esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Illustrative Example
### Seismic Risk and Mitigation Strategy  
The seismic risk of transportation network described by Equation (2) is determined by \(p(\lambda)\) and \(\varphi(\lambda)\). \(p(\lambda)\) can be calculated by the ground-motion intensity and vulnerability parameters of bridges, and \(\varphi(\lambda)\) is calculated by assuming that the control value is \(Q_{c}=0.9\) for MMI = 7.0 in this example. With the assumption, the initial failure probability of each network case is: 75.33% for Case 1, 21.31% for Case 2, 53.45% for Case 3, and 49.90% for Case 4. For risk mitigation, the retrofit behavior of a bridge can be considered by scaling the median value of ground motion intensity in its fragility function [45,83]. In the example, the magnifications for medians are respectively assumed to be 155%, 175%, and 204% for slight, moderate, and extensive damage states, which corresponds to the full steel jacketing reinforcement [84,85].  
According to the retrofit decision model in Section 2, we calculate the post-earthquake network functionality of all possible damage scenarios to obtain the real optimal solution of Equation (5). In this example, the total number of enumerated scenarios is \(4^{10}=1,048,576\), considering 4 damage levels (i.e., none, slight, moderate, and over extensive) for each bridge based on Table 1. For efficient decision-making on retrofit strategy, we compare the performance of four bridge importance ranking approaches. The first approach is based on the betweenness centrality (BC), which is defined as the sum of the proportions of the shortest paths passing through a bridge to the total number of shortest paths between each node pair. The larger BC value indicates a stronger role of the bridge in the whole network topology. The second approach is based on the removal effect (RE) of individual bridges on the network functionality. The lower the value of residual functionality, the higher the importance of the bridge to maintain the initial functionality of the network. The other two approaches are based on MCS as introduced in Section 3. In this example, the sample size is set to be \(N_{s}=10000\), the computational cost of which is about \(1/100\) of the enumeration method. This number is selected to simulate real engineering applications while ensuring the diversity of samples. Specifically, the third approach is based on the individual importance (MI) of bridges according to Equation (7), and the fourth approach is based on the joint importance (JMI) of bridge sets calculated by Equation (10). In the following, we will analyze and discuss the mitigation performance and strategy accuracy of the four importance ranking approaches in detail.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Pharmacological Strategies
#### 5.3.2 Fibrinolytic Co-Therapy  
Cancer patients often experience a hypercoagulative state, which exacerbates mortality and morbidity. This condition can lead to vascular occlusion within tumors, driven by the overexpression of proinflammatory cytokines, platelet activation, and leukocytes overexpressing tissue factors. These factors not only cause vascular blockages but also contribute to tumor heterogeneity in the EPR effect, resulting in poor drug delivery [189].  
Fibrinolytic therapy, which involves the dissolution of fibrin clots in blocked vessels, has emerged as a potential strategy to improve tumor vasculature and enhance drug delivery [190]. For example, [PERSON] et al. pretreated A549 tumor xenograft mice with tissue plasminogen activator (tPA), a thrombol drug, before administering paclitaxel-loaded nanoparticles. The pretreatment with tPA reduced fibrin deposits at tumor vessel walls, leading to higher accumulation and deeper penetration of 115 nm nanoparticles compared to saline-treated groups. This study demonstrated the potential of fibrinolytic therapy to enhance the EPR effect and improve nanomedicine delivery [191].  
Another study investigated the impact of tPA pretreatment on the biodistribution and penetration of Doxid(r) (liposomal doxorubicin) in tumors. While tPA pretreatment did not significantly affect the biodistribution of Doxid(r), it enhanced the penetration of the drug into tumors, further supporting the role of fibrinolytic therapy in improving drug delivery [192].  
Despite its potential, fibrinolytic co-therapy faces challenges such as the short half-life and low specificity of tPA. Addressing these limitations is critical for the broader application of fibrinolytic therapy in cancer treatment [190].
## 2 Related Work
Firstly, we relax the definition of what is a common feature, i.e., a common feature is supported by at least two CO frameworks. Secondly, we also determine unique features that are only supported by one CO framework. Thirdly, we give a systematic and exhaustive overview of all common and unique features whereas the work of Kratzke et al. presents meta-models of configuration languages that encompass concepts to support expressing cluster or application configurations that are commonly supported by all CO frameworks; in other words, our work is complementary as it can be used to refine and update the meta-models with support for common features that have not been discovered by Kratzke et al. Finally, we do not only study common features, but we also study the maturity of these common features to distinguish between stable features and those features that are relatively immature and subject to change; additionally, we also discuss the risks of feature deprecation. In summary, to our knowledge, this is the first work that presents a detailed and exhaustive commonality and variability analysis among popular container orchestration frameworks and that studies the maturity frameworks as well as the risks of feature deprecation. The systematic approach of processing the high-quality documentation of the frameworks ensures that no features of importance are overseen in this work.
## 5 Use of IRT in Livestock Production  
The infrared technology is constantly evolving, expanding its use, and being used as a valuable tool for diagnosing pain and disease in animals and humans. In livestock production, it is a fast and efficient tool providing information on animal health without the need for physical contact [52]. In veterinary medicine, thermography can be used as a diagnostic tool to detect changes in tissues, can be a complementary method to other diagnostic methods in the physical examination, or can be used for routine monitoring of animals to detect subclinical problems [120]. One of the main functions of infrared thermography is to detect changes in body temperature and blood flow based on visualizations of thermographic changes [121; 122]. The advantage of IRT is that it can locate the exact site of injury or inflammation and diagnose the disease before clinical symptoms appear [123], which usually develop two weeks after changes appear on thermographic images [13]. Infrared thermography was first used in veterinary medicine in 1965 in horses [124] and over time has become a widely used technique that can be applied to farm, wild, laboratory animals and animals kept in captivity.  
Focusing on livestock, the infrared technology can be used, for example, to detect mastitis in cows [51] and sheep [125], lameness in dairy cows [126] and sheep [127], ectoparasites in cattle [128; 129], measure surface temperature in ewes during the estrous cycle [130] or detect estrus and ovulation in cows [131], measure febrile states after vaccination in pigs [132], diagnose respiratory diseases in pigs [133] and calves [134], assess body surface temperature in sows and piglets [135], thermographically examine the musculoskeletal system during race training in young thoroughbreds [136], assess stress in broilers [137], detect responses to handling stimuli in cattle [138], assess semen quality in bulls [139]; it can be used as an indicator of meat quality and also to evaluate stressors acting on animals before slaughter [140]. All in all, IRT can be used to assess the welfare of livestock [141].
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
## 3 Results and Discussion
### Effect of Initial Metal Concentration on the Pb\({}^{2+}\)-Binding Ability of FucoPol  
The metal-binding performance of FucoPol was evaluated in a range of different initial Pb\({}^{2+}\) concentrations, 3 to 100 mg/L (Figure 2). This range was chosen since it was described that the conventional methods are inefficient for removal of Pb\({}^{2+}\) concentrations below 100 mg/L [16; 17].  
The results presented in Figure 2 proved that the initial concentration of Pb\({}^{2+}\) had a significant effect on the Pb\({}^{2+}\) uptake efficiency of FucoPol. For higher Pb\({}^{2+}\) concentrations, metal uptake by FucoPol improved greatly. When the concentration of metal was augmented from 3 mg/L to 48 mg/L, the specific Pb\({}^{2+}\) uptake increased approximately 28 times, reaching a \(q\) value of \(108.0\pm 5.0\) mg/gEPS. However, for metal concentrations of 100 mg/L, the specific uptake had no further significant increase (\(110.4\pm 9.9\) mg/gEPS), probably due to the saturation of the binding sites of the polysaccharide [4; 23]. For the highest metal concentrations tested (48 and 100 mg/L), metal uptake reached a plateau value (\(q_{\rm max}\)) and FucoPol was saturated, therefore the overall metal removal decreased drastically from the highest value of 93.8 \(\pm\) 5.3% achieved with 10 mg/L of Pb\({}^{2+}\) to \(14.1\pm 1.3\)% with 100 mg/L of Pb\({}^{2+}\). A similar trend was reported for the EPS produced by a mixed culture of _R. radiobacter_ F2 and _B. sphaieicus_ F6: when the initial Pb\({}^{2+}\) concentration increased from 0.1 to 5 mg/L, metal uptake increased 44.5 times, however, metal removal was maintained above 97% for concentrations in the range of 0.1-2 mg/L and decreased for 90% for higher concentrations [38].
## 2 Fungal Pigments Found on Paper
### Carotenoids  
Despite the fact that fungi are non-photosynthetic organisms and carotenoids are not as widely distributed in them as in plants [2], some caroten hydrocarbons have been found in several fungal species [67; 94]. Carotenoids are naturally occurring pigments that are produced in an isoprenoid pathway [95]. Thus, they are comprised of isoprene units that contain light-absorbing conjugated double bonds, forming an aliphatic polyene chain and providing characteristic yellow, orange, or reddish colors [94]. Their essential biological role is in the protection of an organism from oxidative stress and membrane stabilization [94]. It has been suggested that by growing a strain of _Penicillium_ sp. under high oxidative stress and in the deficiency of antioxidants, higher carotenoid colorant yields and elevated sclerotial biomass would be obtained [96]. They are also important precursors to essential metabolites such as vitamin A [95].  
It has been shown that \(\beta\)-carotene production, as the most abundant carotenoid in nature, seems to be present in species such as _Aspergillus giganteus_[97], _Cercospora nicotianae_[98], _Penicillium_ sp. [96], and _Aschersonia aleyroides_[99].  
In the review written by [PERSON] et al. [2], it has been reported that of 80 different colorants produced by fungi identified as paper colonizers, approximately 96% were of a polyketide nature and only around 4% were carotenoids. They have found only three different carotenoids in the literature that were mentioned in the context of colorants produced by fungi identified on paper: neurosporaxanthin, \(\beta\)-carotene, and sporopolenins. Moreover, the most common stains produced by fungi on paper have been found to be brownish and black. It has been suggested that these stains can be a mixture of different colorants. Specifically, the hallmark of _A. niger_ is the formation of black or dark brown conidia, which appears to be an outcome when dark brown melanins combine with hexahydroyl pentacyclic quinoid green pigments [100]. Additionally, the color of the fungal stains on paper may be a result of the oxidation process of a colorant in the given medium, which can further lead to the darkening of the stain. This can be seen in the brown pigment sporopolenin, an oxidative polymer of yellow \(\beta\)-carotene [68].  
For full insight into the chemical structure of the fungal pigments, refer to [PERSON] et al. [2].
## 7 Role of Artificial Intelligence in the Development of Integrated Photonics
Through experimental validation, the framework's effectiveness was showcased by training ANNs to model strip WGs and chirped Bragg gratings (BGs). This approach employed a minimal set of straightforward input and output parameters pertinent to photonic circuit designers. Once trained, these ANNs significantly reduce the computational cost, surpassing traditional design methodologies by a factor of 10,000. To demonstrate the capabilities of our innovative design paradigm, both forward and inverse design tools powered by the ANN were demonstrated. Utilizing these tools, several integrated BG devices were designed and fabricated for practical use in photonic circuits. The ANN's predictions align closely with experimental measurements, achieving high accuracy without the need for any post-fabrication training adjustments. Figure 8b-e provides a comparison between the ANN's predictions and the measured data, highlighting the accuracy and reliability of the neural network model in predicting the performance of the fabricated devices [172]. Figure 8: (**a**) The process overview describes the new design methodology as follows: Dataset Generation: Initially, datasets are created using traditional numerical methods. Neural Network Training: The dataset is then used to train a neural network to characterize the specific device. Iterative Modeling: Designers often iterate between dataset generation and neural network training  
until a satisfactory model is developed. Design Applications: Once the model is finalized, it can be applied to various design tasks, such as circuit simulations and inverse design solutions. Fabrication and Validation: The designed devices are fabricated to validate the model's predictions. Model Sharing and Extension: The validated model can then be shared and further extended. This methodology ensures the development of accurate and efficient models for integrated photonic circuits [172], (**b**) comparison of fabrication data with corresponding ANN predictions: (**b-e**) show the measured transmission responses for gratings with period chirps of 5 nm (**b**), 10 nm (**c**), 15 nm (**d**), and 20 nm (**e**) [172].
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 2 Related Works
### Energy-Efficient Transmission Protocols  
The routing protocol in [20] uses the history of successful transmission to the neighbor nodes, the link quality, the residual energy and the buffer space to select next-hop relay nodes. For this reason, the protocol has good next-hop node selection and energy consumption performance. The work in [21] divides the nodes into different levels based on their power level. The nodes can adapt their transmission power based on the network conditions. The power levels and the residual energy of nodes are used to choose the next-hop relay nodes. PULRP [4] and E-PULRP [22] layer the network according to the distance between the sink and nodes. When a sender broadcasts a control packet with its information, the receiver in the lower layer will respond with an ACK. Once the potential relay node is identified, all other nodes can go back to sleep. In this case, the multi-copy problem can be avoided and thus energy can be saved. EDBF [23] uses a depth-based forwarding mechanism to select next-hop with three parameters: depth of the forwarding node, residual energy and forwarding quality. It reduces communication overhead, but in determining forwarding quality, the end-to-end delivery ratio does not improve significantly.  
DCC-MAC [24] divides the channel into a common control channel (CCC) and multiple data channels. Nodes in DCC-MAC can adjust the bandwidth of their control channel adaptively by flexibly selecting proper data channels to extend their control channel. It can significantly reduce the collision probability among control messages, thus further reducing data packet collision and energy consumption. However, control messages taking up the data channel will result in increased data transmission time. MC-UWMAC [25] operates on a single slotted control channel to avoid the missing receiver problem and multiple data channels to improve the network throughput. To make it collision-free, MC-UWMAC proposes a grid-based slot assignment procedure on the common slotted control channel and a quorum-based data channel allocation procedure. The channel allocation process requires only the location information of the nodes themselves and does not require the interaction of information between nodes. Further, MC-UWMAC allows multiple data communications along with handshaking on the common control channel to take place at the same time. These two approaches reduce the exchange of control packets and hence the network energy efficiency is improved. However, this multi-channel mode will increase transmission delay underwater, which is not suitable for transmission-intensive scenarios.
# Machine Learning-Assisted Secure Random Communication System
## 6 Results and Discussion
### Unintended Communication  
We also analyzed the ML-RCS from the perspective of an ILR to determine its strength against potential eavesdropping. In this section, we model the ILR as a highly capable and sophisticated eavesdropper by depicting the worst-case scenario for the T and LR. As shown in Figure 4, we assume that the ILR utilizes the same DT-based classifier. However, the ILR does not have access to the \(\alpha\)-SNS\(\mathcal{D}\) to produce a pretrained DT similar to that developed by the T and LR for the intended communication. Therefore, the parameter settings of the SkaSNSK responsible factors (\(\alpha\), \(\Delta\beta\), and \(N\)) for the ILR are not the same as those in Table 2.  
Confusion Matrices  
The parameter settings for the ILR are given below in Table 4.  
It is important to note in Table 2 that the ILR must possess the actual pulse length (\(N\)) utilized by the T and LR to successfully retrieve the binary information hidden in the transmitted SkaSNSK signals. This is practically impossible, with a probability of \(\frac{1}{\infty}\). However, to analyze the ML-RCS under the worst-case scenario in this study, the ILR is  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Selected Values** \\ \hline \(\alpha\) & \((0,\,2]\) \\ \(\beta_{0}\) & \([-1,\,0)\) \\ \(\beta_{1}\) & \([0,\,1]\) \\ \(\Delta\beta\) & \([0,\,2]\) \\ \(N\) & \([1,\,\infty]\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings responsible for ILR.  
Figure 10: BER vs. MSNR\({}_{\text{dB}}\) performances for the FT, MT, and CT configurations of the ML-RCS; total transmitted bits = 1000. The figure shows that the FT is the best configuration for the cart classifier while retrieving binary information from the SkaSNSK.  
assumed to use a pulse length of '\(N+1\)', which is identical to the actual pulse length \(N\). With the above assumption, a confusion matrix for the ILR is generated, as shown in Figure 11. Therefore, the ILR cannot correctly predict \(\hat{\beta}\) even in the most favorable scenario for unintended communication. Even when \(\Delta\beta=2\), the true positive rate (TPR) for \(\hat{\beta}\) does not exceed 50%, resulting in a false negative rate (FNR) above 50%. The TPR and FNR achieved by the ILR are impractical for retrieving a single binary information bit from the communication established between the T and LR in the ML-RCS. This finding indicates that communication is sufficiently secure for the intended purposes.
## 2 Materials and Methods
### Metaheuristic Optimization Algorithms
#### 2.2.3 Cuckoo Search Algorithm  
Known for their aggressive reproduction strategy [21], cuckoos not only lay their eggs in other birds' nests, but they may also remove others' eggs with the purpose to secure their generation. This specific cuckoo's brood parasitism reduces the possibility of their eggsbeing abandoned and thus increases the cuckoo's reproductivity. The interesting breeding behaviour is implemented in the CS metaheuristic algorithm for resolving optimization problems [21]. The CS algorithm assumes the eggs in the host's nest to be a set of potential solutions to an optimization problem, while the cuckoo egg itself is interpreted as a new one. At each iteration of the algorithm, these new and likely better solutions are used to achieve a very good final solution to the problem.  
The pseudocode of the CS algorithm based on [21] is presented in Algorithm 3.  
```
1:begin
2:Define
3:Number of nests \(n\)
4:Switching parameter \(p_{a}\)
5:Objective function\(f(x)\), \(x=(x_{1},\ldots,x_{d})^{T}\)
6:Generate initial population of \(n\) host nests \(x_{i}\) (\(i=1,2,\ldots,n\))
7:while(\(t<\) MaxGeneration) or (stop criterion) do
8:Get a cuckoo randomly by Levy flights
9:Evaluate its quality or fitness value \(f_{i}\)
10:Choose a nest among \(n\) (say, \(j\)) randomly
11:if(\(f_{i}<f_{j}\))
12:Replace \(j\) by the new solution \(i\)
13:endif
14:A fraction (\(p_{a}\)) of worse nests are abandoned
15:New solutions (nests) are built
16:Keep the best solutions, i.e. nests with quality solutions
17:Rank the solutions and find the current best
18:endwhile
19:Postprocess results and visualization
20:end begin
```  
**Algorithm 3** Pseudocode of CS algorithm