esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture  
Figure 1 depicts the proposed architecture. It includes an SDN controller that, at power-on, obtains the complete list of hosts, switches, and links from the controlled network topology. Using these data, the controller creates a Networkx [25] algebraic network representation. Then, the controller, by querying that abstracted topology, obtains a list with all paths among hosts. The unique identifiers of all these paths are transferred to an MAB agent that decides, in each system time slot, which path (arm in MAB nomenclature) to use for message-routing. These decisions are made to maximise the expected reward  
in the long run; reward, in this case, represents the inverse of end-to-end delay associated with the path previously selected by the agent. Hence, in each time slot, the MAB agent chooses a path and relays its unique identifier to the controller, which, in turn, deletes old flow rules and installs new ones in each switch of that path. Then, the controller actively measures the selected path's end-to-end bidirectional delay and returns it to the MAB agent so that a reward can be computed.  
The end-to-end path delay is the sum of the individual delay contributions by all the network devices involved in that path. In addition, the delay induced by each network device to the end-to-end path is directly proportional to the local congestion level at that device. This means, when considering an end-to-end path with two devices, A and B, if device A is more congested than device B, device A's delay contribution to the end-to-end delay path will be higher than that of device B.  
The MAB agent is controlled by a novel and generic MAB algorithm, React-UCB, which we introduce to optimize the routing in dynamic communication networks. The next sections detail React-UCB, explaining how it decides the arm to pull under piecewise-stationary reward distributions in each time step. The main goal of React-UCB is to learn about end-to-end path delay minimization (i.e., learn about the more robust end-to-end path with the minimum aggregated congestion level), mapping arms to paths and rewards to path delays.
## 3 Case Study Analysis of Online PD Monitoring in Power Transformers
### Transformer C
#### 3.3.1 Results of Online Monitoring of Partial Discharges
The statistical analysis carried out to compare the number of pulses recorded by the UHF and AE methods showed that in as many as 39.7% of cases (FN), only acoustic emission signals were detected (Table 9). This could be related to the excitation of piezoelectric sensors during storms and the accompanying heavy rainfall. During the two weeks of the monitoring system operation, there were 4 days with intensive weather phenomena. For comparison, the total time in which only the UHF method recorded pulses was 17.3% (FP). Both methods simultaneously detected pulses in 32.7% of cases (TP), and in 10.3% of cases, their absence was indicated. The Accuracy Index (0.429) shows that the results of both methods were consistent in 42.9% of the analyzed cases, which indicates a relatively low level of overall consistency of the UHF and AE systems. The Jaccard Index (0.364) reflects that only 36.4% of the cases in which at least one method detected a PD pulse include situations in which both methods simultaneously detected the same event.
## 3 Materials  
Under the condition of the same amount of SBS (4%), different amounts of SMC normal temperature-modified asphalt were added. According to the content of SMC, an asphalt modifier is 10%-12% of the asphalt quality (DBJ 64/T060-2016) [27], and the literature [28] on the properties of SMCSBS composite modified asphalts, 8%, 10%, and 12% of SMC content are selected to prepare a composite modified asphalt. The SMC normal temperature-modified asphalt was added into the SBS-modified asphalt according to the above content, the heating temperature was 110-120 \({}^{\circ}\)C, and the mixing time was 1-2 h (DBJ 64/T060-2016). Referring to the SMC Normal Temperature Modified Asphalt and Asphalt Mixture Design and Construction Technical Guide by the Scientific Research Institute of the Ministry of Transport (2014) testing method, the main technical indexes of normal temperature modified asphalts with different SMC contents were obtained through testing. The test results are shown in Table 1.  
One dense-graded asphalt mixture (AC-16) was used for evaluation in this research. AC-16 mixtures are commonly used in China for the surface course of expressway bituminous concrete pavements. According to the JTG F40-2004 standard [29], the recommended mid-value gradation for this mixture was selected. The aggregate gradation and mix design are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**SMC Content/\%**} & \multicolumn{2}{c}{**25 \({}^{\circ}\)C Penetration/0.1 mm**} & \multicolumn{2}{c}{**Softening Point \({}^{\circ}\)C**} & \multicolumn{2}{c}{**5 \({}^{\circ}\)C Ductility**} \\ \cline{2-7}  & **Average** & **Standard Deviation** & **Average** & **Standard Deviation** & **Average** & **Standard Deviation** \\ \hline
8 & 191 & 0.211 & 74 & 0.050 & 89.7 & 0.531 \\
10 & 228 & 0.325 & 56.5 & 0.400 & \textgreater{}100 & 0 \\
12 & 248 & 0.561 & 55 & 0.636 & \textgreater{}100 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Process indexes of modified asphalts with different content.  
The fatigue performance of SBSSMC modified asphalt mixtures are studied by selecting 4.3%, 4.8%, and 5.3% of the asphalt aggregate ratio as specimens.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 4 Proposed Methods  
### U2 Net Detection Models  
U2 Net is a deep learning network model based on a semantic segmentation proposed in 2020, which is widely used in saliency detection tasks. U2 Net is based on an encoding-decoding structure, which introduces a variant of the UNet structure. U2 Net is characterized by having an inner and an outer layer, with the outer layer adopting a U-shape structure and the inner layer adopting a U-shape structure as well, which is referred to as a double U-shape structure. In the external U-shaped structure, encoders and decoders are used to effectively capture features at different scales. The internal U-shaped structure contains a small UNet structure at each stage of the encoder and decoder, which is called the RSU module. When the features of the input image are collected through the RSU module, a wider receptive field can be obtained by using expansive convolution without increasing the amount of computation. This design is used to capture local features and edge information, making the segmentation results more accurate.  
While U2 Net's strengths are great, there are still shortcomings. The main manifestation of this is the loss of detail information after constant sampling by the RSU module. At the same time, due to the use of conventional convolutional kernel, in the boundary, details cannot be captured with sufficient accuracy, which easily leads to the lack of edge information. The task of this paper is to detect defects in blister tablets, and a significant portion of these defects are located at the edges of the tablets, because the edges of the tablets are more prone to defects. To solve these problems, this paper introduces the large kernel attention mechanism.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 2 Methodology
### Parallel Orbit Determination Process  
The parallel approach proposed for the ultra-rapid orbit determination of the double-differenced network solution mainly adopts the MPI/OpenMP hybrid parallel programming model. The optimized orbit determination process is shown in Figure 1. After reading observation files, navigation ephemeris files, and some auxiliary model correction files, data preprocessing includes gross error detection, basic model correction, forming double-differenced baseline, and floating solution. Then, the entire time period to be calculated is divided into n segments, and multiple processes are developed based on MPI to read and construct sub-normal equations in parallel. Each process read from the first epoch to the last epoch of the current period so that the consistency of the current arc parameter sequence can be maintained. The above multiple sub-normal equations are accumulated to obtain the total normal equation. Next, in order to further improve the efficiency of orbit determination, the OpenMP multithreading model is used for the parameter elimination process. Finally, after obtaining the precise dynamic parameters by the LS estimation, the same multi-thread model is used to realize the parallel orbit integration to obtain the final satellite POD results. The entire parallel optimization does not destroy the correlation between the parameters without the ambiguity segmentation problem. The input and output of orbit determination do not need to be adjusted greatly, which is easy to implement. It can be applied to single satellite POD as well as multi-GNSS POD.
# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 3 Speaker Identity Discrimination in Different Feature Space
### Pitch Discrimination  
Pitch is one of the most important features for describing the excitation source property in speech signal processing [24]. We used the Normalized Cross Correlation Function (NCCF) coefficients [22] extracted by using the Kaldi pitch tracker [25] to represent the pitch information for our ASV. The NCCF is not the normal pitch value; it is a pitch estimation method that is very similar to the autocorrelation function, but it better follows the rapid changes in pitch and the amplitude of speech signal. In comparison with the normal autocorrelation function, the peaks corresponding to pitch period in the NCCF are more prominent and less affected by the rapid variations in the signal amplitude. In [22], the effectiveness of NCCFs has been proved on speech recognition tasks. Figure 1 shows the NCCF contour of the same female speaker's reading and singing speech with the same text. Figure 2 shows the NCCF contour of reading and singing speech between two different female speakers, given with the same text.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **\#Target Speakers** & **\#Test Segments** & **\#Target Trials** & **\#Non-Target Trials** \\ \hline
1 s-1 s-GD & 600 & 600 & 300 male, 300 female & 2700 male, 2700 female \\
1 s-1 s-GI & 600 & 600 & 600 & 11,400 \\
3 s-3 s-GD & 300 & 300 & 150 male, 150 female & 1350 male, 1350 female \\
3 s-3 s-GI & 300 & 300 & 300 & 5700 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Text-dependent short-time ASV task description.  
In Figure 1, it is clear that the NCCF contour of reading speech deviates far from the one of singing speech, with the same speaker and the same text. By comparing the NCCF trajectories in Figure 2, we can see that the difference of pitch information between two different speakers from singing speech is larger than the one from reading speech, even under the text-dependent task.
## 4 Case Studies
### Optimization Results
#### 4.1.1 Typical Summer Day
Meanwhile, a relatively large amount of the load demands of peak periods is shifted to the period 10-14; this leads to an increase in the load demands so as to consume PV-rich power, which is significant for PV power accommodation in the summer scenario. In this set of tests, the cooling is mainly used for the space cooling. The EC equipped in the system is the main cooling unit to produce cooling energy. Shown in Figure 7 is the optimized operation state of the energy conversion facilities over a 24-h period on an hourly basis. Figure 8 illustrates the cooperative operation of the ES, CS and HS. In valley periods, e.g., 1-7, as the electricity is cheap, more electricity is purchased, the cooling energy is produced by the EC, and the heat energy is produced by the EH. Excess capacity is stored in the HES for future usage during peak hours, e.g., periods 17-20, as indicated in Figure 8. In peak periods, e.g., 17-20, as the electricity has a higher price, less electricity is purchased and the MEMG switches to consuming gas and producing power and heat using the CHP. During the period, the AC equipped in the system converts heat energy to output cooling energy to supply part of the cooling load demand while reducing the cooling energy produced by the EC to save cost. Electricity purchased from the main grid, gas purchased from the gas network and PV power accommodation under the optimization framework with and without considering IDR are comparatively studied in Figure 9. We can learn from the figure that the amount of  
Figure 8: Scheduling of the electrical energy storage (ES), CS HS and the state of charge (SOC) of the ES, CS and HS on a typical summer day. Figure 7: Operation state of energy conversion facilities on a typical summer day. CHP: combined heat and power; GB: gas boiler; EC: electric chiller; EHP: electrical heat pump; AC: absorption chiller. the purchased electricity increased in the valley periods, e.g., 3-7, and reduced at the peak periods, e.g., 17-20, because of the presence of IDR and HES. There is a little difference in the amount of purchased gas because the gas price is considered to be fixed. The optimal daily cost of the studied system, which includes the electricity purchase cost from the main grid and gas consumption cost of the CHP and GB is attained as 881.3V, which was 932.044V without considering IDR. This represents a reduction of 5.44% in the energy purchasing cost. It is also observed in Figure 9 that curtailment of PV power occurs during the period from 10 to 14 before implementing IDR. After implementing IDR, all the available PV power is utilized to supply the electrical load demand. This is because IDR shifts the electrical, cooling and heat loads from peak load hours to high PV generation hours, which effectively improves the ability of the PV power accommodation in the MEMG system.
# Extended Spatial-Index LED Modulation for Optical MIMO-OFDM Wireless Communication
###### Abstract  
An efficient optical modulation technique for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) visible light communication system is proposed in this paper. The proposed modulation technique is termed as extended spatial-index light-emitting diode (LED) modulation. In the proposed technique, the indices (the spatial domain) of the LEDs are exploited in a dynamic style to not only get rid of the optical OFDM time-domain (OFDM\({}_{td}\)) shaping problem but also to expand the LED indices spatial modulation domain. The indices of the active LEDs in the proposed technique are changed from the two LEDs active situation to the situation where all or several LEDs are active. Moreover, within the selected active LED indices, the power weight distribution and the positions of the OFDM components are varied to expand the resultant spatial domain. Therefore, the proposed technique offers a considerable spectral efficiency improvement over the up-to-date LED index OFDM modulation schemes even with a lower number of LEDs. The key idea of the proposed technique is to maximize the LEDs' indices spatial position (spatial domain) utilization, where both the power weight allocation and the positions of the complex OFDM time domain components are varying several times over the same active LED indices combination, which improve the optical system spectral efficiency. The simulation results asserted the superiority of the proposed technique, as it improves both the average bit error rate (ABER) and the achievable data rate (\(R\)) compared with existing up-to-date OFDM-LED index modulations with even lower computational complexity.  
optical MIMO-OFDM; visible light communication (VLC); OFDM-LED index modulation; generalized led index modulation +
Footnote †: journal: Electronics Letters
# COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
## 4 COVID-19 Data: Deaths, Confirmed, Recovered  
As a result of the outbreak, many organizations and individuals have created publicly available datasets regarding COVID-19 data for analysis and research. One of the most popular attempts is the White House in collaboration leading research groups, in order to provide a dataset of over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2 and related coronaviruses.  
In this study, two publicly available datasets, accessible on kaggle.com, were used for model development and analysis purposes: the \"Novel Corona Virus 2019 Dataset\" [61] and the population-by-country dataset [62]. For access to both datasets see Appendix A.  
The former is comprised of time-series information concerning the progression of the virus in each country, while the latter was used to extract, model and forecast the active virus cases as a percentage of the total population for each country. This was accomplished by diving each time-series value by the total population during training, evaluation and testing, allowing for a more clear picture to be formed as well for more fair comparison to be drawn between the spread rate of the disease among the different countries.  
The \"Novel Corona Virus 2019 Dataset\" contains daily information regarding:  
1. The number of confirmed COVID-19 cases
2. The number of recovered COVID-19 patients
3. The death toll due to COVID-19  
The number of active cases in each country for each day was calculated by subtracting both the recovered patients and the number of deaths from the confirmed cases.  
The distribution of total confirmed cases per country as of 4 May 2020 is displayed in Figure 1, while Figure 2 shows the number of active cases, also as of 4 May 2020, at using logarithmic scale.  
Based on the statistics presented is Figure 1, the 10 countries with the greatest number of total confirmed cases were kept, accounting, as of 4 May 2020, for more than 70% of all confirmed cases worldwide. These countries, being the most affected by the virus and probably in later stages of the pandemic could act as a reference point and a paradigm for newly-infected countries.  
Lastly, the percentage of active cases with respect to the total population per country is shown for each country in Figures 3 and 4. In the former, the spread of the pandemic is shown for each country throughout the whole period since January, while, in the latter, the first confirmed case in each country is used as moment zero. This alignment between the different time series data could help form a better picture and gain a deeper understanding of the spread of the pandemic in each of these ten countries as well as better knowledge of the stage of the pandemic in each country.  
Figure 3: Percentage of active cases with respect to the total population per country using linear scale.  
Figure 2: Number of active cases per country using logarithmic scale.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 7 Results
### Fixed Frame Length  
We now turn to the fixed frame length formulation, FRM/1. One clear application for this formulations is cases where the frame length is fixed by external factors, so, it is more efficient to include it as a parameter rather than a variable in the optimization. However, another potential use is to provide an alternative algorithm for finding an optimal solution in cases where the general formulation takes too long to solve, as we saw in the results for FRM/2. We therefore ran the same experiments as above, but with FRM/1, for each frame length between 1 and 15 (the maximum) inclusive. We then selected the frame length and corresponding solution where the objective value was the lowest, to obtain the final, optimal solution for each experiment.  
Figure 4: Pilot rate minimization with variable frame length: objective value.  
Figure 5: Min pilot rate with variable frame length: optimal frame length.  
Using this iterative approach with FRM\(/1\), we were able to obtain complete results for minimum pilot rate optimization. The objective values for each experiment are shown in Figure 6. These values were normalized to the obtained frame length.  
We see a clear advantage using the iterative approach in terms of solution times (Figure 7). The total solution times here were less than 1 second in all cases for all iterations together. This indicates that the frame length variable adds considerable complexity to the optimization problem in the case of pilot rate minimization, and so a simple linear search as we have performed here is much more efficient. This method makes pilot rate minimization feasible for use in practical scenarios.  
Figure 6: Pilot rate minimization with fixed frame length: objective value.  
Figure 7: Pilot rate minimization with fixed frame length: solution time.  
The frame lengths obtained with FRM/1 are shown in Figure 8. Once again, these are generally close to the maximum, but with more variance. This indicates that there may be multiple optimal solutions in some cases, and the iterative approach with its more complete search can find more different solutions than what is chosen by the solver when the frame length is a decision variable. It is not clear if it is better to use a shorter or longer frame length, though a shorter frame length could perhaps be advantageous if a retransmission scheme is used in the case of packet errors. The shorter frame length could then give a shorter time to receive (or fail to receive) an acknowledgment and retransmit an erroneous packet.
# Mobile Robot Path Planning Algorithm Based on NSGA-II
## 3 Smooth Path Planning
### Bezier Curve  
In 1962, French mathematician [PERSON] was the first to study this method of vector drawing curves and provided detailed calculation formulas. Consequently, curves drawn according to such formulas are named after his surname and known as Bezier curves [22].  
The \(n\)th degree parametric curve segment, known as a Bezier curve, is defined as follows, given \(n+1\) spatial vectors \(Q_{i}(i=1,2,\ldots,n)\):  
\[q(t)=\sum_{i=0}^{n}Q_{i}B_{i,n}(t),0\leq t\leq 1, \tag{10}\]  
where \(t\) represents the normalized time variable, \((x_{i},y_{i})\) represents the coordinate vector of the \(i\)th control point \(Q_{i}\), and \(B_{i,n}(t)\) are the Bernstein polynomials, serving as the basis functions in the expression of the Bezier curve.  
\[\begin{split} B_{i,n}(t)&=C_{n}^{i}t^{i}(1-t)^{n-i} \\ &=\frac{n!}{i!(n-i)!}t^{i}(1-t)^{n-i},i=0,1,\ldots,n.\end{split} \tag{11}\]