esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Analysis
In the 100-4 topology, MCD-BP yields a result of 0.9979, while MARA achieves a performance of 1; both outcomes are proximate and can provide fundamental routing protection. The NPC algorithm, U-turn algorithm, and LFC algorithm follows with results of 0.9749, 0.96796, and 0.96599, respectively. Figure 6 illustrate the FPR of various topologies, with node degrees ranging from 2 to 12, under different algorithms. These figures demonstrate that the MCD-BP algorithm maintains a relatively consistent performance in comparison to other algorithms. This is particularly evident in the topologies of 200-8, 200-10, and 200-12, where the FPR of the MCD-BP algorithm reaches 1. However, for the 200-6 topology, the performance of different algorithms varies. Notably, the MARA algorithm displays the lowest FPR of 0.94 in this 200-6 topology, which is less than the 0.994 in the 200-4 topology. Furthermore, the MARA algorithm exhibits an inconsistent trend in the subsequent two topologies. The subsequent Table 3 presents the specific experimental data pertaining to FPR. Figure 4: Experimental results of the failure protection rate. The X-axis delineates the names of the actual topologies, while the Y-axis corresponds to FPR values. A larger FPR value signifies superior performance. This figure illustrates a comparative analysis of the FPR between the MCD-BP and other algorithms such as LFC, NPC, U-turn, and MARA applied to real-world topologies, namely, AttMpls, B2004, Cernet, NJLATA, USLD, and V2008. Figure 3: Experimental results of the failure protection rate. The X-axis delineates the names of the actual topologies, while the Y-axis corresponds to FPR values. A larger FPR value indicates superior performance. This figure compares the FPR performance of MCD-BP algorithm with that of LFC, NPC, U-turn, and MARA algorithms across real-world topologies such as Abilene, Agis, Ans, A19719, A19723, and A19728. ### Path Stretching Degree

The Path Stretching Degree (PSD) refers to the proportional increase in the length of a backup path when compared to the optimal one. This measure directly impacts data transfer latency, making it particularly significant for applications that are sensitive to

Figure 5: Experimental results of the failure protection rate. The X-axis delineates the “node count-node degree” simulated topology, while the Y-axis showcases the FPR values. A larger FPR value signifies superior performance. This figure provides a comparative analysis of the FPR between the MCD-BP and other algorithms such as LFC, NPC, U-turn, and MARA, specifically within simulated topologies with a small number of nodes.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 5 Conclusions  
LPWAN is an emerging technology, which may connect to the IoT and public carrier networks for disaster monitoring and recovery networks. We proposed to use calling party numbers (PAN) to forward location information (and measurements of sensors) of individual sensors to a destination such as a disaster centre (e.g., DRA) in order to be analysed and processed for disaster management. This can be done by minimal changes to carrier networks to allow the PAN to go through the networks.  
Figure 6: An example contour plot of the temperature distribution from the sensor network at time \(T_{2}\), with the effect of wind. The locations of sensors are overlaid on the plot. The direction of disaster propagation for selected sensors is shown by the black arrows. The grey arrows show the direction of disaster propagation from Figure 5 without the effect of wind for comparison.  
Despite the fact that it may require additional configuration in carrier networks, it will be useful in developing countries where there are less sophisticated network technologies. Using the PAN carrying the location and timestamp data of sensors, we showed an application of the idea in a bushfire situation. This is shown by utilising isovalue contours and calculations of matrices for the rate of temperature change at each sensor, as well as the velocities and directions of disaster propagation at each impact zone. The calculations for prediction the spread to further areas of impact in time were suggested. It can potentially be useful in remote areas utilising LPWAN for several hundred kilometres of coverage through a single gateway. For future study, a verification of this project by real tests should be conducted. In addition, more comprehensive disaster scenarios reflecting variables such as wind speed and direction, humidity, density of flora and temperature may be developed and modelled to help real disaster situations.  
[PERSON] conceived and designed the solution and application, and wrote the paper. [PERSON] reviewed and provided guidance.  
The authors declare no conflict of interest.
## 2 Basic Elements
### Inverse and Ill-Posed Problems  
On the one hand, inverse problems entail finding an unknown property of an object or medium from observations of its responses to test signals. Forward problems, on the other hand, present information about the causes that describe a process in a medium, and the solution to the problem leads to the discovery of the effect produced by such causes. Therefore, in contrast to forward problems, inverse problems provide partial information about the results or effects produced in the medium by some unknown causes that must be found by analyzing these results. Thus, forward problems are cause-effect problems, while inverse problems are effect-cause problems [4].  
Source identification problems are widely investigated in many research fields, and they are modeled as boundary value problems for which both the associated forward problem and its corresponding inverse problem must be considered. The inverse problem involves determining the source that produced measurements on the boundary of a region. It appears in applications such as inverse electroencephalography, inverse electrocardiography, and inverse geophysics, for which problems are modeled using partial differential equations.  
An operational equation of the form \(Ax=y\), where \(A:X\longrightarrow Y\) and \(X\) and \(Y\) are Banach or Hilbert spaces, is well-posed if it satisfies the following conditions [4]:  
1. For each \(y\in Y\), a solution to the problem exists.
2. For each \(y\in Y\), the solution to the problem is unique.
3. The solution \(x\) to the problem continuously depends on initial data \(y\).  
Problems that violate one or more of these conditions are referred to as ill-posed problems. Condition 3 is associated with numerical instability, i.e., small errors on the right side of the operational equation can result in significant changes to the location of the solution \(x\). To address this numerical instability, regularization methods must be applied. In this work, we applied the Tikhonov regularization method, which involves choosing a regularization parameter in terms of the error [4; 5].
## 5 Microbiome Influence on Atopic Dermatitis
### Microbial Colonization  
Profound changes in the skin microbiota occur in some patients with AD, and the pathogenic importance of microbial organisms is recognized [128]. The diversity of the microbiome decreases in inflamed atopic skin, with a reduction in the genera _Streptococcus_, _Corynebacterium_, and _Cutibacterium_, as well as the phylum Proteobacteria, in favor of an increase in the genus _Staphylococcus_, particularly _S. aureus_[129].  
In AD, there is a notable loss of strictly anaerobic bacteria, altering the skin's microbiome from anaerobic to aerobic metabolism. Healthy skin is normally devoid of oxygen, but dry and scaly skin with compromised epidermal barrier function can increase oxygenation and decrease the abundance of strictly anaerobic bacteria such as _Lactobacillus  
Figure 2: Mechanisms of skin microbiota’s influence on AD pathogenesis.  
spp. or _Finegoldia_ spp. In the absence of oxygen, skin bacteria metabolize organic substances through fermentation, such as the amino acid serine derived from the breakdown of filagrine, producing lactic acid, propionic acid, and other short-chain fatty acids. These metabolic products reduce the skin's pH to below 5.5, preserving the skin's protective acidic environment. Additionally, Gram-positive anaerobic coccu such as _Finegoldia_, _Anaroococcus_, and _Peptoniphilus_ stimulate a rapid antimicrobial peptide (AMP) response in human keratinocytes, which could be an important signaling mechanism for keratinocytes when the skin is damaged. When these organisms are partially or completely absent, the signaling mechanisms in keratinocytes and other protective functions may be compromised, increasing the likelihood of _S. aureus_ colonization [130].  
Colonization by _S. aureus_ on AD skin has been directly linked to disease severity, but, as mentioned earlier, the role of other constituents within the skin's bacterial community may be just as significant [115]. Dysbiosis related to AD is often characterized by colonization by _S. aureus_ and the simultaneous loss of other potentially beneficial species, although the loss of anaerbes in AD does not seem to be driven by the presence of _S. aureus_[130].  
The skin of AD harbors a microbial growth environment that is very different from that of normal skin, and this may be crucial in explaining the dysbiosis observed in AD. A dysfunctional physical barrier of the skin leads to an increased pH on the skin surface, which favors the growth of _S. aureus_[115]. Besides the frequent presence of _S. aureus_ on the skin of people with AD, there are other factors that strengthen the idea that the microbiota has a significant impact on the development of the disease [58].
###### Abstract  
With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push-pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs.  
autonomous electric vehicles; VANETs; path-planning; charging optimization +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Dynamic Path-Planning and Charging Optimization for  
Autonomous Electric Vehicles in Transportation Networks. _Appl. Sci._**2023**, _13_, 5476. [[https://dot.org/10.3390/app13095476](https://dot.org/10.3390/app13095476)]([https://dot.org/10.3390/app13095476](https://dot.org/10.3390/app13095476))
###### Abstract  
Conventional black box machine learning (ML) algorithms for gas-phase species identification from THz frequency region absorption spectra have been reported in the literature. While the robust classification performance of such ML models is promising, the black box nature of these ML tools limits their interpretability and acceptance in application. Here, a one-dimensional convolutional neural network (CNN), VOC-Net, is developed and demonstrated for the classification of absorption spectra for volatile organic compounds (VOCs) in the THz frequency range, specifically from 220 to 330 GHz where prior experimental data is available. VOC-Net is trained and validated against simulated spectra, and also demonstrated and tested against experimental spectra. The performance of VOC-Net is examined by the consideration of confusion matrices and receiver-operator-characteristic (ROC) curves. The model is shown to be 99+% accurate for the classification of simulated spectra and 97% accurate for the classification of noisy experimental spectra. The model's internal logic is examined using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, which provides a visual and interpretable explanation of the model's decision making process with respect to the important distinguishing spectral features.  
Keywords:VOC; DNN; CNN; classification; THz; spectroscopy; rotational; microelectronic; spectrometer; gas sensing +
Footnote †: journal: Applied sciences
## 3 Results
Figure 7: Comparison of learning curves for Light Gradient Boosting Machine classifier on the same EEG epoched dataset, and (**a**) with a patient-aware 10-fold cross validation as described in approach [B2], (**b**) with a Stratified 10-fold cross validation as described in approach [B3]. The y-axis represents accuracy score. In figure (**a**), the learning curve shows high performance on the training set (in blue) but significantly lower performance on the training set where accuracy is between 40% and 50% (green curve), with a large gap between the two curves. This indicates overfitting on the training dataset and poor generalization on unseen data. In figure (**b**), the learning curve shows accuracy over 90% on the training set (in blue) and accuracy over 80% on the training set (in green). This indicates that the model generalizes to unseen data. Figure 8: ROC Curves for LGBM Classifier and [B3] validation approach. Class 0 refers to CNT, class 1 to EPI, and class 2 to PNES. Figure 9: Precision-Recall curve and [B3] validation approach. In general, the results show a good predictive power of the last discussed model, and the EPI class demonstrated the highest level of discrimination, with an AUC of 98% and an error rate of 11%, see Figures 8-11. However, more comprehensive analyses are required to delve deeper into the comparison of different validation approaches across multiple EEG datasets. These analyses would help investigate the underlying factors contributing to the significant difference in performance observed between validation approaches [B2] and [B3]. To gain a better understanding of the observed performance disparities, further investigations should be conducted on a larger and more diverse set of EEG datasets. This would  
Figure 11: Class prediction errors for LGBM classifier and [B3] validation approach. Figure 10: The ten most important features w.r.t. LGBM and [B3] validation approach. enable researchers to explore potential sources of variation, such as differences in data characteristics, recording protocols, or patient populations. By examining the performance across multiple datasets, it would be possible to assess the generalization capabilities of the model and determine whether the observed discrepancies are consistent or dataset-specific.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 4 Simulation  
The harmonic structure is one of the most important audio features, which has a significant impact on both human hearing and ASR systems [22; 33; 34; 35]. The harmonic distortion of a DUT can distort the original harmonic structure of the input signal, and cause performance degradation by affecting the balance of Mel-frequency cepstrum coefficient (MFCC) [36; 37; 38]. Then, the noise of the DUT will flood the harmonic structural features to some extent, making it more difficult to be extracted [39]. Therefore, the SNR, SINAD, THD and ADDR performance of the DUTs indirectly affect the accuracy of ASR. In other words, the word error rate (WER) of ASR is low when the SNR, SINAD, THD and ADDR of the DUTs are high. In our simulation, an ASR system is introduced to verify the proposed ADDR index, and its supplementary effect on the existing indexes is demonstrated by the WER of ASR in two situations: (1) Performance comparison of DUTs with the same SNR, SINAD, THD and SFDR indexes; (2) Performance comparison of DUTs with a minor variation of SNR, SINAD, THD and SFDR.  
The block diagram of the verification experiment is illustrated in Figure 5. The experiment includes an utterance generation module, a simulated DUT and a Kaldi ASR system, which corresponding to the part 'a', 'b' and 'c' in Figure 5 respectively. The utterance generation module attenuates the white noise, babble noise and factory1 noise from the NOISEX-92 noise library by an attenuator according to the specified SNR, and then superimposes them on the clean speech, respectively [40]. The clean speeches are 10 short (each is about 10 s, all are 16 kHz sampling frequency and 16-bit sampling resolution) segments from a male. The simulated DUT distorts the input speeches according to the corresponding frequency response. For clarity of contrast, the simulated DUT ensures that the DUTs have harmonic distortions of the same relative magnitude at any non-zero input signal frequency. The phases of distorted spectral components are the same as the fundamental spectral components. Each output frequency of the simulated DUT is equal to the summation of the input signal and the distorted spectral components at this frequency. The ASR system consists of an open-source ASR toolkit Kaldi [41] and a Mandarin TDNN chain model CVTE trained on commercial data.
## 2 Heat-to-Power Applications
### Supercritical Organic Rankine Cycles
#### 2.2.2 Experimental Results  
The experimental work on small-scale supercritical ORC, although limited, has provided some important results concerning the cycle's efficiency and power production in transcritical conditions.  
Several tests conducted by [PERSON] et al. [35] in the laboratory have shown a cycle efficiency of 4.4% at supercritical conditions, when in subcritical operation it reached a value of 7%. However, supercritical operation was difficult to achieve and only when the cooling water flow rate was decreased, could the engine operate and maintain such conditions. At these conditions, the expander frequency/speed was low, keeping a pressure ratio close to the designed one, but leading to a low expansion efficiency. Nevertheless, the real test data and their study provide good arguments whether a supercritical ORC can be indeed more efficient than a subcritical cycle, and if the theoretical results can be verified. The thermal efficiency results in the field of CPV-T system showed that the ORC operated only in subcritical conditions. The most important conclusions from the laboratory tests were that such ORC engine with a capacity of just 3 kW can reach an adequate thermal efficiency, when operating at very low temperature.  
In [PERSON] et al. [37] tests, the ORC scroll expander was able to produce up to 6 kWe gross, with supercritical R-134a supply and has shown good performance, however, the ORC engine performance was insufficient, with a net thermal efficiency of only 1%. Nevertheless, it showed a good potential for waste heat recovery application since it performed better than \(\
icefrac{{2}}{{3}}\) of the same power scale ORC for gross exergetic recovery efficiency.  
[PERSON] et al. [38] experimental results showed that their attempt in using a screw expander in a transcritical ORC was successful, since it converted low-grade heat into approximately 20 kW of power. The experimental results also demonstrated a net thermal efficiency (gross thermal efficiency) of 2.75% (5.7%), 2.66% (5.38%), and 2.63% (5.28%) for heat source temperatures of 90 \({}^{\circ}\)C, 95 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C respectively.  
[PERSON] et al. [39] have proven the feasibility of thermally driven heat pump based on small-scale turbomachinery demonstrated and the turbine used reached an efficiency of 0.75 as well as the compressor. However, the exergy losses between the turbine and the compressor reached \(\sim\)30%. In Table 2, a summary of the aforementioned experimental prototypes is presented.  
The main layouts of the supercritical ORC engine, as described in the aforementioned prototypes.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 4 Results
### Multiple Target Tracking
#### 4.3.2 Tracking Results of Video 2  
In Video 2, 23 targets appear for 214 frames. Many redundant tracks are caused by the multiple measurements on the same target without track association. A total of 56 valid tracks are reduced to 26 with the proposed method. The tracking efficiency increased from 41% to 92% because two segmented tracks were generated from one target. Figure 20a,b show the tracking results without and with track association, respectively, in the expanded frame. Two supplementary multimedia files (MP4 format) for tracking vehicles are available online. One is target tracking without track association (Supplementary Material Video S3) and the other is using the proposed method (Supplementary Material Video S4). The symbols and the numbers in the MP4 files are displayed the same way as in Video 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Target 2** & **Target 4** & **Avg.** \\ \hline Position & Previous & 2.88 & 2.34 & 2.61 \\ (m) & Fused State only & 0.85 & 0.49 & 0.67 \\ Velocity & Previous & 1.06 & 1.15 & 1.11 \\ (m/s) & Fused State only & 2.33 & 1.21 & 1.77 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Position and velocity RMSE of Fused States.  
Figure 19: Velocity RMSE of (**a**) Target 1; (**b**) Target 2; (**c**) Target 3; (**d**) Target 4; (**e**) Target 5; (**f**) Target 6; (**g**) Target 7; (**h**) Target 8; (**i**) Target 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **Target 1** & **Target 2** & **Target 3** & **Target 4** & **Target 5** & **Target 6** & **Target 7** & **Target 8** & **Target 9** & **Avg.** \\ \hline Position & Previous & 6.01 & 2.88 & 0.62 & 2.34 & 0.58 & 2.34 & 6.29 & 0.84 & 1.13 & 2.56 \\ (m) & Proposed & 5.89 & 2.41 & 1.64 & 1.56 & 1.89 & 4.61 & 1.95 & 4.41 & 2.20 \\ Velocity & Previous & 2.32 & 1.06 & 0.86 & 0.12 & 1.56 & 1.89 & 4.61 & 1.95 & 4.41 & 3.19 \\ (m/s) & Proposed & 2.45 & 4.82 & 0.86 & 0.12 & 1.56 & 1.89 & 4.61 & 1.95 & 4.41 & 3.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Position and velocity RMSE of Video 1.