esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Results Analysis
### Key Strength Probability (\(P_{f}\))
#### 4.3.1 Behavior Analysis of \(P_{f}\)  
To show the behavior of \(P_{F}(t)\), the key strength probability as a function of \(t\) (the time interval elapsed since the activation of the key of a node), we use the RC5 cryptography algorithm [41], because, besides being a widely-used algorithm, it is used in TinySec [42], a security architecture for WSNs implemented in TinyOS [11].  
RC5 has a variable key size (0 to \(2040\) bits). The Distributed Computing Technologies, Inc. (a.k.a. distributed.net) [43] runs several brute force attack projects that aim to break RC5 keys using distributed computing. Some of these projects manage to achieve test rates (\(f\)) of more than 394 billion keys per second [43].  
Figure 2 presents how \(P_{F}\) varies with time \(t\) for different values of key strength \(s\). Notice that the greater the strength of a key, the more secure this key is, since its key space will be larger. On the other hand, \(P_{F}\) decreases as time goes on, since more keys are tested, which increases the chances of an adversary breaking the algorithm. Furthermore, note that when \(t\) increases, \(P_{F}\) decreases more steeply for smaller values of \(s\). This is because for keys with smaller strengths, a greater fraction of the total key space is tested for a given value of \(t\).
###### Abstract  
This paper focuses on the preparation of boron-containing phenolic resin (BPR)-derived carbon modified three-dimensional (3D) needlesled carbon fiber reinforced silicon oxycarbide (SiOC) composites through a simple precursor infiltration and pyrolysis process (PIP), and the influence of PIP cycle numbers on the microstructure, mechanical, high-temperature oxidation resistance. The electromagnetic wave (EMW) absorption properties of the composites were investigated for the first time. The pyrolysis temperature played an important role in the structural evolution of the SiOC precursor, as temperatures above 1400 \({}^{\circ}\)C would cause phase separation of the SiOC and the formation of silicon carbide (SiC), silica (SiO\({}_{2}\)), and carbon. The density and compressive strength of the composites increased as the PIP cycle number increased: the value for the sample with 3 PIP cycles was 0.77 g/cm\({}^{3}\), 7.18 \(\pm\) 1.92 MPa in XY direction and 9.01 \(\pm\) 1.25 MPa in Z direction, respectively. This composite presented excellent high-temperature oxidation resistance and thermal stability properties with weight retention above 95% up to 1000 \({}^{\circ}\)C both under air and Ar atmosphere. The minimal reflection loss (RLmin) value and the widest effective absorption bandwidth (EAB) value of as-prepared composites was -24.31 dB and 4.9 GHz under the optimization condition for the sample with 3 PIP cycles. The above results indicate that our BPR-derived carbon modified 3D needed carbon fiber reinforced SiOC composites could be considered as a promising material for practical applications.  
SiOC; pyrolysis temperature; PIP cycle; microwave absorption +
Footnote †: journal: Applied Physics Letters
# Positron Emission Tomography in Coronary Heart Disease
### Imaging Microcalcification with 18F-Sodium Fluoride (NaF)  
NaF has been used to detect bone metastases and it is known to replace the hydroxyl group of hydroxylapatite in areas of calcification [20; 21]. Regarding vascular calcification, two processes occur in a continuum in the atherosclerotic plaque as a healing response to the necrotic core inflammatory mediators: microcalcification, followed by macrocalcification. The stage of microcalcification renders the plaque unstable and more predisposed to rupture, making its identification an interesting surrogate marker for detecting \"vulnerable\" plaques. NaF-PET was shown to be able to identify microcalcification in atherosclerotic plaques in a consistently different pattern of uptake to the macroscopic calcium observed on computer tomography (CT) [22] (Figure 1).  
Different groups have proposed that NaF uptake could be used as a maker of cardiovascular risk by demonstrating its correlation with various validated clinical scores for cardiovascular disease burden [23; 24; 25; 26]. Our group showed that in a high cardiovascular risk population, NaF atherosclerotic plaque uptake was related to the burden of cardiovascular risk factors and thoracic fat volume, but there was no association between coronary uptake and calcium score [27].  
In the context of acute coronary syndrome patients, the culprit plaques associated with infarcted myocardium were shown to display greater NaF uptake than \"non-culprit\" plaques [28].  
In perhaps the most significant clinical study with NaF-PET, patients with known coronary artery disease underwent NaF-PET/CT and were followed up for fatal or nonfatal myocardial infarction over 42 months. Total coronary NaF uptake predicted MI independently of age, sex, risk factors, segment involvement and coronary calcium scores, presence of coronary stents, coronary stenosis, REACH and SMART scores, the Duke coronary artery disease index, and recent myocardial infarction [29].
# Agile Service Engineering in the Industrial Internet of Things
## 3 SERVUS Methodology
### D3: Relationship to Reference Architecture Models  
The third dimension is related to the positioning of the analysis and design artefacts and activities with respect to (standardized) architecture reference models. In the case of the IIoT and Industrie 4.0, this is related to the Industrial Internet Reference Architecture specified by the Industrial Internet Consortium (IIC) [2] and the Reference Architecture Model Industrie 4.0 (Figure 5) [3]. Referring to the three dimensions of the SERVUS methodology (see Figure 1), these architectural aspects belong to the dimension D3 \"architectural frameworks\".  
The SERVUS requirements analysis approach refers to all architectural aspects of RAMI4.0 (referred to as \"layers\"). The artefacts \"user story\" and \"use case\" may be positioned in the RAMI4.0 business layer whereas they contain references to all other layers too. It encompasses all assets encapsulated in I4.0 components focusing on the functional interface of its administration shell and the meta-data (\"manif  
Figure 4: SERVUS analysis and design activities [4].  
Figure 5: Main Industrie 4.0 concepts: RAMI4.0 and I4.0 component (graphics of the figure taken from Reference [3]).  
The SERVUS requirements contain references to the asset types in the RAMI4.0 hierarchy level dimension (e.g., cloud-based data storage, controller device, production cell) upon which the requirement is set.  
The IIRA defines five so-called functional domains, which provide the major building blocks for Industrial Internet Systems (see Figure 6):  
* Control domain
* Operations domain
* Information domain
* Application domain
* Business domain  
These functional domains may be refined according to the needs of a project and an application domain, and mapped to architectural patterns, e.g., the three-tier architecture pattern as illustrated in Figure 7.  
The SERVUS methodology supports this refinement. It foresees defining lists of project-specific \"topics\" and assigning them to IIRA functional domains. Requirement and capabilities artefacts may also refer to these topics.  
Figure 8 shows an example of a landscape for the capability development in relation to the Architecture Elements (adopted from and positioned according to the three-tier IIS Architecture in IIRA). The colored circles (\"dots\") indicate how many capabilities have been identified for the respective Architecture Element at the given time horizon up to 2025. Note that a capability may relate to several Architecture Elements. In fact, there are 114 relations of a capability to an Architecture Element in Figure 8, but only 57 distinct capabilities. The area of each dot is proportional to the number of respective capabilities. The green dots for 2015 refer to existing capabilities, the others to planned capabilities. Such reports help to the project manager to plan the activities for the upcoming years and assign corresponding personal resources.  
Figure 6: The Industrial Internet Reference Architecture (IIRA) functional domains [2].
## 5 Analysis
### Ablation Study  
To verify the effectiveness of each part of our OtO method, several teacher-student architectures are selected to conduct an ablation study for CIFAR100. Overall, we explored the following: (i) a multi-exit architecture network (Mu) using only classification loss, i.e., setting \(\alpha\) in Equation (5) to 0; (ii) applying a conventional KD method on a multi-exit architecture network(Mu+KD), i.e., only the final exit of the multiple-exit student network architecture receives guidance from the original teacher network; (iii) OtO and the use of ensemble learning in the above three training modalities. The experimental results are shown in Figure 4. As shown in the figure, the multi-exit architecture student network with only one last exit to receive guidance from the teacher network improves the accuracies of the single last exit and ensemble learning. This illustrates the effectiveness of KD applied to multi-exit architectures. Furthermore, our proposed method is substantially better than the original multi-exit architecture network and conventional KD method, which indicates that a multi-exit architecture teacher-student network for one-on-one instruction stimulates the learning ability of the student network and results in a greater amount of knowledge. As shown in Figure 4b, in several teacher-student architectures, the accuracy is eventually improved by the ensemble, which illustrates the practical relevance of the ensemble for combining multiple stages. In conclusion, our proposed method can achieve better results using the ensemble.  
Figure 4: _Cont._  
are correctly classified for each exit. The results reveal that the samples classified correctly in each exit are not identical, and there are cases in which samples classified correctly at the earlier exit are misclassified at the later exit. Figure 5 shows a set of samples that are correctly classified at the first exit only. Grad-CAM [51] can be used to visualize the importance of this set of samples at the first exit versus that at the last exit in the prediction process for each position of the image for the corresponding class. Notably, the first and last exits are concerned with the general structure of the input image and image details, respectively. The candle sample in Figure 5 is an example in which the first exit focuses only on the simple structure of the candle to arrive at the correct classification. In contrast, the final exit focuses on the image details. More specifically, the final exit only focuses on the local information of the candle, which results in the classifier mistaking the image for that of a jellyfish and finally misclassifying it. The different exits of the student network focus on different aspects of the samples, which results in different classifications. Furthermore, variabilities exist among the multiple exits of a multi-exit architecture student network and the validity of the ensemble is ensured.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 2 Our Proposed Methods
### Adaptive Data Embedding
The secret data concealment is based on the modulus operation, as demonstrated in Equation (3), where \(k_{R},k_{G},k_{B}\in\{-1,0,1\}\):  
\[\left\{ \begin{array}{ll}P_{R^{\prime}}=d_{R}+P_{R}-(P_{R}\;mod\;b_{R})+k_{R} \times b_{R},&\mbox{ where }k_{R}=\mbox{arg min}|P_{R^{\prime}}-P_{R}|\\ P_{G^{\prime}}=d_{G}+P_{G}-(P_{G}\;mod\;b_{G})+k_{G}\times b_{G},&\mbox{ where }k_{G}=\mbox{arg min}|P_{G^{\prime}}-P_{G}|\\ P_{B^{\prime}}=d_{B}+P_{B}-(P_{B}\;mod\;b_{B})+k_{B}\times b_{B},&\mbox{ where }k_{B}=\mbox{arg min}|P_{B^{\prime}}-P_{B}|\end{array} \right. \tag{3}\]  
where \((d_{R},d_{G},d_{B})\) represent the hidden secret digits for their corresponding channels. The \(\mbox{arg min}|\mathbf{P^{\prime}}-\mathbf{P}|\) operation ensures that the stego pixel \((P_{R^{\prime}},P_{G^{\prime}},P_{B^{\prime}})\) produced by \((k_{R},k_{G},k_{B})\) is as close as possible to the original one, thus reducing the distortion as much as possible. We remark that when embedding message to LDR images, our algorithm sets \((\mu_{R},H_{G},\mu_{B})\) as the boundaries, and the pixel values after the message concealment will remain within their original levels without crossing the boundaries to different levels. This means that our algorithm can extract correct messages under the adaptive message embedding strategy. Taking an LDR pixel as an example for message embedding, we let \(\mathbf{P}=(P_{R},P_{G},P_{B})=(59,114,91)\), \((d_{R},d_{G},d_{B})=(6,3,7)\), and \((\mu_{R},\mu_{G},H_{B})=(106.91,\,100.87,\,92.31)\). According to Equation (2), \((b_{R},b_{G},b_{B})=(7,7,11)\) represents the determined embedding base. We first use Equation (3) to calculate \(P_{R^{\prime}}=6+59-(59\;mod\;7)+k,k\in\{-7,0,7\}\), which results in \(P_{R^{\prime}}=62\), as it is closer to \(P_{R}=59\) than the other two candidates, \(55\) and \(69\). Similarly, we can derive \(P_{G^{\prime}}=115\) using the same process. As for \(P_{B^{\prime}}=7+91-(91\;mod\;11)+k,k\in\{-11,0,11\}\), which produces three possible values: \(84\), \(95\), and \(106\). Since \(95\) is the closest one to \(P_{B}=91\), we might choose it. However, \(95\) is larger than the level boundary of \(\mu_{B}=92.31\). Therefore, we choose the value \(84\), instead, in order to ensure the correctness of message extraction. Finally, the stego pixel value produced is \(\mathbf{P^{\prime}}=(P_{R^{\prime}},P_{G^{\prime}},P_{B^{\prime}})=(6 2,115,84)\). It is worth noting that our adaptive method conveys more secret data in pixels with low E values, which typically appear dark after the tone-mapping process. Similarly, our two-level adaptive LDR image embedding conceals more secret messages in pixels with values below the mean of the corresponding channels. These outcomes are consistent with the human visual sensitivity system that darker pixels are better suited for conveying more messages without attracting human attention, making it less likely for the stego image to be detected by malicious attackers.
## 1 Introduction
Although the use of the LVAC distribution system is dominant in this system, various studies have focused on protection methods that combine the LVDC distribution network with the LVAC distribution network [19]. Protection strategies for the hybrid AC/DC low-voltage distribution network system were created and synchronized. A transitory analysis of events in both systems was first performed, followed by the development of protective measures for each system. Owing to the variety of DC and AC electrical loads, it is necessary to ensure the autonomy of these loads by considering the inside and outside system conditions whatever the type of electrical load. In this paper, we propose a novel multizone architecture that contains four buses, comprising an LVDC-HVDC-LVAC distribution system with an electrical grid. This architecture contains power devices, DER, and ESS to establish a foundation for analyzing the distinct features of the different buses. Furthermore, steady-state and transient-state features are examined. The remainder of this paper is structured as follows: The novel multizone architecture design and component modeling of the investigated architecture is presented in Section 2. Subsequently, Section 3 describes the simulations using MATLAB/Simulink to validate the performance and efficacy of the proposed architecture. Finally, conclusions are presented in Section 4.
## 3 Results  
After removing duplicate manuscripts, a total of 354 manuscripts were identified from the 3 databases. After the first screening, 305 papers were excluded, as the title and abstract were considered not relevant to the topic. Forty-nine papers were shortlisted for extensive analysis. Sixteen of these were excluded from the search, as they did not focus on dosimetric data for RT toxicity prediction in PCa, and a further 6 because they were articles on SBRT and BT. The remaining 27 articles were reviewed, as they fulfilled the inclusion criteria [103; 104; 105; 106; 107; 108; 109]. Figure 1 describes the PRISMA workflow.  
In the majority of the reviewed studies, a logistic regression model was used to determine correlations between dosimetric parameters and side effects.  
Commonly used software for statistical analysis includes R and FORTRAN. The accuracy of predictive models was determined by performance indices with area under the ROC curve (AUC) [130] values ranging from 0.626 to 0.904.  
Only one study implemented ML techniques to enhance predictive accuracy [124].  
Several dosimetric parameters were analyzed, such as the volume receiving a certain dose (Vdose), maximum dose (Dmax), and mean dose (Dmean), primarily focusing on the rectum and bladder.  
The studies reported dosimetric parameters that were predictive of genitourinary (GU), haematological, and gastrointestinal (GI) toxicity.  
Figure 1: PRISMA flow diagram.
## 4 Results and Discussion
### Flood Discharge Simulation
As the hydrological process is a complicated system, the hydrological model has significant uncertainties [57, 60]. They may come from input data, model parameters, and hydrological processes such as soil moisture changing, evaporation, and runoff process. More specifically, the scatterplots between observed and simulated discharge are shown in Figure 7. The results with the bias-corrected SPPs show that the high flow part is considerably improved, while the low flow part received new biases. This is related to the fact that satellite precipitation generally overestimates light rain and underestimates heavy one [61]. As we all know, there are uncertainties not only in SPPs but also in hydrologic processing. In this study, we obtained satisfactory results of flood discharge simulation with bias-corrected NRT SPPs. Even though the corrected NRT SPPs still have biases in temporal and spatial, it has the potential to runoff simulation at the basin scale. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cases** & **Metrics** & **Gauge** & **GSMaP** & **GSMaP-RBC** & **IMERG** & **IMERG-RBC** \\ \hline \multirow{4}{*}{July 2002} & _NSE_ & **0.94** & 0.27 & **0.45** & 0.2 & **0.81** \\  & _CC_ & 0.97 & 0.71 & 0.79 & 0.78 & 0.93 \\  & _BIAS_ & 22.34 & \(-\)83.15 & 53.46 & \(-\)81.04 & 25.47 \\  & _RMSE_ & 176.02 & 625.87 & 544.55 & 655.3 & 321.45 \\ \hline \multirow{4}{*}{August 2003} & _NSE_ & **0.94** & 0.14 & **0.83** & 0.21 & **0.82** \\  & _CC_ & 0.99 & 0.49 & 0.93 & 0.56 & 0.92 \\  & _BIAS_ & \(-\)31.03 & \(-\)152.59 & \(-\)6.32 & \(-\)139.42 & \(-\)13.06 \\  & _RMSE_ & 151.66 & 576.5 & 259.14 & 554.55 & 260.8 \\ \hline \multirow{4}{*}{October 2004} & _NSE_ & **0.95** & 0.46 & **0.76** & 0.53 & **0.71** \\  & _CC_ & 0.98 & 0.7 & 0.88 & 0.75 & 0.85 \\  & _BIAS_ & \(-\)72.42 & \(-\)88.45 & \(-\)43.32 & \(-\)98.52 & \(-\)57.61 \\  & _RMSE_ & 165.23 & 555.06 & 367.5 & 518.98 & 409.01 \\ \hline \multirow{4}{*}{July 2007} & _NSE_ & **0.96** & 0.48 & **0.48** & 0.61 & **0.66** \\  & _CC_ & 0.98 & 0.78 & 0.7 & 0.79 & 0.83 \\  & _BIAS_ & \(-\)5.98 & \(-\)27.22 & 17 & 70.23 & 12.72 \\  & _RMSE_ & 121.71 & 464.15 & 462.39 & 402.04 & 372.09 \\ \hline \multirow{4}{*}{October 2009} & _NSE_ & **0.74** & 0.25 & **0.37** & 0.41 & **0.41** \\  & _CC_ & 0.95 & 0.77 & 0.75 & 0.87 & 0.78 \\ \cline{1-1}  & _BIAS_ & 100.04 & 190.29 & 115.58 & 114.84 & 113.49 \\ \cline{1-1}  & _RMSE_ & 164.73 & 281.66 & 257.63 & 248.66 & 248.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation criteria for flood discharge simulations.
## 6 Experimental Process and Results
First of all, a kernel-based approach was applied over all the five examined regressors (KernelSHAP), which is agnostic regarding the applied learning model and introduces a linear model that is fitted over the sampled pairs of (data, targets) and their generated weights. To generate these weights, several coalitions over the \(F\) space is produced, while the marginal distribution instead of the accurate conditional distribution is sampled for replacing the features that are absent during a random coalition. Although the assumptions here may lead to poor results because of the randomly selected coalitions that ignore some feature dependencies, the fact that a linear regression is applied during the last stage of the computation, additional strategies may easily be implemented trying to smoothen possible defects of this approximation (regularization, different learning model). On the other hand, a tree-based approach (TreeSHAP) has been applied in the case of GB-based approaches trying to figure out possible discrepancies between the explanation of this kind of learner. TreeSHAP constitutes an expansion of the KernelSHAP approximations, leading to faster results and facilitating the learners that are based on Decision Trees, integrating aggregating behavior through proper additive properties. Further information is provided in the original work [55]. We present here only the corresponding diagram of GB (ls) with both SHAP explainers, ignoring the similar enough performance of GB (huber), since it is the only tree-based regressor. The SHAP visualization plots (Figures 4-8) illustrate the attribute impact on the output of the produced regression model (the attributes are ranked in descending order from top to bottom) and how the attribute values impact the prediction (red color correlates to positive impact) in the first scenario using the \(D_{1}\) dataset. Attributes \(\text{Wri}_{1}\) (grade in the first written assignment), \(\text{Ocs}_{1}\) (presence in the first optional contact session), and \(\text{V}_{31}\) (number of views in the module forum) are the most important ones in all cases regardless of the regressor employed. In addition, these attributes seem to positively influence the target attribute (i.e., student grade in the final examinations). Therefore, high-achieving students in the first written assignment, students with high participation rates in the first optional contact session, and students with high view rates in the module forum achieve a higher grade in the final course exam. Very similar results were produced regarding the second scenario using the \(D_{2}\) dataset. In this case, attribute \(\text{Wri}_{2}\) (grade in the second written assignment) proved to be the most significant, along with attribute \(\text{V}_{32}\) (number of views in the module forum). Figure 4: KernelSHAP values of the 5 NN regressor (\(D_{1}\) dataset). Figure 5: KernelSHAP values of the LR regressor (\(D_{1}\) dataset). Figure 6: KernelSHAP values of the GB (ls) regressor (\(D_{1}\) dataset). Figure 7: TreeSHAP values of the GB (ls) regressor (\(D_{1}\) dataset).
# Treatment of Water Contaminated with Diesel Using Carbon Nanotubes
## 3 Results
### Adsorption Kinetics
The system with the highest carbon nanotube content has a maximum adsorption capacity value of 10 after 15 min. Although carbon nanotubes, diesel and water have different densities, on average, of 1.35 g/cm\({}^{3}\), 0.835 g/cm\({}^{3}\) and 1 g/cm\({}^{3}\), respectively, the dispersion of the phases mainly occurs through the hydrophobic nature of the nanotubes. In fact, carbon nanotubes in the absence of functional groups, as carbon nanotubes used in this experimental work, show a predominantly hydrophobic nature. In this case, the interaction with extremely polar molecules, such as water, is very weak, while the interaction with low-polarity organic molecules, such as those present in diesel fuel, is favored. This explains the preferential adsorption of carbon nanotubes towards diesel organic molecules.