esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Limited Consideration of Interrelated Effects of Wildfire and Its Behavior on Evacuation  
Another significant gap in existing evacuation models is the limited consideration of the interrelated effects of wildfire and its behavior on the evacuation process. Many available evacuation models primarily focus on modeling the evacuation process itself without adequately accounting for the dynamic and evolving nature of the wildfire and its influence on evacuation decisions and behaviors.  
In reality, wildfires are highly complex and dynamic events, and their behavior can change rapidly due to various factors such as weather conditions, fuel types, and topography. As the wildfire progresses, its intensity, direction, and speed can vary, directly impacting the evacuation process. Evacuation decisions and behaviors are not solely determined by static factors but are profoundly influenced by the evolving and uncertain nature of the wildfire.  
Addressing this gap requires advancements in wildfire behavior modeling, data assimilation techniques, and the integration of real-time information during evacuation simulations. Combining wildfire simulation models with evacuation models in a coupled manner can enable a more holistic understanding of the interrelated effects of wildfire and evacuation dynamics. Such an integrated approach will help capture the feedback loops between wildfire behavior and evacuation processes, resulting in more realistic and robust evacuation simulations.  
Additionally, research efforts should be directed toward understanding how evacuees respond to uncertainty and risk perception during dynamic wildfire events. This includes exploring how real-time information updates, emergency alerts, and situational awareness influence evacuee decision-making and behaviors. An improved understanding of these psychological and behavioral aspects can inform the development of decision-support tools and communication strategies that enhance the effectiveness of evacuation plans.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 3 Description of an Isolated Fractional-Order Microgrid Model
### Model of a Photovoltaic (PV) Generation  
The electrical energy can be produced from the energy of the photons in PV cells, which are constructed from semiconductor materials. Due to the external and boundary contiguity along with the series resistance, losing power is inevitable in PVs. Naturally, the PVs have an intermittent characteristic, and their generated power depends on several factors, such as the radiation intensity, the surface area of the cell, and the ambient temperature [11]. A random power source can be utilized to model the stochastic behavior of PVs in simulations.
# Using Plastic Waste in a Circular Economy Approach to Improve the Properties of Bituminous Binders
## 3 Results and Discussion
### Complex Shear Modulus (\(G^{\star}\)) and Phase Angle (\(\delta\))  
Figure 11 shows the evolution of complex shear modulus (\(G^{\star}\)) and phase angle (\(\delta\)) as a function of temperature (\(\theta\)), obtained in the DSR identified in Section 2.2.4 (@ \(\omega\approx 1.6\) Hz \(\approx 10\) rad\(\cdot\)s\({}^{-1}\)). The samples were 25 mm in diameter, 1 mm in thickness, and were moulded with the same three bituminous binders (35/50, PMB, and PMB + wax).  
It was observed that theend line of shear modulus versus temperature of PMB + wax was the one that presented the lower exponent (in absolute value). Furthermore, the phase angles measured in this binder also showed the lowest values at all temperatures. Thus, in this case, it could be inferred that PMB + wax respects the objective of achieving high values of \(G^{\star}\) and low values of \(d\) to have better rutting resistance. A similar conclusion was already achieved by [PERSON] [61] after testing FT and PE waves mixed in two soft binders (50/70 and 160/220). This researcher stated that when the modification is conducted correctly, incorporating these waves improves the performance of the bituminous mixtures at high temperatures.  
Table 6 shows a comparison between the results of mixing three different waves in two PMB (the one studied in this research (PMB + wax) and two others tested by [PERSON] et al. [17]--RPPW and FTW). The evaluated parameters, the rutting resistance (\(G^{\star}\)/sin d, at 60 \({}^{\circ}\)C) and the upper limit temperature (when \(G^{\star}\)/sin d = 1 kPa or log (\(G^{\star}\)/sin d) = 0, with d being in radians), were defined according to SHRP specifications [62].  
Figure 11: Rheological properties of bituminous binders: (**a**) complex shear modulus (\(G^{\star}\)); (**b**) phase angle (\(\delta\)).  
Figure 10: Complex dynamic viscosity (\(\eta^{\star}\)) of three bituminous binders.  
It was possible to confirm that adding the developed wax blend (PMB + wax) and FTW (a commercial FT wax for WMA) makes both PMB less sensitive to rutting and promotes an increase in the upper limit temperature, while the RPPW (obtained from the pyrolysis of \"recycled cross-linked polyethylene\", previously chipped) favours a slight variation. However, as mentioned by [PERSON] et al. [62], the use of the SHRP parameter (G*/sin d) is under scrutiny as it will not be the best way to assess the rutting resistance of binders, particularly at higher temperatures. For this reason, the protocol known as multiple-stress creep recovery (MSCR) has been applied more since 2011. In this procedure, the Jnr and %R parameters (non-recoverable creep compliance and recovery percentage, respectively) are evaluated, using the EN 16659 [63] or AASHTO T350 standards, which give better indications about the performance of binders at high temperatures [28; 32].
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 2 Existing Studies and Literature Review
### RFID and Tagging Technologies in Coffee Traceability Systems  
RFID implementation offers robust physical traceability capabilities. [PERSON] et al. (2021) developed UHF RFID solutions operating in the 902-928 MHz range with 3-5 m read distances through coffee sacks. In addition, they incorporated anti-collision protocols, achieving 98% identification accuracy at conveyor speeds of 2.5 m/s [20]. [PERSON] and [PERSON] (2016) addressed durability concerns with IP67-rated tags capable of withstanding 85-95% humidity levels while maintaining 99.3% read reliability over a 12-month period [21].  
Energy efficiency advances were demonstrated by [PERSON] and [PERSON] (2022), who implemented a heterogeneous system combining passive ISO/IEC 18000-6C tags with IEEE 802.11 ah active readers, resulting in a reduction of power consumption by 73% compared to conventional implementations [22].
## 2 Accuracy of Predictive Methods and What Is Required
### Why do Methods often not Perform up to the Required Level of Accuracy and Reliability?
Consequently, a hybrid method involving, for example, a Group Contribution method and a fast semi-empirical quantum mechanics method can yield accurate and reliable predictions of the Gibbs free energy. To arrive at the highest accuracy that seems currently realizable, anharmonic contributions need to be taken into account, where those related to the relatively low rotational barrier of C-C bonds are most often the most important tones and a practical procedure can be put in place [40]. This is just a specific example, but more examples exist. Referring to toxicity prediction, when there is an understanding of the molecular interactions responsible, methods may be developed that are more realistic than statistical methods only, an example being \"Thiol Reactivity and Its Impact on the Ciliate Toxicity of \(\alpha\),\(\beta\)-Unsaturated Aldehydes, Ketones and Esters\" [41]. These are a few, though real-life relevant examples. Ad (\(\
u\)) refers to the large class of neutral organic molecules, several physico-chemical properties are of wide interest in different science communities and different methods have been developed for the same purpose. This particularly applies to properties including log K\({}_{\text{ow}}\) where technologists, environmental scientist and chemists all have an interest, the pKa and boiling point. There is no a priori and natural communication between the different communities, although there are obviously exceptions. Looking in the different areas might provide you with a more suitable predictive method. Eventually, close collaboration between such research groups would greatly facilitate progress in the field of predictive modelling.
## 3 System Architecture
### Digit Recognition Scheme  
The \"one-size-fits-all\" approach to digit recognition assumes that we are running the inference with the same ML model on each of the edge nodes. Following the basic idea that the edge nodes will be used in a variety of environments, with differing counter types, lighting conditions, as well as relative positions between the camera and the counter, the aforementioned approach turns out to be inappropriate. Thus, our strategy relies on the capability of the edge nodes to adapt to the on-site environment by updating, re-training, and improving their local ML models.  
Digit recognition is a classification problem, which, by its nature, can be solved utilizing a supervised learning paradigm: input instance is an image that is to be classified into one of the 10 categories corresponding to decimal digits. A sufficiently large dataset needs to be generated and labeled in advance to train an appropriate ML model. The easiest way to do it is to take a series of images and upload them to the server to be labeled (either manually or by using a highly accurate digit recognition model, which is beyond the scope of this work). However, several key points need to be addressed to apply this strategy as efficiently as possible:  
* Full-size image produced by a 5-megapixel camera is by itself a cumbersome piece of data, for two reasons. Uploading such an image to the server would be associated with unacceptably high communication costs. On the other hand, to run an inference on such a large input instance, the ML model would have to be too large to fit into a resource-constrained edge device. Therefore, a considerable effort must be put into reducing the size of the input image, without compromising the ability to extract useful information from it.
* The nature of the problem is such that the rate by which the digits on the counter are updated is extremely unbalanced, even between the digits belonging to the same counter: starting from the rightmost (i.e., least significant) digit, every subsequent digit is updated by a tenth of the rate of the previous one. Furthermore, some counters might be used in households with people living inside, thereby changing state significantly faster than others used in remote objects, where the same utility is used seasonally and/or occasionally. This implies the necessity to implement a mechanism to reliably detect when a change in the counter state actually occurred, which would make the system robust and adaptive to the volatile nature of the measurement process.
## 3 Results and Discussion
### Catalyst Characterisation  
#### 3.1.1 TEM Results  
The PdZn catalyst was characterised by TEM (Figure 1) to explore the bulk properties of the system (bulk zinc palladium atomic ratio, particle size distribution, and average particle size). The catalysts displayed a mean particle size of 2.1 nm, with a narrow particle size distribution (Figure 1b) and with particles well dispersed on the surface of the carbon support. On the other hand, the Pd-monometallic counterpart (1% Pd/HHT) showed a higher average particle size of 3.9 nm [4]. STEM-XEDS analysis confirmed the presence of Pd-Zn bimetallic particles with an average molar ratio of 86:14 lower than the nominal one (60:40) (Figure 1a). Figure 2 displays STEM images representing the d spacing.  
Figure 1: (**a**) STEM–XEDS of Pd6 Zn4 catalyst (Pd–Zn atomic ratio: 86:14) and (**b**) particle size distribution.
## 3 Results and Discussions
### Hydraulic Conductivity  
The hydraulic conductivity of the aquifer provides information on the propagation rate of pollutants in the aquifer. The pumping tests in twenty-two boreholes capturing the aquifer [38] were used to establish the map of this parameter. According to the established map (Figure 9g), three classes of conductivity are distinguished: (i) Class (1) presents a value of hydraulic conductivity oscillating from \(14.7\times 10^{-5}\) to \(32.9\times 10^{-5}\) m/s--this class is located in the Ain Chabro locality (south of the plain); (ii) class (2) presents a values of hydraulic conductivity varying between \(4.7\times 10^{-5}\) and \(14.7\times 10^{-5}\) m/s--this class is located in an area limited to Tebessa and El Hammamet cities in the south and Morsott city in the north; (iii) class (3) has very low values of hydraulic conductivity that vary between \(4.7\times 10^{-7}\) and \(4.7\times 10^{-5}\) m/s--this class is distributed over the entire plain.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
###### Abstract  
Few-shot learning (FSL) is a learning paradigm which aims to address the issue of machine/deep learning techniques which traditionally need huge amounts of labelled data to work out. The remote sensing (RS) community has explored this paradigm with numerous published studies to date. Nevertheless, there is still a need for clear pieces of evidence on FSL-related issues in the RS context, such as which of the inference approaches is more suitable: inductive or transductive? Moreover, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference? This study aims to address these and other relevant questions in the context of FSL for scene classification in RS images. A comprehensive evaluation was conducted considering eight FSL approaches (three inductive and five transductive) and six scene classification databases. Some conclusions of this research are as follows: (1) transductive approaches are better than inductive ones. In particular, the transductive technique Transductive Information Maximisation (TIM) presented the best overall performance, where in 20 cases it got the first place; (2) a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase. The most impressive gains occurred particularly considering the AID (6-way) and RESISC-45 (9-way) datasets. Notably, in the AID dataset, a remarkable 58.412% improvement was achieved in 1-shot tasks going from 10 to 200 epochs; (3) using five samples in the support set is statistically significantly better than using only one; and (4) a higher similarity between unseen classes (during inference) and some of the training classes does not lead to an improved performance. These findings can guide RS researchers and practitioners in selecting optimal solutions/strategies for developing their applications demanding few labelled samples.  
few-shot learning; remote sensing image scene classification; deep learning statistical tests +
Footnote †: journal: Medical Image and Vision  
**Citation:** [PERSON], V.A.d.  
Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing  
**Languages. _Appl. Sci._**2024, 14, 10776. [[https://doi.org/10.3300/app142310776](https://doi.org/10.3300/app142310776)]([https://doi.org/10.3300/app142310776](https://doi.org/10.3300/app142310776))**  
Academic Editor [PERSON]  
Received: 13 October 2024  
Revised: 7 November 2024  
Accepted: 13 November 2024  
Published: 21 November 2024
# Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method
## 3 Results
### Simulated Organ Doses  
The simulated organ doses and the measured \(WED\) for the 9 females are plotted in Figure 2. The lung, heart, and breast doses were 14.04-23.19 mGy, 14.54-23.97 mGy, and 11.92-21.12 mGy, respectively. Moreover, the mean dose of lung, heart, and breast were 18.15 \(\pm\) 2.69 mGy, 18.68 \(\pm\) 2.87 mGy, and 16.11 \(\pm\) 3.08 mGy, respectively. For both men and women, there was no statistical difference between lung dose and heart dose (\(p\) > 0.05). However, the breast dose was statistically different from that of the other two organs (\(p\) < 0.05). As shown in Figures 2 and 3, the organ doses generally increase with the increase of the \(WED\) for both genders (\(p\) < 0.05). The phenomenon is consistent with previous research [13]. Through a one-way ANOVA test, it was found that there was no statistical difference in the absolute doses of the three organs for both males and females. However, for individuals, the difference between the breast dose and the lung or heart dose could reach up to 20%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Patient ID** & **Age** & **Gender** & \begin{tabular}{c} \(WED\) \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} \(CTDI_{vol}\) \\ **(cmGy)** \\ \end{tabular} & \begin{tabular}{c} **Lung Volume** \\ **(cm\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Heart Volume** \\ **(cm\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Glandular Tissue** \\ **Volume of both** \\ **Breasts (cm\({}^{3}\))** \\ \end{tabular} \\ \hline
1 & 32 & F & 18.27 & 7.63 & 3461 & 609 & 233 \\
2 & 26 & F & 19.54 & 8.89 & 3588 & 546 & 28 \\
3 & 69 & F & 20.22 & 10.21 & 44  
To test the correlations between the organ dose and organ volume, Pearson correlation analysis was performed in this study. No statistical correlation was found between the organ dose and the organ volume (\(p>0.05\)). This implies that it is possible to roughly estimate the organ dose with the aid of the _WED_. It was also found that larger patients received higher organ doses than smaller patients, which can be explained, as the tube current increases to compromise the more X-ray attenuation for larger patients by the ATCM.
## 4 Simulation Results
### Output C/N\({}_{0}\) Performance when Interferences with Different Polarization Modes  
In this scenario, the output C/N\({}_{0}\) performance of the proposed STPAP algorithm is compared with those of the STPAP algorithms based on the criterions in [29; 30; 31] when there are multiple GNSS satellites in the FOV. Without loss of generality, we can compare the output C/N\({}_{0}\) performance of these four algorithms when one beam covers two, three, and four BD2 satellites, respectively when there are respectively one, two, three, and four interferences. As shown in Figure 10, the results can be summarized as follows: (a) the output C/N\({}_{0}\) performance of the STPAP algorithm in [30] is always the best one, while the output C/N\({}_{0}\) performance of the STPAP algorithm in [29] is worse than the other three criteria; (c) the output C/N\({}_{0}\) performance of the proposed STPAP algorithm is a little better than the existing STPAP algorithm in [31]. Meanwhile, it is close to that of the existing STPAP algorithm in [30] when one beam covers two or three satellites. It indicates that the parameter \(J\) in (12) is better to be set as 2 or 3 to prevent the output C/N\({}_{0}\) performance from degrading dramatically.  
Figure 10: Output C/N\({}_{0}\) performance comparison between the proposed STPAP algorithm and the STPAP algorithms based on the criterions in [29; 30; 31] when there are interferences with different polarization modes: (**a**) one interference; (**b**) two interferences; (**c**) three interferences; (**d**) four interferences.
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
## 4 Simulation Results
### Performance of the Improved Posterior Probability Calculation Method  
The decoding performance of SPDCAC is closely related to the posterior probability calculation method used in decoding. Figure 5 shows the BER performance of SPDCAC using three different posterior probability calculation methods in the decoding process. After the encoder of SPDCAC encodes a binary Markov source (\(P_{t}=0.9\)) of length \(N~{}=~{}1024\) at a rate about \(0.3~{}b/s\), the decoder of SPDCAC uses three different posterior probability calculation methods to decode the source sequence under different \(H(X|Y)\) (the maximum width of the decoding tree is limited to 256), and then, their respective BER performance results are obtained. The three calculation methods for the posterior probability are: 1. the method proposed in (5); 2. the method used in [21], that is \(\lambda_{i}~{}=~{}logP(\hat{X}_{i}|Y_{i})+logP(\hat{X}_{i}|\hat{X}_{i-1})\); 3. the method used in DAC [15]. It can be seen from the figure that the application of the context model for the calculation of the posterior probability during the decoding process has a significant impact on the BER performance of the decoder. Both methods 1 and 2 use the context model in the posterior probability calculation, and their BER performances are significantly better than method 3 which does not employ the context model. Meanwhile, the BER performance of method 1 is far better than that of the other two. Especially when \(H(X|Y)<0.35\), method 1 can obtain lossless decoding results, while the other two methods cannot.  
The posterior probability calculation in [21] makes use of the conditional probability \(P(X_{i}|X_{i-1})\), so it exhibits a better BER performance than the method in [15]. However, that scheme does not consider the different decoding cases in the decoding process (whether the decoding ambiguity appears). Those cases are taken into account in the proposed algorithm, where the conditional probability \(P(X_{i}|X_{i-1})\) is only calculated at the positions where \(x_{i}\) are purged. This can expand the gap between the values of the cumulative product of posterior probabilities of different decoding paths. The most likely correct decoding path will quickly accumulate to a greater posterior probability. Therefore, the algorithm proposed in this paper is more effective than the scheme in [21] in terms of BER performance.