esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 5 Discussion and Limitations
However, attention-based approaches present their own challenges: (1) they would require architectural modifications to incorporate attention mechanisms in both teacher and student models, potentially limiting the method's generality; (2) the relationship between attention patterns and concept learning is less understood compared to saliency maps' relationship with feature importance; and (3) attention maps might capture different aspects of visual concepts than saliency maps, possibly leading to complementary rather than superior results. Future work could explore a hybrid approach that leverages both attention and saliency information for more robust visual concept transfer. **Extension to other modalities. ** While our current work focuses on visual concept knowledge distillation, recent work has demonstrated the potential of extending concept-based models to other modalities, particularly text data. For instance, Text Bottleneck Models (TBMs) [48] have shown promising results by automatically discovering and measuring text concepts using large language models without human curation. However, adapting VICO-KD to text data presents unique challenges that differ from visual domains: (1) text concepts are often more abstract and contextual compared to visual attributes (e.g., \"customer satisfaction\" vs. \"red color\"); (2) the mechanism for concept measurement would need to be fundamentally different since saliency maps are specific to visual data; and (3) maintaining interpretability with sequential data structures poses additional challenges. Future research could explore bridging these gaps by developing text-specific concept transfer mechanisms, perhaps leveraging techniques like attention visualization or embedding space analysis while preserving the core principle of concept-based knowledge transfer.
## 2 Materials and Methods
### Research Methods
#### 2.3.2 Trend Detection  
Temporal trends of each product are calculated by employing Theil-Sen's slope estimator with time as the independent variable and remote sensing data as the dependent variable. This method is insensitive to outliers, so it is more robust, theoretically. We used the modified Z-score to standardize the raw data before calculated the trend. The formula of [PERSON]'s slope estimator is as follows:  
\[\beta=Median(\frac{X_{j}-X_{i}}{j-i})\quad 2000\leq i\leq j\leq 2018 \tag{4}\]  
where _Median_ is a statistic function; \(X_{j}\) and \(X_{i}\) are the biophysical data, and \(\beta\) indicates the indicates the trends for each pixel. The determination of the significance level is supplemented by the Mann-Kendall test. The algorithms are as follows:  
\[S=\sum_{j=1}^{n-1}\sum_{i=j+1}^{n}sgn(X_{j}-X_{i}) \tag{5}\]  
\[sgn(X_{j}-X_{i})=\left\{\begin{array}{c}1,(X_{j}-X_{i})>0\\ 0,(X_{j}-X_{i})=0\\ -1,(X_{j}-X_{i})<0\end{array}\right. \tag{6}\]  
where \(sgn\) is the symbolic function; \(n\) represents the number of years during 2000-2018, i.e., \(n=19\). The standard normal test statistic (\(Z\)) can be calculated by  
\[Z=\left\{\begin{array}{c}\frac{S-1}{\sqrt{var(S)}},S>0\\ 0,S=0\\ \frac{S+1}{\sqrt{var(S)}},S<0\end{array}\right. \tag{7}\]  
\[var(S)=\frac{n(n-1)(2n+5)-\sum\limits_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+5)}{18} \tag{8}\]  
where \(var\) indicates the variance; \(m\) is the number of recurring datasets in the series, and \(t_{i}\) is the number of repeated datasets in the \(i\) group.  
The outputs of the Theil-Sen analysis and Mann-Kendall test are two images of the slope and significance tests at the 500 m resolution. When \(\mid Z\mid>Z_{1-\alpha/2}\), the null hypothesis is refuted, and the trend is significant. The value of \(Z_{1-\alpha/2}\) can be found in the standard normal distribution table. In this research, significance degree at \(\alpha=0.05\) is selected, which corresponded to \(Z_{1-\alpha/2}\) value of 1.96 to classify the slope values into three categories: significant positive (\(\beta>0,\mid Z\mid>1.96\)), negative (\(\beta<0,\mid Z\mid>1.96\)), and insignificant changes (\(\beta\)\(\exists,\mid Z\mid\leq 1.96\)) [19,48].
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Limited Number of User Pairs  
The advantages of NOMA are made possible by its use of power-domain multiplexing. In order to pair a user at the cell's periphery with a user in the cell's center, there must be a propagation loss difference of roughly 8 dB. As a result, the capacity benefit of NOMA is diminished since the number of user pairs is capped in a typical NOMA design. It is crucial to find a method of signal recognition and decoding that can boost the capacity for user pairs [163].
## 7 Conclusions  
The ultimate goal of the TB drug-development efforts is a design of new, more effective regimen, which could replace the current combination therapy. One of the most promising DprE1 inhibitors is macozinone. Its safety and tolerability profiles, pharmacokinetics, and efficacy in terms of early bactericidal activity were evaluated in the course of three clinical trials conducted by Nearmedic Plus LLC in the Russian Federation, two clinical trials by iM4 TB foundation are in progress. High tolerability and a favorable safety profile of the drug in the studied dose range were demonstrated both in healthy volunteers and in patients with newly diagnosed pulmonary tuberculosis with bacterial excretion and preserved sensitivity to isoniazid and rifampicin. The main pharmacokinetics parameters of macozinone after single and multiple administration in the dosage range up to 1280 mg were studied, and a statistically significant efficacy of the drug after monotherapy at a dose of 640 mg a day was established, which allowed the preferred regimen of its intake to be determined.  
A thorough study aimed at interactions of macozinone with a spectrum of clinically used and experimental TB drugs was recently performed [30]. It revealed that macozinone does not have synergistic or antagonistic interactions with the tested first-line (rifampin, isoniazid, ethambutol) or second-line (amikacin, levofloxacin, moxifloxacin, D-cycloserin, ethionamide, _para_-aminosalicyc acid) drugs. Among the tested re-purposed or new drugs, clarithromycin, delamamid, lansoprazole sulfide, linzeold, meropenem or sutezoldid did not show synergistic effects, when tested individually with macozinone by the checkerboard assay. Synergism was observed for macozinone and clofazimine or bedaquiline, confirming previous findings [20; 41]. At the same time, enumeration of colony forming units after drug exposure revealed the next two potentially synergistic candidates--delamamid and sutezold. In the mouse model of TB, the combination of macozinone-delamamid-sutezold was more active in the lungs of _M. tuberculosis_-infected animals compared to the regimen of rifampin-isoniazid-pyrazinamide, while the bacterial burden in spleen remained comparable. However, increased toxicity of the combination for HepG2 cells was observed, so further studies are needed to evaluate the potential of this drug combination [30]. Nevertheless, neither antagonism nor increased toxicity was found for most combinations, which paves the way for using macozinone in the development of more efficient TB regimens [30].  
Both authors contributed to writing of the paper. All authors have read and agreed to the published version of the manuscript.  
[PERSON] acknowledges current support by Slovak Research and Development Agency (APVV-15-0515).  
Authors would like to thank Nearmectis Plus LLC, and personally to [PERSON] for information concerning clinical trials and preclinical studies including unpublished data.  
[PERSON] is a named inventor on patents pertaining to work on benzothiazinones mentioned herein. [PERSON] declares no conflict of interest.
# Experimental Study on the Risk Preference Characteristics of Members in Supply Chain Emergencies
## 1 Introduction
Moral hazard is reflected in the risk of unethical behavior caused by member enterprises using information asymmetry or the lack of a supervision mechanism. Legal risk refers to the risk caused by the non-standard legal behavior of member enterprises during the operation of the supply chain. Information risk refers to risks caused by inaccurate information transmission, leading to decision-making errors among supply chain members. Supply risk refers to the risks caused by the delayed transportation of goods, interruption of supply, shortage of goods, poor quality, etc. Logistical risk refers to the impact on the operation of the supply chain caused by the inability to provide normal logistics services due to uncertain factors. Demand risk refers to the risk that the supply chain system is confronted with in identifying and meeting customer needs due to the personalization, variability, and randomness of customer demand preferences. Cooperation risk refers to the risk of a \"cask effect\" or unequal distribution generated by member enterprises when implementing cooperation. Economic risk is the possibility of supply chain losses during operation due to the uncertainty of economic prospects. Political risk refers to the risk of supply chain interruption caused by political, cultural, and other conflicts between countries. Then, the correlation between the conventional risk fields and the risk preference was analyzed. Based on this, the risk preferences of subjects were measured using prospect theory and [PERSON]'s [13] questionnaire methods in the scenario of a supply chain emergency. Furthermore, the correlation between risk preference and the environment of supply chain risk decision-making was also explored. Given the time pressure variable (time pressure, no time pressure), the impact of time pressure on the risk preferences of the decision makers was investigated in supply chain emergencies, and the consistency test was carried out on the risk preference of supply chain emergencies with a time pressure variable and conventional risk fields with time pressure. Finally, this study explored the regularity of interaction between risk preference and decision risk frames, time pressure, and other factors to provide an effective methodological reference for the optimization of a supply chain emergency response method and decision management.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 5 Aviation Big Data Multi-Layer Security Sharing Framework
### Blockchain Optimization Deployment and Consensus Mechanism for ATM  
As a data-sharing solution, blockchain is widely applied across various industries, such as the Internet of Things (IoT), vehicular networks, education, and healthcare. However, blockchain deployment and consensus models pose challenges for ATM participants. Since blockchain occupies significant storage space, resource-constrained terminals (e.g., aircraft) struggle to deploy a full blockchain. Additionally, in the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, each node must broadcast twice in a single consensus round, resulting in a network communication complexity of O(N\({}^{2}\)) for an N-node system. As the number of nodes increases, communication costs and consensus latency rise sharply, leading to rapid system performance degradation. This makes PBFT difficult to apply directly in dense, real-time ATM sharing systems.  
To address these issues, an optimization algorithm is used to partition the full blockchain into multiple blocks and distribute them across different nodes, reducing storage pressure on resource-limited nodes. Additionally, a clustering-based grouped PBFT algorithm is employed to decompose consensus tasks, lowering communication overhead and consensus latency.
## 5 Implementation and Results
### Experimental Results
This implication can be observed by the average TPR or sensitivity and the F1 score (93.57%) that is comparable to the overall accuracy presented in Table 7. It can be observed that approximately 11 out of 15 fruit or vegetables can be classified with an accuracy of 99%. A classification confusion matrix of the custom CNN AdaBoost optimisation is depicted in Figure 5 for the fruit and vegetable classes presented in Table 3.  
An inference time analysis was performed to estimate the practical implementation of the proposed technique. A batch of 15 random images, with one from each class was selected for inference analysis where the total inference time (ms) was noted as the time to classify all 15 images. We performed this analysis on a device for both CPU and GPU-based classification where the fastest CPU-based inference is approximately three times slower than GPU-based inference. A description of the hardware used for the computation is presented in Table 9. The images were loaded in the form of a tensor in the memory where total inference time includes a time to read the tensor of 15 images from memory and the model computation time. On average, a GPU-based inference of an image takes approximately 588.44 ms which is 2.8 times faster than the CPU-based inference time of 1647.65 ms with the optimised custom CNN. A comparison of inference times for the AdaBoost optimised Google, MobileNets, and custom CNN models is presented in Table 10, the time for single image inference is obtained by dividing the total inference time by the number of images in the batch. The inference time for Google and MobileNets is significantly higher than the proposed AdaBoost-optimised CNN network, however, an inference in a real implementation will also depends upon the Input/Output (I/O) and related overheads of an execution platform. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Epochs** & **Network** & **Training Accuracy (\%)** & **Test Accuracy (\%)** & **Network** & **Training Accuracy (\%)** & **Test Accuracy (\%)** \\ \hline
10 & & 81.90 & 78.56 & & 78.69 & 71.52 \\
15 & Pre-trained & 93.45 & 82.71 & Pre-trained & 81.23 & 78.86 \\
20 & GoogleNet & 94.65 & 81.78 & MobileNet & 89.98 & 80.23 \\
25 & & 96.45 & **83.56** & & 94.87 & 81.44 \\
30 & & **95.67** & 82.10 & & **95.56** & **83.15** \\ \hline
10 & & 81.86 & 72.96 & & 86.56 & 81.45 \\
12 & AdaBoost & 87.10 & **81.24** & AdaBoost & 92.63 & 87.21 \\
14 & GoogleNet & 89.74 & 78.58 & MobileNet & 94.44 & 88.45 \\
16 & & 93.25 & 76.63 & & **95.50** & **91.33** \\
18 & & **96.21** & 76.00 & & 94.88 & 87.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison of training and test accuracy of transfer learned and AdaBoost-optimised Google and MobileNets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Epochs** & **Training Accuracy (\%)** & **Test Accuracy (\%)** \\ \hline
10 & 93.10 & 80.13 \\
15 & 94.17 & 83.43 \\
20 & 96.42 & 88.69 \\
22 & 95.67 & **93.97** \\
25 & **97.14** & 85.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training and test accuracy achieved with AdaBoost-optimised custom CNN.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Missions
#### 7.1.1 Merlin  
An eagerly anticipated mission is the MERLIN satellite (a collaboration of CNES and DLR) due for launch around 2028, which will carry a LiDAR to measure atmospheric methane concentrations. MERLIN will use an Integrated Path Differential Absorption (IPDA) LiDAR to map methane with 50 km resolution, operating day and night at high latitudes where passive sensors have limitations. Its success could pave the way for a suite of greenhouse gas LiDARs (NASA has considered a similar \(CO_{2}\) IPDA concept under the name ASCENDS) [166; 167; 168].
# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 3 Experiments and Results
### Evaluation Methods  
The Bjontegaard Delta-rate (BD-rate) metric is expressed as bits per pixel (Bpp). To evaluate the distortion between the reconstructed frames and the original frames, Peak Signal-to-Noise Ratio (PSNR), Multi-Scale Structural Similarity Index Measure (MS-SSIM),and Video Multi-method Assessment Fusion (VMAF) were employed. Based on the obtained PSNR and MS-SSIM results, we calculated the average PSNR and average MS-SSIM of all reconstructed frames and plotted the RD curve to visualize the comprehensive performance of DFVC at different bit rates.
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 6 Dynamic Simulation Results
### Case 3: Wind Speed = 13.0 m/s  
Figure 10 displays the simulation results, where the wind speed condition of the DFIG-based WTG is 13.0 m/s (full load case). In this case, the wind speed is greater than the rated wind speed so that the pitch angle controller is activated.  
The maximum _df/dt_ in the proposed strategy is -0.388 Hz/s, which is better than that of the conventional strategy and the MPPT operation (see Figure 10a). The frequency nadir in the proposed strategy is 59.267 Hz, which is greater than that of the conventional strategy and the MPPT operation by 0.650 Hz and 0.090 Hz, respectively. In the proposed strategy, a large amount of active power is injected to the power grid due to the proposed control gain in (13); as a result, the reduction in the rotor speed is large, and further, the large rotor speed deviation results in the large reduction in the pitch angle due to the large control gain of the proposed strategy (see Figure 10e). Consequently, the performances of the frequency nadir and the maximum _df/dt_ are better than the conventional strategy (see in Figure 10b,c).  
Furthermore, the nadir-based frequency response of the proposed strategy is 122.78 MW/Hz, which is more than in the conventional strategy by 9.72 MW/Hz and more than that of the MPPT operation by 13.42 MW/Hz. This is because of the high frequency nadir. As displayed in Figure 10c, the released kinetic energy from the DFIG-based WTG in the proposed method is 0.017 s, which is more than that of the conventional method by 0.007 s due to the large control gain in the proposed strategy. As shown in Figure 10e, the pitch angle of the proposed strategy decreases to 5.39\({}^{\circ}\), whereas the pitch angle of the conventional strategy decreases to 6.11\({}^{\circ}\).  
The above simulation results display that the suggested virtual inertia control strategy is capable of ensuring better performance on the maximum _df/dt_ and the frequency nadir by using the kinetic energy-dependent control gain under the scenarios of full and partial loads.  
Figure 10: Results for Case 3: **(a)** system frequencies; **(b)** active powers; **(c)** rotor speeds; **(d)** control gains; **(e)** pitch angle.
## 4 Simulation
### Genetic Sliding Mode DYC
#### 4.2.2 Low Tire-Road Friction Coefficient Road  
The tire-road friction coefficient is set to 0.3. Such a road condition is a more severe test for vehicles. Other simulation conditions are the same as those in Section 4.2.1. The simulation results are shown in Figure 8.  
Figure 8: DYC controller on low tire–road friction coefficient road. (**a**) yaw rate; (**b**) sideslip angle; (**c**) front left driving torque; (**d**) front right driving torque; (**e**) rear left driving torque; and (**f**) rear right driving torque.  
As shown in Figure 8a,b, on a low tire-road friction coefficient road, there is chattering in both the yaw rate and sideslip angle when the ordinary sliding mode control is applied, which is unsafe and uncomfortable, and the deviation from the ideal value is large. However, the genetic sliding mode DYC almost eliminates the chattering in the yaw rate and sideslip angle, and the tracking accuracy is also improved. As shown in Figure 8c-f, the ordinary sliding mode control has an excessive chattering in the wheel torque which is impossible to be applied in practice. However, even on the low tire-road friction coefficient road, the genetic sliding mode control can still suppress the chattering effectively, which makes it more practical.  
To sum up, the proposed genetic sliding mode DYC is able to maintain the yaw stability effectively, even on the low tire-road friction coefficient road. Chattering is common in the sliding mode control. Different from other studies, the genetic algorithm is introduced to compute the two parameters of the approaching law in the sliding mode control. The simulation results show that the tracking effects have been further improved. More importantly, the chattering caused by the sliding mode control is effectively suppressed.
# Reconstructing the 26 June 1917 Samoa Tsunami Disaster
## 4 Results  
### Tsunami Inundation and Validation  
Figure 4 shows the modelled maximum tsunami wave heights for the 1917 tsunami event. Of particular note is that the southwest side of Savai'i is mostly affected where wave heights of 2 m appear to have impacted the coast. Interestingly, the arrival of the simulated wave in Apia harbour suggests that it took approximately 34 min travel time to this location. This is consistent with the observed tide gauge record (Figure 5) when using a 20 min shift in the reference time.  
Figures 6 and 7 show which parts of Upolu and Savai'i were most affected by the tsunami, which are generally consistent with available runup observations [4; 31] as well as sedimentary evidence presented in [30]. Inundation on Savai'i mostly affects the south western side of the island with much higher flow depths compared with eastern parts of the island including Upolu. Northern Upolu appears unaffected except in areas near Apia. Observed runup points derived from historical records identified in Savai'i (Figure 6) and Upolu (Figure 7) highlight the limited runup observations available for this event.  
Figure 4: Maximum wave height offshore for the modelled 1917 tsunami.  
Figure 5: Detided water level recorded in Apia (black line) and simulated (red line). Earthquake time is also given.  
Figure 6: Inundation on Savai’i in close-ups (10 m horizontal resolution). The map shows the 10 m resolution run-up on Savai’i (**a**) and close-up of inundation in Satiatiata (**b**), and in Palauli and Satup’ita (**c**).  
The total number of people living in residential buildings within the modelled 1917 tsunami inundation zone are exhibited in Figure 9. In total, approximately 7919 people across 1074 residential buildings (71% of which are in Savai'i) would be affected by the tsunami, which amounts to approximately 4% of the total population in 2016 [37].  
It is worth noting that on Savai'i, 13% of the affected inhabitants live in buildings that are estimated to sustain damage states of DS5 (i.e., complete building collapse). This is particularly the case for the district of Palauli West. On Upolu, more than half of the affected population live in buildings estimated to sustain damage states DS0 (i.e., no damage). The more inundated part of Upolu in the west of the island exposes approximately 77 people who live in buildings likely to sustain damage state DS3 or greater.
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
## 3 Some Statistical Features
### Quantile Function  
The \(q\)th QF \(x_{q}\) of the \(TLMW(\alpha,\beta,\theta,\lambda)\) is the real solution of the following equation  
\[\alpha x_{q}+\beta x_{q}^{\theta}+\frac{1}{2}\ln\left(1-q^{\frac{1}{\lambda}} \right)=0. \tag{16}\]  
In particular, setting \(q=\frac{1}{2}\) in Equation (16), the median of \(X\) can be calculated. Also, using the QF given by Equation (16), the Bowley's skewness (see [33]) and the Moors' kurtosis (see [34]) can be determined as  
\[B_{{}_{S}}=\frac{Q(\frac{3}{4})-2Q(\frac{2}{4})+Q(\frac{1}{4})}{Q(\frac{3}{4}) -Q(\frac{1}{4})}\]  
and  
\[M_{k}=\frac{Q(\frac{7}{8})-Q(\frac{5}{8})+Q(\frac{3}{8})-Q(\frac{1}{8})}{Q( \frac{6}{8})-Q(\frac{2}{8})}.\]  
where \(Q(.)\) is the QF provided in Equation (16). Table 1, provide the median, skewness and kurtosis, of the \(TLMW\) distribution for various values of \(\alpha\), \(\beta\), \(\theta\) and \(\lambda\). It is clear from Table 1 that for fixed \(\alpha\), \(\beta\) and \(\theta\) with increasing \(\lambda\), median and kurtosis are decreasing but the skewness is increasing.