition analysis of MLCTs and their distillate narrow fractions were investigated at the macroscopic and molecular level by gas chromatography-mass spectrometer (GC-MS) and proton nuclear magnetic resonance (\({}^{1}\)H NMR). The two MLCTs show obvious characteristics of medium-low temperature coal tar, including a high H/C, high-oxygen and nitrogen, low-sulfur, low-density, and low viscosity. As the boiling point increases, the molecular weight of each distillate fraction increases continuously. Meanwhile, the yield of each distillate fraction increases gradually, except for the 270-300 \({}^{\circ}\)C distillate fractions. The oxygen content in the 170-230 \({}^{\circ}\)C distillate fractions is much higher than that of the other distillate fractions. The dominant groups of compounds in the MLCTs were saturates, aromatics, and resins, and the resin content was above 24.5 wt%. The molecular composition of the below 170 \({}^{\circ}\)C fractions mainly consists of benzene, toluene, and xylene, and the main phenolic compounds in the 170-230 \({}^{\circ}\)C distillate fraction are low-rank phenols, such as phenol, cresol, and xylenol. Although the macroscopic properties of the MLCT-Z and MLCT-S were quite similar, the molecular composition, the group composition and hydrogen distribution in each MLCT and its narrow distillate fractions are still different. The present work has contributed to our present understanding of the composition of MLCTs and to the guiding of the efficient processing of MLCTs.  
medium-low temperature coal tar; narrow fractions; molecular level; true boiling distillation; GC-MS; \({}^{1}\)H NMR +
Footnote †: journal: Applied Sciences
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 5 Experimental Methodology, Results, and Discussion
### Scenario 3: BBR vs. PCC-BBR
This is likely because the unstable throughput of PCC could have resulted in buffer overflow and packet drops. Both clients benefited from reduced packet loss after the switch. Client 1 (BBR) displayed a more significant improvement, with median packet loss dropping from around 29% to near 10% in Figure 16. This further emphasizes the positive impact of BBR's congestion-avoidance strategy, which helps minimize packet drops by ensuring a smoother and more predictable data flow. RTT and Jitter  
Analyzing Scenarios 1, 2, and 3 reveals the critical role dynamic CCA selection plays in achieving fair resource allocation. Static CCAs can struggle to adapt, leading to underutilized bandwidth or flow starvation. A DRL model, when coupled real-time network data analysis, can choose the most suitable CCA for each flow, which prevents these issues. Furthermore, fairness is crucial in ensuring a smooth user experience. A DRL model trained with fairness objectives can select CCAs that optimize individual flow performance while promoting fair resource distribution. This dynamic adaptation based on network conditions ensures optimal performance and user experience across all connections. Figure 16: Packet loss ratio comparison of Client 1 (BBR) vs. Client 2 (PCC->BBR) before and after switching at 25 s. Reduced packet loss ratio from 32% to 10% after the switch (**a**). The median packet loss for Client 1 (BBR) dropped from around 29% to near 10% (**b**). Figure 15: Jitter comparison of Client 1 (BBR) and Client 2 (PCC->BBR) before and after switching at 25 s. Average jitter for Client 2 dropped from around 3 ms to nearly 1 ms after switching (**a**). Median jitter for Client 1 remained near 0 ms (**b**).
## 2 Signal Models and Problem Formulation
### Regularization Reformulation
The vertical and horizontal differences of \(\mathbf{\tilde{S}}\) can be presented as \(\mathbf{R}_{L}\mathbf{\tilde{S}}\) and \(\mathbf{\tilde{S}}\mathbf{R}_{L}^{T}\), respectively, where \(\mathbf{R}_{L}\) is the first-order difference matrix [42], defined as  
\[\mathbf{R}_{L}(m,n)\triangleq\left\{ \begin{array}{ll}1,&n=m+1,\\ -1,&n=m,\\ 0,&\text{otherwise}.\end{array} \right.\]  
when the spatial data \(\mathbf{\tilde{S}}\) is referred as a vector via vector-matrix operator, the vertical and horizontal difference matrices are presented as \(\mathbf{D}_{v}=\mathbf{I}_{L}\otimes\mathbf{R}_{L}\) and \(\mathbf{D}_{h}=\mathbf{R}_{L}\otimes\mathbf{I}_{L}\), respectively. Then, these two difference matrices are incorporated into a block vector \(\mathbf{\tilde{H}}=[\mathbf{D}_{v};\mathbf{D}_{h}]\). With due consideration of \(N\) endmembers aforementioned, the ultimate vector-based difference matrix is constructed as \(\mathbf{H}=\mathbf{\tilde{H}}\otimes\mathbf{I}_{N}\). The regularizer \(\sum_{\{m,n\}\in\varepsilon}\|\mathbf{s}_{m}-\mathbf{s}_{n}\|_{1}\) in (7d) can be reformulated into the form of a matrix-vector product as  
\[\phi_{h}(\mathbf{S})=\sum_{\{m,n\}\in\varepsilon}\|\mathbf{s}_{m}-\mathbf{s}_{ n}\|_{1}=\|\mathbf{H}\mathbf{s}\|_{1}, \tag{10}\]  
where \(\mathbf{s}\triangleq\mathbf{vec}(\mathbf{S})\in\mathbb{R}^{NL}\). In addition, the expression of \(\|\mathbf{S}\|_{1,1}\) can be converted directly to \(\|\mathbf{s}\|_{1}\). Furthermore, One can observe that (6) is a bi-convex problem, that is, it is convex when we fix either \(\mathbf{A}\) or \(\mathbf{S}\) alone. Alternating optimization (AO) is widely used to solve the bi-convex problem.
## 2 Materials and Methods
### Analysis of Multiple Input Features
#### 2.2.1 Rotor Motion Equation  
According to the rotor motion Equation (2), the rotor angle of generators is directly affected by the rotor speed, electromagnetic power and mechanical power of generators.  
\[\left\{\begin{array}{l}\frac{d\delta}{dt}=\omega-1\\ T\frac{d\omega}{dt}=\frac{P_{m}-P_{e}}{\omega}-D\omega\\ \end{array}\right., \tag{2}\]  
where \(\delta\) is the rotor angle; \(\omega\) is the rotor speed; \(P_{m}\) is the mechanical power; \(P_{e}\) is the electromagnetic power; \(T_{j}\) is the inertia time constant; and \(D\) is the damping coefficient of the generator.  
The electromagnetic power can be calculated by Equation (3), during the time domain simulation.  
\[Pe=V_{d}l_{d}+V_{d}l_{q}, \tag{3}\]  
Figure 5: Principal diagram of SVM kernel function.  
where \(V_{d}\) and \(V_{q}\) are the \(d\)-axis voltage and \(q\)-axis voltage, respectively; \(I_{d}\) and \(I_{q}\) are the \(d\)-axis current and \(q\)-axis current, respectively.  
Due to the rapid development of the transient process, it is assumed that the mechanical power \(P_{m}\) does not change during the short process. The damping coefficient \(D\) is also neglected in this study. When a certain fault occurs, the electromagnetic power \(P_{e}\) of generators will change rapidly with the changes of voltage and current variables; the imbalance power between \(P_{m}\) and \(P_{e}\) will then cause the rotor speed \(\omega\) to change. Rotor speed \(\omega\) changes will further affect the rotor angle \(\delta\), that is, the transient stability of power system can be partially traced back to \(P_{e}\). Therefore, the electromagnetic power can also be considered as an important factor affecting the transient stability besides the existing input features [27], namely, the rotor angle \(\delta\) of generators, rotor speed \(\omega\) of generators and voltage amplitude \(V\) of buses.