esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Indicator Weighting
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Country** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** \\ \hline Brunei Darussalam & 0.348 & 0.479 & 0.916 & 0.943 & 0.639 & 0.703 & 0.028 & 0.000 \\ Cambodia & 0.048 & 0.219 & 0.347 & 0.514 & 0.000 & 0.388 & 0.837 & 1.000 \\ Indonesia & 0.670 & 0.732 & 0.658 & 0.640 & 0.605 & 0.395 & 0.008 & 0.082 \\ Lao PDR & 0.000 & 0.000 & 0.282 & 0.000 & 0.038 & 0.000 & 0.000 & 0.039 \\ Malaysia & 0.626 & 0.730 & 0.817 & 0.878 & 0.659 & 0.000 & 0.000 & 0.236 \\ Myanmar & 0.088 & 0.018 & 0.000 & 0.590 & 0.159 & 0.691 & 0.433 & 0.159 \\ Philippines & 0.364 & 0.542 & 0.578 & 0.519 & 0.654 & 0.198 & 0.025 & 0.455 \\ Singapore & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.031 \\ Thailand & 0.758 & 0.748 & 0.608 & 0.786 & 0.718 & 0.920 & 0.351 & 0.315 \\ Vietnam & 0.428 & 0.568 & 0.623 & 0.730 & 0.464 & 0.690 & 0.590 & 0.404 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Under-risk utility matrix with risk coefficient = 0.88. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Country** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** \\ \hline Brunei Darussalam & 0.302 & 0.433 & 0.905 & 0.935 & 0.601 & 0.670 & 0.017 & 0.000 \\ Cambodia & 0.032 & 0.178 & 0.300 & 0.469 & 0.000 & 0.341 & 0.817 & 1.000 \\ Indonesia & 0.635 & 0.701 & 0.621 & 0.602 & 0.565 & 0.348 & 0.004 & 0.058 \\ Lao PDR & 0.000 & 0.000 & 0.237 & 0.000 & 0.024 & 0.000 & 0.000 & 0.025 \\ Malaysia & 0.587 & 0.699 & 0.795 & 0.863 & 0.622 & 0.000 & 0.000 & 0.194 \\ Myanmar & 0.063 & 0.010 & 0.000 & 0.549 & 0.123 & 0.657 & 0.387 & 0.123 \\ Philippines & 0.317 & 0.499 & 0.537 & 0.475 & 0.618 & 0.158 & 0.015 & 0.408 \\ Singapore & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.019 \\ Thailand & 0.730 & 0.719 & 0.568 & 0.761 & 0.686 & 0.910 & 0.304 & 0.269 \\ Vietnam & 0.381 & 0.526 & 0.584 & 0.699 & 0.418 & 0.656 & 0.549 & 0.357 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Normalized decision matrix. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Country** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** \\ \hline Brunei Darussalam & \(-0.216\) & \(-0.169\) & \(-0.025\) & \(-0.017\) & \(-0.115\) & \(-0.093\) & \(-0.339\) & \(-0.350\) \\ Cambodia & \(-0.331\) & \(-0.264\) & \(-0.217\) & \(-0.157\) & \(-0.350\) & \(-0.202\) & \(-0.050\) & 0.000 \\ Indonesia & \(-0.104\) & \(-0.084\) & \(-0.108\) & \(-0.114\) & \(-0.12  
the lower the population, the more the advantages of digital transformation are realized. As a result, secure internet servers, UHC service coverage index, and population are the three most essential indicators in this case study.
## 4 Conclusions  
This paper examined the influence of process design of thermosonication on the recovery of phenolic compounds from _Foeniculum vulgare_ using ethanol as solvent. Thermosonication-assisted extraction processes can reach high temperatures due to the temperature increase caused by acoustic cavitation and the external heat source. Higher \(\Delta\)T values were observed for thermosonication performed at 400 W. A working temperature of 60 \({}^{\circ}\)C decreased the intensity of the acoustic cavitation and promoted a decrease in the \(\Delta\)T values during thermosonication. The thermosonication carried out with 300 W and 60 \({}^{\circ}\)C extracted more phenolic compounds (3670 \(\pm\) 67 ug GAE/g), and antioxidant compounds measured by DPPH (1195 \(\pm\) 16 ug TE/g) and ABTS (2543.12 \(\pm\) 0.00 ug TE/g). The thermosonication extraction technique showed advantages compared to longer extraction techniques of percolation and Soxhlet. The thermosonication (300 W and 60 \({}^{\circ}\)C) in 20 min of extraction achieved a greater recovery of TPC than percolation (2702 \(\pm\) 4 ug GAE/g) and only 20% less than the Soxhlet (4849 \(\pm\) 353 GAE/g) techniques. Moreover, thermosonication showed lower energy consumption due to the shorter extraction time and the lower volume of solvent used. Thus, thermosonication can be used as an innovative technique for the recovery and availability of bioactive compounds.  
Conceptualization, E.K.S.; methodology, A.C.M.U. and M.M.S.; practical work and acquisition of data, A.C.M.U. and M.M.S.; analysis and interpretation of data, A.C.M.U., M.M.S., and E.K.S.; writing--original draft preparation, A.C.M.U. and M.M.S.; writing--review and editing, E.K.S.; visualization, E.K.S.; supervision, E.K.S. and M.A.A.M.; project administration, E.K.S. and M.A.A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[PERSON] thanks CAPES (financial code 001) for the scholarship. [PERSON] thanks CNPq (141110/2018-0) for her doctorate assistantship. [PERSON] thanks CNPq (309825/2020-2) for her productivity grant.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 2 Materials and Methods
### Network Performance Testing  
In order to test the performance of the segmentation network, \(k\)-fold cross-validation was used [26]. The cross-validation method divides the database into \(k\) approximately equal subsets, one part of which is used for training and validation, and the other for testing. The network is then trained \(k\) times, each time using different subsets for training, validation, and testing. In this way, it is achieved that each data is repeatedly found in each set and the impact of imbalanced data split is minimized. Two different evaluation approaches have been implemented, and both are based on \(5\)-fold cross-validation, with one fold being used for testing, one for validation, and the rest for training. The first approach is based on dividing the data into five equal parts so that the images showing each of the tumor categories are equally represented in all parts. This approach is hereinafter referred to as  
Figure 1: Image example for each type of tumor in each of the planes from the database [25]: **(a)** Normalized MRI images with bordered tumors; **(b)** Corresponding tumor masks.  
record-wise cross-validation. The second approach used in testing network performance is based on dividing the data into five equal parts where images from one subject can be found in only one of the parts. Thus, each of the parts contains images of several subjects, regardless of the tumor category. Hereinafter, this approach is called subject-wise cross-validation. A second approach has been implemented to test the network's generalization ability in medical diagnostics [27]. The ability to generalize the network in clinical practice is the network's ability to accurately diagnose data on subjects about which there is no data in the network training process. Therefore, data on individuals in the training set should not appear in the test set. If this is not the case, complex predictors can recognize the interrelationship between identity and diagnostic status and produce unrealistically high classification accuracy [28].  
In order to compare the performance of the new network and other existing methods, the network was tested without the use of \(k\)-fold cross-validation, i.e., by training the network only once (one test). One test method was used with record-wise and subject-wise approaches for data dividing. When training the network with one test method, data splitting is the same as with the cross-validation methods, i.e., 20% of the data are used for testing, 20% for validation, and 60% for training.  
All the methods mentioned above for testing the network were performed on original and augmented datasets. In total, the network was tested using eight tests, combinations of two evaluation methods (5-fold cross-validation and one test), two data division methods (record-wise and subject-wise), and two training datasets (original and augmented).
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 5 Experimental Results and Analysis
### Feature Selection
The heart has to exert significantly more effort to sustain an adequate blood supply. Prolonged impairment due to myocardial fatigue can gradually elevate the risk of heart failure. The highest heart rate reflects the heart's function in extreme states. If it remains abnormal for an extended period, it indicates that there is insufficient cardiac reserve function or potential lesions present. In such a case, the heart is unable to effectively meet the body's stress requirements, thereby impacting its normal function. With the increase in age, the structure and function of the heart naturally deteriorate, and problems such as weakening myocardial elasticity and valve calcification gradually appear. At the same time, the elderly often suffer from various chronic diseases, which further damage the heart, making age closely related to heart failure. Abnormal resting blood pressure, whether it is hypertension or hypotension, will disturb the normal pressure load balance of the heart. Hypertension increases the ejection resistance of the heart. Prolonged hypertension can lead to myocardial hypertrophy over time. Meanwhile, hypotension can result in inadequate perfusion of the heart. In the long term, both the increased ejection resistance due to hypertension and the insufficient perfusion caused by hypotension can predispose an individual to heart failure. The paper further tries the influence of feature combination on the prediction results, mainly from three aspects: the characteristics related to chest pain, the combination related to cardiac blood supply, and the combination related to cardiac electrophysiology. The heart is a combination of heart and heart and heart. The heart is a combination of heart and heart. ical activities. The following Table 2 shows the experimental results of accuracy under different combinations. When predicting heart failure, the overall quality of the model highly depends on the quality of the selected key feature group. Through many experiments, it was found that among many feature combinations, the combination according to the characteristics related to chest pain is the worst in predicting heart failure. Among the combinations related to heart blood supply, the combination of Oldpeak, ST_Slope, RestingECG, and ExerciseAngina has shown good results. The combination related to cardiac electrophysiological activities, such as RestingECG, MaxHR combined with ExerciseAngina, and Oldpeak, ST_Slope combined with RestingECG, also performed well. However, these combinations are not as good as all-feature combinations in overall performance. This clearly demonstrates that the comprehensive feature set encompasses more crucial and extensive information during the prediction of heart failure. Such information plays a pivotal role in enhancing the overall performance of the model. Therefore, in order to ensure that the model can achieve the best prediction effect, this paper decided to keep all the features in the feature selection.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 3 Model of Broadcast Assisted Transmission Based IA Application in VANETs
### The Model of Data-Driven Assisted Transmission  
In contrast to other types of information, safety related information in VANETs is open to all vehicles. Furthermore, during the same scheduling frame, information of a vehicle remains unchanged. Therefore, when a vehicle receives information from others, it could re-encode safety related information together and broadcasts to others in one session to improve the efficiency of information propagation. Based on these characteristics, in this subsection, we design a data-driven assisted transmission model for safety related information.  
Transmission without data-driven assisted is illustrated in Figure 6. Taking the session sent from vehicle \(E\to D\) as an example, it can be delivered or relayed by base station and vehicle \(G\). When vehicle \(E\) and \(B\) broadcast sessions simultaneously, vehicle \(D\) would receive two messages at same time slot. Once the number of received signals exceeds number of vehicle \(D\)'s freedom degrees, it will not be able to obtain the valid information. Therefore, in order to avoid collision, messages of vehicle \(E\) and vehicle \(B\) should be transmitted in different time slots. Since vehicle \(G\) also needs to transmit data to vehicle \(D\), there should be one more time slot be assigned.  
As shown in Figure 7, we can arrange one time slot for session \(E\to G\) with session \(B\to I\) and another time slot for \(G\to D\). After vehicle \(G\) receives message from vehicle \(E\), it can merge its own information with other vesicles. Although vehicle \(D\) get message of \(E\) one time slot later, there are only two time slots are occupied for all network.  
Nevertheless, when vehicles adopt assisted transmission, it is necessary to ensure safety related information is delivered in time. In this paper, we choose minimum hop counts donated as \(hcount\) to be the upper limit of hops, then we get  
\[\sum_{i=1:N,j=1:N}rot_{i,j}(f)\leq hcount,\ (1\leq f\leq F) \tag{19}\]  
\(rot_{i,j}(t)\) is feasible routing path of session \(f\). If a routing path bears a data relay task, the following formula is met  
\[rot_{i,j}(f)\leq\sum_{t=1:K}\lambda_{\ i,j}(t),\ (i=Src_{f},1\leq f\leq F) \tag{20}\]  
Figure 6: Example of V2I without Assisted Transmission.  
Figure 7: Example of V2I with Assisted Transmission.  
For session \(f\), its initiator is start of route, then we get  
\[\sum_{j=1:N}rot_{j,i}(f)\leq 0,\ (i=Src_{f},1\leq f\leq F) \tag{21}\] \[\sum_{j=1:N}rot_{i,j}(f)\geq 1,\ (i=Src_{f},1\leq f\leq F) \tag{22}\]  
For relaying vehicles, we get the following constraint:  
\[\sum_{j=1:N}rot_{i,j}(f)=\sum_{j=1:N}rot_{j,i}(f),(i\
eq Src_{f},i\
eq Des_{f}) \tag{23}\]  
As destination vehicles, there must be some vehicle that broadcasts message to them, then we have  
\[\sum_{j=1:N}rot_{j,i}(f)\geq 1,\ (i=Des_{f},1\leq f\leq F) \tag{24}\]  
In order to avoid routing loop at destination vehicle, we have:  
\[\sum_{j=1:N}rot_{i,j}(f)\leq 0,\ (i=Des_{f},1\leq f\leq F) \tag{25}\]
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 4 Analysis
### Attacks Aiming to Recover the Private Key  
In this attack, the adversary tries to obtain one of the private keys \(k\), \(r_{2}\), or \(p\) that were used to create a valid signature. Signature parts \(s_{1}=m^{k_{s}}\mod n^{\prime}\) and \(s_{2}=m^{r_{2}\times p}\mod n^{\prime}\) will be sent through an insecure channel, so we suppose that an adversary can obtain both \(s_{1}\) and \(s_{2}\).  
In a successful attack, we assumed that the adversary can have \(s_{1}\) and \(s_{2}\), either by the interception or by being a malicious receiver. We have \(s_{1}=m^{k}\); if the adversary is a malicious receiver, he/she can also know \(m\) by decrypting \(c=Enc(m)\) using its private key. Therefore, to recover \(k\), the adversary has to solve the Discrete Logarithm Problem (DLP).  
After computing \(k\), the adversary can extract \(r_{2}\times p\) using the public keys \((pk,r_{1})\), where  
\[r_{2}\times p=(pk-k)\div r_{1} \tag{12}\]  
Now, the adversary has \(k\) and \(r_{2}\times p\); thus, he/she can create a valid signature. The same for \(s_{2}=m^{r_{2}\times p}\), the adversary must solve the DLP in order to recover directly \(r_{2}\times p\). Otherwise, the adversary must solve the factorization problem in order to obtain \(p\) and then \(k\) from the public \(n\).  
We would like to point out here that \(r\) does not have to be secret. We assumed this in the proposed scheme just to increase the level of security; this means that both \(r_{1}\) and \(r_{2}\) can be known for everyone. Suppose, for example, that the adversary has \(r_{1}\) and \(r_{2}\); he/she still has to know \(k\) and \(p\) in order to create a valid signature (\(s_{1}=m^{k_{s}}\) and \(s_{2}=m^{r_{2}\times p}\)).  
Knowing \(r_{1}\) and \(r_{2}\) does not affect the robustness of the public key (\(pk=k+r\times p\)), nor does it give any information about \(k\) or \(p\). Likewise, knowing \(r_{2}\) does not affect the confidentiality of \(s_{2}\), nor does it give any information on the plaintext \(m\) or the private keys (\(k\), \(p\)). Assuming that the receiver has \(r_{2}\), after decrypting \(c\) and computing \(m\), he/she will have \(m^{r_{2}\times p}=m^{r_{p}}\), where \(m^{\prime}=m^{r_{2}}\); he/she still has to solve the DLP to obtain \(p\). Therefore, we have hidden \(r\) only because there is no need to make it public.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed with the statistical computing software R (v 4.1.2., R Core Team, Vienna, Austria). The normality of the variables was analyzed using the Shapiro-Wilk test, and homoscedasticity was analyzed with the Levene test. Means and standard deviations were used for basic descriptions. For the comparisons between groups (match periods, 15' intervals, or play positions) of continuous variables, non-parametric tests for independent variables, the Mann-Whitney U test and the Kruskal-Wallis test, were used, and to calculate the effect size, Cohen's d and the Eta-square index, were used. For multiple comparisons, the Wilcoxon post hoc test with Bonferroni correction was used. All reported \(p\) values are based on the two-tailed test and the level of statistical significance for all tests was set at 95%.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### Nanofluids
#### 3.3.3 pH  
The pH of the NFs affects the aggregation degree of the NPs and, consequently, the thermal conductivity. The isoelectric point of the value of pH occurs when the amount of positive and negative ions is the same. Some studies reported an increasing enhancement of the thermal conductivity with increasing pH until the isoelectric point, which is then followed by a decrease. This was attributed to the increase of electrical charge on the surface of the NPs, leading to greater electrostatic repulsion [5; 26]. [PERSON] et al. [57] and [PERSON] et al. [58] reported an increase in thermal conductivity with pH of ethanol-based alumina NFs until a maximum value with pH value near 6.0, which is then followed by a decrease with increasing pH. [PERSON] et al. [59] registered similar results with water-based copper and alumina NF, being the maximum thermal conductivity value obtained when the isoelectric point was reached. Other studies showed lower values of thermal conductivity in the isoelectric point, i.e., the repulsive forces among particles are zero, making particles aggregate under this pH value [60]. The experimental work developed by [61] also shown that the pH influences the zeta potential, particle size distribution, rheology, viscosity, and stability, and all these factors affect the thermal conductivity of the NFs containing ZrO\({}_{2}\) and TiO\({}_{2}\) NPs.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.13. Abhydrolase (\(\alpha\)/\(\beta\) Hydrolase) Superfamily  
The \(\alpha\)/\(\beta\) hydrolase superfamily is a diverse group of hydrolytic enzymes that may differ in their catalytic function but that share a common fold with a conserved loop bearing catalytic triad [130]. The catalytic triad involves serine, glutamate/aspartate, and a histidine amino acid residue, and participates in the nucleophilic attack on a carbonyl carbon atom. The \(\alpha\)/\(\beta\) hydrolase fold includes proteases, lipases, peroxididases, esterases, epoxide hydrolases and dehalogenases [131]. Unlike other proteins, the core of the protein belonging to this superfamily has an \(\alpha\)/\(\beta\) sheet composed of 8 \(\beta\) strands lined to 6 \(\alpha\) helices [130; 132].
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 4 Experimental Results and Comparisons
### Run-Time Analysis  
We compare the running time of our method with the other approaches on UCSD dataset. Different image resolutions affect the time required to process each frame. Specifically, the resolutions are \(158\times 238\) for the UCSD Ped1 dataset and \(240\times 360\) for the UCSD Ped2 dataset. Table 3 reports the average running time of each frame during the test phase. Since the original implementations of the other methods are not publicly avilable, we report the running times taken from [21; 36], specifying the working environment. Inevitably, the improvement in terms of accuracy obtained with deep neural network comes at a price of an increased computational cost. Compared with AMDN [21], which also adopted a deep architecture, the computational speed of our method is faster. The main reason is that our method benefits from fully convolutional neural network that can extract the feature representations of all the receptive fields in the input frames at one time, and AMDN crops the input into patches and the same feature extraction process is repeated among these patches. As shown in Table 3, with a GPU, our method has great time efficiency in terms of anomaly detection.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 4 MASU Evaluation Using Simulations
### Simulation Results
#### 4.2.3 Discussion of the Simulation Results  
All previous results suggest that MANET networks can provide an interesting alternative to support pervasive data sensing in dynamic scenarios, since they guarantee higher node presence values (especially, Consumers since the Control Tier of the framework already deals with the presence of Producers), regardless of the mobility patterns considered. This fact means that higher data availability is possible using this types of networks since Consumers can receive data from Producers regardless of the fluctuations in the communication links and the mobility conditions of the nodes.  
The explanation for this high data accessibility can be found in Figure 12. It shows that more than 50% of the time that the Producer is present in the network it is located more than one hop away from the Consumers. There are even cases where the Consumers receive data from the Producer when it is at four or five hops away.  
These benefits of MANETs come at the expense of a higher number of messages that have to be transmitted over the network, as represented in Figure 13. This figure shows the average number of messages that are transmitted over the network for different network infra  
Figure 11: Presence of the Producer and the Consumers for different mobility patterns using an ideal Control Tier.  
Figure 12: Percentage of presence of the Producer versus the number of hops in MANET networks.  
messages is significantly higher in MANETs due to the fact that the data messages transmitted from the Producer to the Consumers are UDP unicast (1 to 1). For this reason, the Producer has to send one message to each one of the Consumers that are within the network. Thus, the number of data messages has a direct correlation with the number of active Consumers. By contrast, in AP and T2T networks the data messages are UDP broadcast (1 to all) so the number of messages has a positive correlation with the number of active Producers.  
The higher cost related to the number of messages in MANETs is compensated by the fact that these types of networks allow that a higher number of units could receive the information sensed by Producers. However, if the number of Consumers is too high, this cost could lead to network congestion, which would also suppose that the MASU framework will not be able to work properly under those conditions. As a result, it would be necessary to find a solution to deal with scalability problems related with the high number of messages sent through the network in MANET.