esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comprehensive Review
### A Comprehensive Overview of Bioactive Compounds in Different Food Drying Systems
#### 4.1.3 Freeze-Drying
The authors argued that while FD is effective in preserving antioxidant activity, it does not promote the Maillard reaction, which occurs at higher temperatures and is linked to the formation of antioxidants, thus making freeze-dried samples less antioxidant-rich compared to those dried at higher temperatures. Some studies have also reported increased levels of bioactive compounds after FD, attributing this to the breakdown of internal structures that enhance the extraction of these compounds. For instance, [PERSON] et al. [58] found higher levels of total phenolics, flavonoids, and pectin in pumpkin following FD. This suggests that freeze-drying can not only preserve bioactive compounds but also facilitate the release and quantification of certain nutrients by disrupting the cellular structure. \begin{table}
\begin{tabular}{c c c c c} \hline
**No. ** & **Drying Method** & **Material** & **Main Findings** & **Reference** \\ \hline
1 & Encapsulation with maltodextrin (different dextrose equivalents), followed by freeze-drying & Amazonian fruits (_Clidemia hirta_ L) D. Don and _Clidemia japurensis_ & The antioxidant activity of _C. hirta_ and _C. japurensis_ juice ranged from 97.04% to 99.79\% (DPPH method) and from 87.80\% to 99.11\% (ABTS method). & [73] \\
2 & Natural drying, microwave, hot air, infrared, vacuum, and freeze-drying & Flowers (_Bletilla striata_ (_Thumb._) & Freeze-drying was the best option for preserving active compounds, with high levels of total phenolics (31.44 mg/g) and total anthocyanins (9.94 mg/g). & [60] \\
3 & Freeze-drying and oven drying (at 50 and 65 \({}^{\circ}\)C) & _Fresh pumpkin_ (_Cucurbita moschata_) residues & [70] \\
3 & Freeze-drying and oven drying (at 50 and 65 \({}^{\circ}\)C) & _Fresh pumpkin_ (_Cucurbita moschata_) residues & [74] \\
3 & Freeze-drying and oven drying (at 50 and 65 \({}^{\circ}\)C) & _Fresh pumpkin_ (_Cucurbita moschata_) residues & [70] \\
3 & Freeze-drying and oven drying (at 50 and 65 \({}^{\circ}\)C) & _Fresh umbrella moschata_) residues & [70] \\  
Although FD is a slow and costly process, it is consistently recognized as one of the most effective methods for preserving bioactive compounds, especially when combined with pretreatments or rapid freezing methods. While it has some limitations, particularly with the oxidative degradation of sensitive compounds and the lack of the Maillard reaction, FD remains one of the most reliable methods for preserving thermosensitive bioactive compounds, with significant potential for food and nutraceutical applications. Continued research and the optimization of FD parameters could further enhance its effectiveness and reduce some of these limitations.
## 4 Discussion
### The Model Performance  
Our model uses only 10 linear measurements (Table 1) to describe cranial shape, with the addition of the geometric mean computed from those measurements. The best performing model (established based on parameter tuning on cross-validated data) was able to estimate sex with 84% accuracy. This result is virtually free of overfitting (see methods for information about how it was avoided), and the log loss performance shows that 90% of the individuals attributed to the correct sex were estimated with a probability higher than 0.65 (Figure 7). Our findings suggest that the model generated is capable of working efficiently with only a limited number of measurements (a positive characteristic for applications on fragmentary crania). Although this result appears underwhelming if compared to  
Figure 8: Receiver operating characteristic (ROC) curve showing the observed performance (compared to the uninformative case—random prediction—and a perfect estimation, where all observations were correctly assigned to their true class.  
the higher accuracies reached by other studies [28, 58], it must be highlighted that those studies have tested data on only small datasets, which cannot guarantee protection from random sampling effects. In this study, the model was tested on more than 600 individuals, suggesting that the result observed here is more reliable. Furthermore, here we base the evaluation of model performance on log loss, which accounts for the probability of the estimation--this is not common practice in the literature regarding sex estimation from skeletal material, and the absence of such information makes it difficult to assess the performance of previous models.  
The model generated here was built on a broad cranial variability of modern \(H\). _sapiens_, which suggests an easier generalisability to applications beyond single populations, although this was not explicitly tested. In addition, the accuracy demonstrated is remarkable if we consider that the training dataset consists of crania whose sex was estimated using a visual approach [41, 42, 43]. This finding is relevant because large datasets of known sex are very rare and often difficult to access, and sex estimation through visual inspection approaches can introduce biases, especially with regard to size [14, 58]. By monitoring the model performance through metrics such as AUC, it is possible to check for the prevalence of correct estimations for both sexes. In fact, AUC can account simultaneously for the classification of both classes in a binary application [59] (Figure 8). In this study, AUC was taken into account when tuning the parameters to choose the best-performing model among those generated. Thanks to the protocol used and based on the performance observed during testing, we can confidently assume that the model is unbiased toward one or the other sex.
# Paneth Cell, Gut Microbiota Dysbiosis and Diabetes Mellitus
## 4 Diabetes Mellitus
### Pathogenesis of Diabetes Mellitus
#### 4.1.6 Post-Transplant Diabetes after Solid Organ Transplant  
Post-transplant diabetes (PTDM) after solid transplantation is a common condition. Its incidence varies from 10-40%, depending on the definition [145; 146; 147; 148; 149]. The mechanism of development of PTDM differs based on the organ transplanted. New-onset diabetes after a liver transplant has a different mechanism than other solid organ transplants [145; 147; 149]. Post-transplantation diabetes is defined as the development of increased blood sugar levels during fasting times above 7 nmol/L, abnormal oral glucose tolerance test of 2 h >11 mmol/L, random glucose greater than 11 mmol/L, elevated glycated glucose (HbAlc) over 6.5% or a need for hypoglycaemic agents after transplantation in the absence of pre-existing diabetes [148]. Metabolic derangements of increased elevated triglycerides, hypertension, obesity and insulin resistance accompany it [149]. Metabolic derangement confers an increased risk for cardiovascular morbidity and mortality affecting survival after organ transplantation compared to non-diabetic organ transplant recipients [150]. The pathophysiology for PTDM is multifactorial, includes reduced insulin secretion, and increased insulin resistance in the peripheral circulation. The pathophysiology involves multiple drug groups, like corticosteroids, calcineurin inhibitors (CNIs) and mammalian target of rapamycin (mTOR) inhibitors. The mechanism relates to the high doses of steroids required to maintain the recipient graft function. Like in the other types of DM, in patients post organ transplant, PTDM is associated with dysbiosis of the gut microbiota [149]. Although Paneth cells are resilient, they sometimes become dysfunctional following solid organ transplant [77].
## 3 System Model and Problem Formulation
### GDRL-SFCR Intelligent Routing Framework
#### 4.2.3 Routing Decision  
DRL can solve MDPs by learning long-term strategies that maximize the objective function in an optimization problem. Specifically, the agent learns the optimal policy \(\pi_{\omega}(a_{t}\mid S_{t})\) through an iterative process that continuously explores the state and action space and is guided by a reward function. In this paper, our decision-making process involves the agent finding all the neighbor nodes of the current node \(v\), generating feature vectors for each case, and inputting them into the GNN. Then, based on the processingresults of the GNN, it makes the final routing decision, i.e., the selection of the next-hop forwarding neighbor, using the policy learned from the policy network. In the action selection phase, all path options that violate the SFC order are directly blocked. Based on the objectives of this paper, we designed the corresponding policy network.  
For each feasible action \(a_{t}\in A_{t}\) at step \(t\), the effective state values after the inner product of its state embedding and the state embeddings of its neighboring nodes are sent to the policy network to obtain the index of the distribution in which it is selected in state \(S_{t}\) as follows:  
\[P(a_{t},S_{t})=MLP_{\omega}[S_{t}] \tag{25}\]  
where \(MLP_{\omega}\) has three hidden layers and a ReLu activation layer. The probability of selecting each \(a_{t}\) is then calculated by applying softmax to all \(P(a_{t},S_{t})\):  
\[\pi_{\omega}(a_{t}\mid S_{t})=\frac{exp(P(a_{t},S_{t}))}{\sum_{a_{t}^{\prime}} P(a_{t}^{\prime},S_{t})},\forall a_{t}\in A_{t} \tag{26}\]  
During training, actions should be sampled according to the policy to explore. There are typically two strategies for solving a given instance using a trained strategy \(\pi_{\omega}\), including greedily selecting the actions with a maximum probability and sampling the actions that follow \(\pi_{\omega}\) in each state. Unlike the greedy strategy, the random sampling strategy may provide a different solution for each run, which provides an additional possibility to find a better solution. Therefore, this paper adopts the random sampling strategy.
# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 7 Discussion
It avoids overfitting by only making statistically significant splits, ensuring that the model generalizes well to unseen data. On the other hand, J48 constructs a static decision tree based on the training data, which often leads to overfitting, especially in complex, multi-class problems. The algorithm tends to create overly complex models that do not perform well on new data, especially when class imbalance is present. This, combined with its tendency to favor majority classes, results in its lower accuracy compared to the Hoeffding Tree in this study. Complex models like Multilayer Perceptron may overfit the training data, performing well on known samples but poorly on new ones. This happens when the model learns irrelevant details from the training data, failing to generalize to new leaf images. For instance, if MLP classifies based on specific edge details that are not consistent across all samples, it will struggle with slightly different images. Variations in image quality, such as lighting, focus, or background clutter, can introduce noise that affects feature extraction. Some algorithms, like Naive Bayes, which rely on probabilistic methods, are more vulnerable to this noise, while others, like Random Forest, handle it better. For example, a blurry or poorly lit image may lose key details like leaf venation, leading to misclassification. Similarly, if the margins of the leaf are not clearly visible, the model may incorrectly classify based on shape.
## 3 Results
### Difference Spectra  
The difference spectra presented in Figure 3 were calculated between the averaged spectra at all temperatures for the groups of waters defined as: (1) aged water (produced in 2014, Cha2014) and the freshly produced water (produced in 2016, Cha2016); (2) low-ion content waters (Gan, Cha, PQ, Q, Cha2016, Cha2014) and high-ion content waters (Tap, Kun, Tibet), where this grouping was defined based on the results of the HCA analysis, with a similarity level \(<\) 0.226 (Figure 1a); (3) low pH waters (pH \(<\) 7.5) and high pH waters (pH \(\geq\) 7.5); and (4) processed (PQ, Cha2014, Cha2016) and unprocessed waters (Q, Cha) (Figure 3, the colors are green, red, black and blue for the difference spectra 1, 2, 3 and 4, respectively). All difference spectra show two main peaks at around 1409-12 nm and in the region of 1487-92 nm. In terms of hydrogen bonding, these two peaks correspond to water structures with weak and strong hydrogen bonds, respectively [2; 3]. If difference spectra are understood as representing the average difference between water groups caused by the increasing temperature, then the highest difference was observed between the aged and freshly produced water, then the low-ion content and high-ion content waters, the low pH and high pH waters and, lastly, the smallest difference was observed between the processed and unprocessed water.  
Figure 2: Dendrograms of HCA analysis performed using SNV transformed NIR spectra of waters acquired at 30 \({}^{\circ}\)C. The nine different waters were classified into 4 groups based on the similarities in the spectral characteristics. The similarity between thus found clusters was 0.8.
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 4 Construction Method of the Top Layer Consisting of IMS
### Construction Algorithm  
Q-learning [13] is usually used to solve MDP problems with unknown prior parameters. \(Q\) learning uses the \(Q\) function to simulate the cumulative reward. The \(Q\) function represents the maximum reward that can be obtained by executing action \(a\) in state \(s\). In the process of learning, we first start with an initial state of \(Q(s,a)\), and then update \(Q(s,a)\) recursively according to Formula (9) in the form of continuous trial and error.  
\[Q(s,a)\gets Q(s,a)+\alpha(r(s,a)+\gamma\underset{a^{\prime}}{\max}Q(s^{ \prime},a^{\prime})-Q(s,a)) \tag{9}\]  
The recursive definition of the \(Q\) function is the root of \(Q\) learning. Taking into account the characteristics of the construction process, based on the improvement of \(Q\) learning, the Markov Decision Making algorithm MDC (Markov Decision Construction) is proposed which is shown in Figure 5. The algorithm contains the following three steps:  
1. Initialization: The MDC algorithm first initializes the \(Q\) matrix to be zero and uses the topological matrix TM and the reward function matrix as input, and initializes all the parameters.
2. Iteration: When the algorithm satisfies the termination condition, the iteration process is terminated. If the termination condition is not satisfied, the iteration process continues and calculates a complete construction path containing all nodes from the given initial node \(s_{0}\). In the process of building path computation, the system starts from the initial state \(s_{0}\) and executes a series of actions according to the greedy strategy. After executing each action, the system updates \(Q(s,a)\) according to Formula (9). In addition, the influence of the loop must be removed in the process of constructing the path.  
* Build: After the iteration is terminated, the optimal construction strategy will be found from the Q matrix. Then, based on the optimal construction strategy, we can build a mapping resolution system with optimal delay performance.
## 4 Performance Evaluations
### Survey Results  
The objective of the survey was to find out at what level of cost per program a user decides to wait for a proximate user to join considering the financial benefit to share the cost. After this value and if no other user has joined, the user may decide not to watch the intended program at the specified cost. We have conducted the survey with 45 users (only to derive some representative statistics) in two ranges of the costs based on the nature of the QoS guaranteed HD content; non-premium and premium. We have selected participants from Ireland and UK. They all pay for TV channels and have a good understanding of the concept of paying for TV based on the usage for specific contents on top of the monthly subscription. As an example they had additionally paid for some premium channels for a certain period at considerably higher rates like major sport tournaments. We explained to them about the benefits of collectively watching real-time contents with neighbours to gain economical incentives under a usage-based charging scheme. We suggested to them to think of one of their favourite programs under each category and complete the survey accordingly.  
Figure 4a shows the results for a non-premium content like previously released movies, news, soaps, kids programs _etc._ A user's desire to wait for proximate user to join was enquiredfrom 0.1 to 5 Euros. This is a reasonable range of rates for this kind of contents when derived from monthly subscription and the average monthly watching time of a person (but watching using HD) we have surveyed. According to the graph more than 80% of users say they will wait for a proximate user to join when the cost is approximately above 2.1 Euros. This shows their desire to use _ProxyTV_ when the cost factor becomes significant.  
Figure 4b shows the same results for a premium content like newly released movies, premium sport events, adult contents _etc._ The enquired rates for this category of contents were from 1 to 30 Euros. We have selected a higher range for premium contents considering the high cost (for high cost channels, cinemas, DVDs, _etc._) they pay for watching premium HD contents intended to deliver in _ProxyTV_. In this case, more than 80% users opt to wait for a proximate user to join when the cost is approximately above 8.7 Euros.  
Figure 4: Survey results (45 users). a. (top) A non-premium content; b. (bottom) A premium content.
# Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand
## 5 Calculation Analysis and Discussion
### Analysis of the Results
#### 5.2.1 Low-Carbon Economy Multimodal Transportation Program Impact Analysis  
Low-carbon policies often guide enterprises to reduce carbon emissions through economic instruments (e.g., \(R_{t}\) and \(Q_{s}\)). In multimodal transport path selection, enterprises  
Figure 7: Comparison of two algorithm iterations under different low-carbon economies.  
need to consider the carbon emission costs of different transportation modes to choose the most cost-effective path. Therefore, different low-carbon economies were considered to analyze the result of four low-carbon policies in multimodal transport path selection. Tables 6 and 7 record the different target values as well as transportation options under various low-carbon regulations.  
As can be seen from Table 5, A (mandatory carbon emission) has the lowest economic cost, which is because mandatory carbon emission policies usually set strict carbon emission limits. The policy of carbon taxation has the most direct and significant impact on transportation costs. This is because the carbon tax policy directly imposes economic penalties on the carbon emission behavior of enterprises and increases their operating costs. In terms of path selection, Model A minimizes the transportation path because carbon emission is a mandatory constraint. The three policies of a carbon tax, carbon trading and carbon offset policy multimodal transportation path selection impact are not significant; the results are mainly dominated by economic factors, suggesting the path with the lowest overall expense.  
From the proportion of various modes of transportation in Table 6, the road transportation mode accounted for 34.8% of the proportion, railroad transportation accounted for 47.8% of the proportion and air transportation accounted for only 17.4%. That is, the multimodal transportation scheme focuses on choosing railroad for transportation, which fully shows that railroad transportation has significant advantages in reducing carbon emissions and costs, while air transportation exists only in a few nodes. However, when choices are made at the nodes, less air transportation is chosen for intermodal transportation because its costs and carbon emissions are higher than the first two modes of transportation. Therefore, although road transport has a bright spot in terms of flexibility and convenience, it lacks advantages in long-distance transport compared with railroads, which are more advantageous in dealing with the path optimization problem under different low-carbon policies and can better balance the objectives.
## 6 Conclusions
### Language Theoretical Considerations  
In addition to the applications, extending the concept of context-free grammars also gives rise to interesting questions in the field of formal language theory. The most obvious of these questions is if adding in length-dependencies changes the class of languages that can be generated. We have already been able to show this (For a simple example take the grammar with the productions \(S\to A\): \(A\to a\mathcal{A}\mid\), with the first production restricted to lengths which are perfect squares. It generates \(\{\sigma^{n^{2}}|n\in\mathbb{N}\}\) which is not context-free), leading us to the follow-up question what the properties of this new class of languages are. First results from this direction of research have been presented at a workshop ([17]) and a paper presenting more results is currently in preparation.
## 4 Simulation Results
### Simulations Varying the Parameters \(\sigma_{\chi}\)  
Some simulations were carried out varying the parameters \(\sigma_{z}\), \(\sigma_{y}\) and \(\sigma_{x}\) in the range \([0.1,0.01]\), and fixing the fractional order to \(\beta=0.5\), in order to analyze the behaviour of the quadrotor system when the control parameters are given different values; the parameter \(\sigma_{\chi}\) was selected since the closed-loop behaviour is more sensitive to changes in \(\sigma_{\chi}\). From the results obtained (Figures 17-19) one can observe that with lower values of \(\sigma_{z}\), \(\sigma_{y}\) and \(\sigma_{x}\), the chattering in the position error signals is reduced.  
With respect to Euler Angles, the simulations show some chattering with higher values of \(\sigma_{\chi}\) while the disturbance does not significantly affect the Euler Angles; the disturbance appears at second \(13\) and it is kept for the rest of the simulation, the errors in Euler Angles are shown in Figures 20-22. It can also be noticed that the control acts in the vicinity of the second \(13\) where the correction needs to be made.  
Figure 16: (**a**) Control signal \(u_{1}\) with \(\beta=0.5\); (**b**) Control signal \(u_{1}\) with \(\beta=0.9\).  
Figure 15: (**a**) Yaw Torque with distinct \(\beta\)’s; (**b**) Yaw Torque with \(\beta=0.5\).  
Figure 19: Errors in z with distinct \(\sigma_{\chi}\).  
Figure 17: Errors in x with distinct \(\sigma_{\chi}\).  
Figure 21: Errors in \(\theta\) with distinct \(\sigma_{\chi}\).  
Figure 22: Errors in \(\psi\) with distinct \(\sigma_{\chi}\).