esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 1 Introduction
### Our Contribution  
Although machine learning has been applied to learn desirable characteristics of a given set of tree decompositions [40] (with a view to speeding up dynamic programming algorithms that then run over the tree decompositions), algorithm selection has not been applied to the computation of treewidth itself. This paper sets out to fill that gap by demonstrating a working algorithm selection framework for the problem. Specifically, we use standard, \"out of the box\" techniques from machine learning to train a classifier which subsequently decides, on a graph by graph basis, which out of a set of state-of-the-art exact treewidth solvers will terminate fastest. The exact treewidth algorithms were all submissions to Track A of the aforementioned PACE 2017 tournament. The resulting hybrid algorithm significantly outperforms any single one of the algorithms in isolation. We also investigate the _features_ (i.e., measurable characteristics) of problem instances and solvers that make some (graph, algorithm) pairings better than others. The data used to train the classifier has been made publicly available at github.com/bslavchev/as-treewidth-exact. We feel that the combination of treewidth and the machine learning approach to algorithm selection is particularly timely, given that they both fit into the wider movement towards fine-grained, multivariate analysis of running-time complexity. In order to keep the exposition compact and accessible for a wider audience we have deliberately chosen for a stripped-down machine learning framework in which we only use a generic, easy-to-compute set of features and a small number of pre-selected classifiers. As we explain in the section on future work, more features, classifiers and analytical techniques can certainly be explored in the future. However, the take-away message for this article is that machine learning-driven algorithm selection can already be effective without having to resort to highly sophisticated machine learning frameworks.  
The structure of this paper is as follows: Section 2 will formally define treewidth and the algorithm selection problem. Section 3 will describe the graph features, treewidth solvers and machine learning models that we used. Section 4 will present the datasets used, the experiments conducted and their results. Section 5 will further analyse the experiment results, while Section 6 contains a discussion of the insights that the research has yielded. Finally, Section 7 reflects on possible future work and the trend towards the incorporation of machine learning techniques within combinatorial optimisation.
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 6 Conclusions  
In this paper, we have proposed a new change-point detection algorithm, named CE.AR. The algorithm is able to detect change-points in piecewise AR(\(p\)) sequences, allowing variations in all parameters of the model. Our method involves two steps. Firstly, we build the MDL-based objective function to estimate the correct number of AR(\(p\)) segments. Then, we apply the Cross-Entropy algorithm for solving the combinatorial optimization problem. The numerical results show that, when the mean and the autoregression coefficient change moderately or strongly, the CE.AR method is very efficient at estimating multiple change-points. The Cross-Entropy algorithm also shows superior performance in detecting the locations of change-points. These two features make the proposed algorithm very useful from a practical point of view. We have demonstrated its usefulness by applying it to the latest real data set on the AUD/CNY exchange rate. The model considered here assumes an AR process; generalization to an ARIMA model is a matter for our further research. A limitation of the method comes from the under-segmentation when there is a small change in the mean or the autocorrelation coefficient. The improvement of detection accuracy for this problem needs to be taken into consideration in future research. In addition, it may be worth examining the impact of the lengths of the segments on the performance of the algorithm. Lastly, the question of whether our method would be helpful to improve the forecast's accuracy is also a topic for our future research.  
**Author Contributions:** Methodology, G.S.; software, L.M.; validation, L.M.; formal analysis, L.M.; investigation, L.M.; resources, G.S.; data curation, L.M.; writing--original draft preparation, L.M. and G.S.; writing--review and editing, G.S.; visualization, L.M.; supervision, G.S.; project administration, G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
Figure 7: The orange dotted line in the plot represents estimated change-points using CE.AR, while the blue line represents the change-points detected by the strucchange method. In (**a**), we show the comparison of two methods on the daily exchange rate of AUD/CNY. (**b**) The change-point detection resulted by applying both methods on the first difference of AUD/CNY daily exchange rate. The data were downloaded from [[https://www.rba.gov.au/statistics/historical-data.html](https://www.rba.gov.au/statistics/historical-data.html)]([https://www.rba.gov.au/statistics/historical-data.html](https://www.rba.gov.au/statistics/historical-data.html)).  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank [PERSON] for reviewing the original manuscript and providing useful suggestions on programming.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 5 Social Impact Assessment of Woody Biomass Utilization
### Issues with Wood Biomass Utilization  
The issues related to the use of woody biomass for power generation and heating in Japan are presented in Table 3. Four issues concerning power generation were identified: high cost, securing feedstock supply, forest destruction, and a lack of technicians. The cost issue presents two primary challenges: dependence on FIT, where woody biomass power plants can sell electricity at a fixed price higher than the market price, and the lack of economic efficiency in small-scale power plants (further discussed in Section 6). Ensuring the supply of feedstock and the destruction of forests are interconnected and tend to create a vicious cycle. Since the introduction of the FIT in 2012, the number of woody biomass power plants in Japan has increased rapidly. However, the Japanese domestic wood market, which has been impacted for a long time by low-priced wood from overseas, has been in a prolonged slump, making a stable supply of feedstock a pressing issue. Simultaneously, supply scarcity drives imports and exacerbates illegal logging in both Japan and other exporting countries, perpetuating a vicious cycle. Finally, a lack of technicians is an urgent issue for small-scale power generation.  
The issues associated with heat utilization are similar to those associated with power generation, with a few distinctions. Regarding the cost issue, the initial cost of heat utilization is notably high and relies heavily on government subsidies. However, these subsidies do not cover the entire heat utilization supply chain and only address the initial costs. Consequently, there is an intense competition for fossil fuels during the operational phase. In terms of ensuring the supply of feedstock, the challenge lies more in the shortage of suppliers rather than the feedstock itself. The predominant heat utilization of woody biomass in Japan is low, resulting in dispersed locations that lack centralized utilization. This dispersion also contributes to a scarcity of suppliers and technicians. Finally, the most crucial issue for heat utilization is ensuring the heat demand. Given the high efficiency of heat utilization, the heat produced by large-scale utilization must be consumed over a wide area. However, district heating systems (DHSs) are scarce in Japan. Additionally, the high installation costs of these systems have hindered the widespread adoption of wood biomass for heat utilization.
# FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
## 3 Proposed Algorithm
Each client \(i\) updates its local model parameter \(\theta_{i}\) based on its data \(D_{i}\) and the global model parameter \(\theta_{G}\) as  
\[\theta_{i}^{(t+1)}=\theta_{i}^{(t)}-\eta\
abla_{\theta_{i}}\mathcal{L}(D_{i}, \theta_{i}^{(t)}), \tag{1}\]  
where \(\eta\) is the learning rate, \(\
abla\)L is the gradient of the loss function \(L\), and \(t=1,2,\ldots,T\) denotes the number of rounds. The central server updates the global parameter \(\theta_{G}\) based on all clients' local parameter updates as  
\[\theta_{G}^{(t+1)}=\sum_{i=1}^{N}\frac{|D_{i}|}{|D|}\theta_{i}^{(t+1)}, \tag{2}\]  
where \(|D_{i}|\) is the number of data points for client \(i\), and \(|D|\) is the total number of data points. For representation learning, the model parameters are divided into two components \(\theta_{i}=(\theta_{i,b},\theta_{i,b})\). Here, \(\theta_{i,b}\) represents the base parameter shared among all clients, and \(\theta_{i,b}\) represents the head parameter of the \(i\)-th client. The model parameters from Equation (1) are modified as follows:  
\[\theta_{i}^{(t+1)}=\theta_{i}^{(t)}-\eta\
abla_{\theta_{i}}\mathcal{L}(D_{i}, \theta_{i,b}^{(t)},\theta_{i,b}^{(0)}). \tag{3}\]  
Excluding FedBABU [20], previous works stop the gradient of the head parameter during training and during the aggregation phase. The global model is updated using both the base and head parameters. However, FedBABU and our scheduling algorithm perform both training and aggregation using only the base without involving the head. Only after training are a few rounds of fine-tuning included using both the base and head. The aggregation process, which excludes the use of the head, proceeds as follows:  
\[\theta_{G}^{(t+1)}=\sum_{i=1}^{N}\frac{|D_{i}|}{|D|}\theta_{i,b}^{(t+1)}. \tag{4}\]  
Our FedSeq algorithm follows the same training setup as FedBABU [20], wherein the head layer is not trained or aggregated during training; instead, it is fine-tuned for each client after training completion. In FedBABU, the training round sets the learning rate of the head to zero, allowing gradient computation but preventing its application. In contrast, our scheduling algorithm ensures complete decoupling by freezing gradients in both the head and base parts, preventing both computation and application. \begin{table}
\begin{tabular}{c l} \hline
**Symbol** & **Description** \\ \hline \(N\) & Total number of clients \\ \(C_{i}\) & Client \(i\in\{1,2,\ldots,N\}\) \\ \(|D_{i}|\) & Number of data points for client \(i\) \\ \(|D|\) & Number of data points \\ \(T\) & Number of global rounds \\ \(F\) & Number of fine-tuning rounds \\ \(K\) & Number of base layers \\ \(t_{k}\) & Unfreeze point (round) of \(k\)-th layer \\ \(\eta\) & Learning rate \\ \(\mathcal{L}\) & Loss function \\ \(\theta_{i}\) & Local parameter for client \(i\) \\ \(\theta_{G}\) & Global model parameter \\ \(\theta_{i,b}\) & Base parameter shared among all clients \\ \(\theta_{i,b}\) & Head parameter of client \(i\) \\ \hline \end{tabular}
\end{table} 
Table 3: List of Symbols Used.
## 4 Simulation and Discussion
This is because in this circumstance, the symbol, which is picked up from the rotated constellation, makes the destination more difficult to discern the truth. But, when EB/N0 is higher, the higher diversity order makes the proposed scheme achieve the best performance. These conclusions also coincide with the results provided by [10] for the rotated QSTBC at a scale factor since both of the proposed scheme and the ones in [10] are endowed with maximum transmission rate and full diversity though the scenario for them to operate is far from each other. Moreover, according to \(d_{gain}=\lim\limits_{SNR\rightarrow\infty}-\dfrac{\log P_{e}(SNR)}{\log( SNR)}\) where \(P_{\text{e}}\) is the bit error rate [11] and SNR is signal-to-noise ratio, we can discover that the diversity gain of the proposed scheme approximates diversity order 4 as the SNR increases. Again, it demonstrates that the proposed scheme has the highest diversity gain. Its performance is superior to all other schemes considered in this context. Figure 3: **(a)** The SERs of four cooperative schemes versus Eb/N0, with the source using BPSK, the rotation angle of which for the RQSTBC scheme is 1/2\(\pi\) (left). **(b)** The SERs of four cooperative schemes versus Eb/N0, with the source using 4 QAM, the rotation angle of which for the RQSTBC scheme is 1/4\(\pi\) (right).
## 4 Results and Discussion
### Prediction Model for Geophysical Logging Response of Coal Quality
#### 4.4.2 Validation of Coal Quality Logging Response Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**DHN** & **NCS** & \(N_{\rm{\it ps}}\) & \(N_{\rm{\it DEN}}\) & \(N_{\rm{\it GR}}\) & \(\mathbf{M_{\rm{\it ad}\text{-}PV}}\) (\%) & \(\mathbf{M_{\rm{\it ad}\text{-}MV}}\) (\%) & \(\mathbf{AE\) (\%)} & \(\mathbf{RE\) (\%)} \\ \hline KZ-2 & 8 & 0.69 & 0.26 & 0.14 & 1.82 & 1.78 & 0.04 & 2.25 \\ KZ-5 & 8 & 0.52 & 0.58 & 0.44 & 1.79 & 1.36 & 0.43 & 31.62 \\ KZ-8 & 7 & 0.51 & 0.52 & 0.10 & 1.8 & 0.28 & \(-0.48\) & \(-21.05\) \\ XZ-1 & 8 & 0.30 & 0.85 & 0.42 & 1.78 & 1.31 & 0.47 & 35.88 \\ XZ-3 & 8 & 0.03 & 0.82 & 0.72 & 1.79 & 0.22 & \(-0.43\) & \(-19.37\) \\ XZ-7 & 7 & 0.67 & 0.98 & 1.00 & 1.79 & 1.64 & 0.15 & 9.15 \\ \hline \end{tabular}
\end{table} 
Table 3: Error analysis of predicted moisture content in coal. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**DHN** & **NCS** & \(N_{\rm{\it LLD}}\) & \(N_{\rm{\it GR}}\) & \(\mathbf{N_{\it AC}}\) & \(\mathbf{V_{\rm{\it dd}\text{-}PV}}\) (\%) & \(\mathbf{V_{\rm{\it dd}\text{-}MV}}\) (\%) & \(\mathbf{AE\) (\%)} & \(\mathbf{RE\) (\%)} \\ \hline KZ-1 & 8 & 0.32 & 0.40 &Analysis of absolute and relative errors confirms the neural network model's accuracy, with minimal deviations supporting its utility for precise coal quality stratification and intelligent mining operations.
# Mapping the Most Susceptible Regions to Fire in Portugal
## 3 Results
### Susceptibility of 2022 Burned Areas  
The PC\({}_{\rm{REC}}\) signal distribution, as well as the land cover distribution, over the top 5 largest burned areas of 2022 in Portugal (see Figure S3) are represented in Figure 8. The A1 fire occurred in the National Park of Serra da Estrela, where the highest mountain of the mainland country is located, and it was the largest fire of 2022, burning around 25,000 ha [55].  
Figure 8: Spatial distribution of the PC\({}_{\rm{REC}}\) (**top panels**) and land cover distribution (**middle panels**) over the top 5 largest burned areas of the fire season of 2022 (A1–A5); boxplots of the distribution of the PC\({}_{\rm{REC}}\) signal over these areas and also over the rest of burned areas of 2022 (**bottom panels**). The whiskers of the boxplots represent the 1 st and 99 th percentiles. The circles represent the outliers.  
Figure 7: Distribution of the land cover for each class of susceptibility.  
According to the results, a large extension of A1 revealed high fire susceptibility, especially in the southern part of the fire scar (Figure 8, top panels). The corresponding box plot shows a large range in PC\({}_{\text{REC}}\) values; despite a median value of PC\({}_{\text{REC}}\)-3, the 99 th percentile (top whisker) reaches about PC\({}_{\text{REC}}\)\(\sim\)15, as there are also outliers above this value, which indicate the presence of extreme susceptibility at several points. The map of the landcover shows that the burned area of A1 is mainly covered by shrublands and forests, composed of _Pi_ and _HW_.  
The A3 results clearly show how the area was susceptible to burn. Mainly covered by shrublands, a large part of the north-western sector of the fire scar presents a PC\({}_{\text{REC}}\) signal above 10, and the other sectors had PC\({}_{\text{REC}}\) values falling in 'High' and 'Very High' susceptibility classes. Moreover, the median value falls in the 'Extreme' class, being PC\({}_{\text{REC}}\)\(\sim\)7.5.  
The signal of the PC\({}_{\text{REC}}\) is quite strong in A4 and A5, mainly falling in 'High' and 'Very High'. Regarding the particular case of A2, despite the large burned area, there were many points classified as 'Low' and 'Moderate'.  
Concerning the fires out of the top 5, the boxplots show that the distribution of the signal of PC\({}_{\text{REC}}\) in these areas is similar to the five major events, as the mean value is about 4.5, which falls in the 'High' category, and the highest percentiles, as well as the outliers, can be found in 'Extreme'.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 3 Proposed Model
### Overlap Avoidance for Random Markov Process Mobility Model  
#### 3.3.1 Random Markov Process Mobility Model  
This model is based on the Markov process which is a probability process where the future state depends on the present state regardless of the state of the past [25]. The Random Markov Process model operates according to the probability table as shown in Table 2[12]. For example, if the current direction of a UAV is straight ahead, the probability of keeping straight ahead is 80% while it turns either left or right with the probability of 10% each. On the contrary, if the current direction is turning right, the probability of keeping right-turn is given by 70%. A UAV determines its direction in every other second.  
In this model, it is difficult to obtain high coverage when UAVs reconnoiter the scanning area owing to insufficient search in some areas. As UAVs fly with dynamic motion, they often tend to hover a point. Hence, it takes a long time in acquiring high coverage.
## 6 Evaluation Results
For a given BER \(=10^{-3}\) (third data point), BPSK-16 WOFDM and BPSK-16 FOFDM have a spectral efficiency of 27 bit/sec/Hz at \(\frac{E_{b}}{N_{o}}=46\ dB\), 16 WOFDM and 16 FOFDM have a spectral efficiency of 30.7 bit/sec/Hz at \(\frac{E_{b}}{N_{o}}=51\ dB\), and 16-QAM has a spectral efficiency of 45.5 bit/sec/Hz at \(\frac{E_{b}}{N_{o}}=66\ dB\). The energy efficiency (EE) _versus_ spectral efficiency (SE) graph for different modulation techniques is shown in Figure 9. The four data points on each curve are obtained by setting the BER values to \(10^{-1}\), \(10^{-2}\), \(10^{-3}\) and \(10^{-4}\). From the graph, it can be observed that there is a trade-off between EE and SE, where an increase in SE due to higher \(\frac{E_{b}}{N_{o}}\) decreases the EE. For a given SE value, it is observed that BPSK-16 WOFDM is the most energy efficient technique. In comparison to SISO systems, MIMO systems are more spectrally efficient due to effective bandwidth utilization [36]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Modulation Type**} & \multirow{2}{*}{\(T_{tr\_MIMO}\) **s/bit**} & \multicolumn{2}{c}{\(T_{pc\_MIMO}\) **s/bit**} & \multicolumn{2}{c}{\(T_{pc\_MIMO}\) **s/bit**} & \multicolumn{2}{c}{\(T_{V-MIMO}\) **s/bit**} \\ \cline{3-10}  & & \(d^{L}=10m\ d^{L}=100m\) & \(T_{Mod}\) & \(T_{Dmod}\) & \(T_{Det}\) & \(T_{pc}\) & \(d^{L}=10m\ d^{L}=100m\) \\ \hline
16-DQPSK & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.069230 & 0.06286 & 0.0875 & 0.21959 & 0.21959 & 0.21959 \\
16-QAM & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.076925 & 0.06537 & 0.0875 & 0.2298 & 0.2298 & 0.2298 \\
16-OQPSK & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.083717 & 0.07948 & 0.0875 & 0.25070 & 0.25070 & 0.25070 \\
16-FOFDM & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.102575 & 0.08717 & 0.0875 & 0.27725 & 0.27725 & 0.27725 \\
16-WOFDM & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.0800 & 0.06922 & 0.0875 & 0.236725 & 0.236727 & 0.236727 \\ BPSK-16 FOFDM & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.10625 & 0.08987 & 0.0875 & 0.28360 & 0.28360 & 0.28360 \\ BPSK-16 WOFDM & 1.75 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.084075 & 0.07682 & 0.0875 & 0.24840 & 0.24840 & 0.24840 \\ \hline \multirow{2}{*}{SISO-BPSK-16 WOFDM} & \multirow{2}{*}{\(T_{tr\_SISO}\) s/bit} & \multicolumn{2}{c}{\(T_{pr\_SISO}\) s/bit} & \multicolumn{2}{c}{\(T_{pc\_SISO}\) s/bit} \\ \cline{3-10}  & & \(d=10m\ d=100m\) & \(T_{Mod}\) & \(T_{Dmod}\) & \(T_{pc\_SISO}\) & \(d=10m\ d=100m\) \\ \cline{1-1} \cline{2-10}  & 1 \(\times 10^{-6}\) & 3.33 \(\times 10^{-8}\) & 3.33 \(\times 10^{-7}\) & 0.084075 & 0.07682 & 0.160895 & 0.160895 & 0.160895 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time Delay.
## 4 Simulation and Results Discussion  
### Simulation Study  
Simulations were carried out in the MATLAB/Simulink environment to verify the effectiveness of the proposed multi-objective sequencing MPTC. The parameters in Table 1 were used for the simulation tests.  
Figure 6: Execution steps of the improved MPTC.  
The simulation settings are as follows. The motor accelerates from rest to 100 r/min within 0-1.2 s. The load torque of 2 Nm is added at 0.8 s, and the speed increases to 200 r/min at 1.2 s. The load is removed at 1.6 s, and the speed decreases to 50 r/min from 2.4 s onwards. A speed reference and a rated load are added to the system to show the overall control behavior of the system. The goal is the evaluation of the overall control behavior. The stator's reference magnetic flux is maintained at 0.94 Wb, which is slightly lower than the rated value to avoid magnetic saturation. Figures 7 and 8 present the simulation results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Rated voltage [V] & 24 \\ Phase resistance [m\(\Omega\)] & 8 \\ Inductance (d-axis) [mH] & 4.0 \\ Inductance (q-axis) [mH] & 4.0 \\ Stator magnetic-flux linkage [mWb] & 26.0812 \\ Pole pairs & 2 \\ Maximum bus voltage [V] & 36 \\ Minimum busbar voltage [V] & 10 \\ Maximum current [\(\Delta\)] & 10 \\ Control step [\(\mu\)s] & 50-100 \\ Friction constant [kgm/s\({}^{2}\)] & \(1.7\times 10^{-3}\) \\ Inertia constant [kgm\({}^{2}\)] & \(2\times 10^{-3}\) \\ Speed-loop response time [ms] & 2 \\ Converter type & 2-level VSI \\ Electrical machine type & IPM-SM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the permanent-magnet synchronous motor.  
Figure 7: Dynamic response of the PMSM under the MPC: (**a**) Variation curve of rotational speed. _Nr_; (**b**) Variation curve of electromagnetic torque _Te_; (**c**) Variation curve of stator’s magnetic flux linkage \(\psi\)s.  
Firstly, the system has significantly improved steady-state performance. Secondly, the output torque can accurately track the load torque _TL_ with fewer fluctuations than the conventional MPC. Fluctuations in the steady state also disappear, and the amplitude of the stator flux has similar characteristics to the torque-tracking curve.  
Since the speed control is carried out by a PI controller, the speed exhibits a smooth response in terms of dynamic performance. It has a short rise time during acceleration and deceleration, and the overshoot of the motor speed is minimal. When the load is suddenly applied and removed, the speed fluctuates slightly. However, it can be stabilized at the set speed soon, indicating that the robustness of the system is high. The new proposed strategy has a faster dynamic response to torque.  
The method proposed in the work has lower switching times, and the switching frequency is reduced by an average of 41.2% compared to the conventional MPC control (Figure 9). However, it is still higher than PMSM with the DTC.