esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Classification and Results
### R-Classification
Furthermore, during the testing phase, the average F1-score and sensitivity stand at 0.935 and 0.958, respectively. Comparatively, the accuracy during the training phase remained consistent at 96.32% for both feature vectors. However, the testing accuracy in the cases of sensors 4 and 5 was slightly lower than in the first case, i.e., 91.67% and 95.83%, respectively, despite a higher number of selected features (12 compared to 9 for sensors 2 and 3). As depicted in the previous graphs of Figure 17, out of the 136 samples randomly assigned to the training phase, again only 5 were misclassified. Specifically, 2 samples from R-Class 3 were classified as R-Class 1, 1 sample from R-Class 3 was classified as R-Class 2, 1 sample from R-Class 3 was classified as R-Class 4, 1 sample from R-Class 4 was classified as R-Class 3. During the testing phase, only 1 sample from R-Class 1 was incorrectly classified as R-Class 4. Figure 15: R-Classification: (**a**) confusion matrix for the training phase; (**b**) confusion matrix for the testing phase in the case S2–S3. classified as R-Class 4, and only 1 sample from R-Class 2 was incorrectly classified as R-Class 3. While the four receiving sensors were initially installed on the plate structure under consideration, the results of the training and testing phases on the two distinct conditions, i.e., sensors pair S2-S3 and S4-S5, suggest that analyzing the signals of only two sensors, either in the lower or upper part of the structure, can be sufficient to identify the occurrence of the damage and its location within the faulted region.
# Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
## 5 Results
### The Results of Comparison between the Proposed Model and Other Models at the Segment-Level  
Since there is no relevant deep learning model to detect the murmur quality to the best of our knowledge, the results of the designed model were compared with the results of some well-known models without pretrained, such as SqueezeNet [40], ECAPA-TDNN [41], EfficientNet B0 [42], MobileNet V3 [43], Resnet50 [44], GoogleNet [45], and DenseNet [46]. In addition, the results of all these models were obtained by five-fold cross-validation in the same conditions following the same data preprocessing.  
Table 5 demonstrates that the F1-score for the \"Harsh\" murmur is all higher than that of the \"Blowing\" murmur (e.g., 73.9% vs. 60.8%), which indicates that all the models were less effective at detecting \"Blowing\" murmurs compared to \"Harsh\" murmurs. This may be due to the features of \"Blowing\" murmurs being less obvious than those of \"Harsh\" murmurs, as in the log-Mel spectrograms in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Harsh**} & \multicolumn{2}{c}{**Blowing**} \\ \cline{2-7}  & & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Without SE & 66.4 \(\pm\) 6.5 & 70.2 \(\pm\) 3.7 & 74.4 \(\pm\) 11.7 & 71.9 \(\pm\) 7.3 & 61.5 \(\pm\) 9.6 & 54.9 \(\pm\) 6.3 & 57.5 \(\pm\) 5.7 \\ SE & **68.8 \(\pm\) 5.0** & **72.4 \(\pm\) 2.6** & **75.9 \(\pm\) 8.9** & **73.9 \(\pm\) 5.5** & **63.8 \(\pm\) 7.5** & **58.6 \(\pm\) 4.7** & **60.8 \(\pm\) 4.4** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation analysis. “Without SE” refers to no SE-Block between the CNN-Block and Bi-GRU, and “SE” refers to the opposite.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Parameters** \\ \hline CPU & Intel(R) Xeon(R) E5-2680 v4 \\ Memory & 32 GB \\ GPU & NVIDIA GeForce RTX 3080 Ti \\ Python 3.11 PyTorch 2.2.1 \\ Cuda 12.1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Conditions.  
The proposed model exhibited higher performance compared to other models. Specifically, the accuracy was 68.8%, the F1-score for the \"Harsh\" murmur was 73.9%, and the precision for the \"Blowing\" murmur was 63.8%, all of which were the highest among the models. This result highlights the advantages of the combination of the CNN and gated recurrent neural network (RNN) with the SE-block, since none of the other models employed the structure associated with RNN. In the designed model, the CNN with the SE-block focuses on the extraction of image features for the acoustic spectrograms, whereas GRU can compensate for the CNN by extracting temporal features. In addition, the designed model uses a smaller number of CNN layers and feature map channels compared to other models, which can reduce the parameters of the model and mitigate the overfitting in this dataset.
# Concept Justification of Future 3 DPVS and Novel Approach towards its Conceptual Development
## 3 Conceptual Design Approach for 3 DPVS
### Potential Methodology and Tools Supporting 3 DPVS Conceptual Design
#### 3.4.1 Methodology and Tools for Research and Design Initiation  
In the initial phase of the conceptual design, two methodologies could be applied.  
_Methodology of Literature Review._ The literature review has played and will continuously play significant role in design and innovation. The chief aim of literature reviews is to collect data, identify current limitations and propose practical objectives. Regarding potential data collection for conceptual 3 DPVS, they could include general forms, like interviews, data extraction, questionnaires and surveys, hypothetical attribute sets, and so forth.  
_Quality Function Deployment (QFD)_. For generating design requirements from literature reviews, customers, end-users or designers, a methodology to identify, compare, analyse these requirements, will be used in initiation phase of 3 DPVS as well. These income requirements, a super-system level, will be compared and analysed with system-level requirements, called design attributes, which will be further compared and analysed with sub-system requirements, called design components. This whole process of comparing and analysing will be conducted through the methodology, namely quality function deployment (QFD). Based on previous research [31; 32; 33], QFD can also be used in concept evaluation, which can be considered as the chief methodology for phase 3.
## 3 Discussion of Results from Sensitivity Analyses
### Tanks-Fuselage Integration Studies Based on Full-Section Layouts
#### 3.2.2 Sensitivity to L/Dstr  
The results given in this section were obtained considering the data in Table 9, set as reference for a single tank/full section configuration with a catwalk, and assigning \(L/D_{str}\) the role of variable. The case with the catwalk was selected since it does not introduce a physical interruption in the cabin, hence it is more in line with today's regulations.  
Figure 20a shows the effects of tank's length on number passengers and range, providing the input to find a trade-off condition.  
Although the latter depends on the desired market placement of the aircraft, Figure 20b can support the trade-off study providing relevant information from the environmental impact point of view, such as the consumption per pax-km and the vented LH\({}_{2}\) mass fraction. Figure 20a also shows that if target range is set to equal the harmonic range of the reference aircraft (5722 km, see Table 7), the maximum passenger capacity would be reduced from 308 to 164 units (18 seat rows less). It is worth underlining that the impact of range extension on passenger capacity is actually milder, since for short ranges (<1000 km) there is an initial loss of 56 passengers (7 rows).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** & **Cross-Section (Example)** \\ \hline Maximum tank radius (\(R\)) [m] & 2.02 & \\ Tank layout & Full-section & \\ Number of tanks in the section & 1 & \\ N. of tank groups \(\left(n_{lg}\right)\) & 1 & \\ \(R_{min}\) [m] & 0.50 & \\ Catwalk width [m] & 0.80 & \\ \(t_{0}\) & \(80^{\circ}\) & \\ \(t_{ins}\) [mm] & 50 & \\ \(L/D_{str}\) & Variable & \\ \(D_{str}\) [m] & 3.94 & \\ \(d_{supp}/L\) & 0.80 & \\ \(f_{end}\) & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Data on the reference single tank/full section configuration with a catwalk.
## 5 Discussion
\"Identification of annualized failure rate for equipment (P3)\" was calculated as the third most important indicator, since this indicator is used in the decision-making process in the procurement of healthcare equipment or systems. If this failure rate is very high, the procurement of healthcare equipment or systems is obstructed by the Turkish Ministry of Health. Moreover, this indicator is used to create a maintenance contract to decide compensation for the loss of earnings. \"Average age (P4)\" indicator has a low importance level for Turkish healthcare FM. However, this indicator is more important for Hong Kong healthcare FM. \"O&M cost (including depreciation and amortization costs) per building area/per patient bed (L17)\" is calculated by dividing O&M costs by building or the number of patient beds. As a result of AHP-PROMETHEE analysis, L17 was found to be the most important indicator under lifecycle cost management FM KPIs. Even though this indicator includes L17 and L25, it can be understood that the respondents try to define and compare both total costs and their sub-items. \"Resource/Energy expenditure per building area/per person (L25)\" was found to be the second most important FM KPI for the \"Lifecycle Cost Management in Healthcare Facilities\" group. [PERSON] and [PERSON] [184] and [PERSON] [185] stated that the management level of the healthcare organization needs to minimize or cut down operational costs to enable profit from investments. Therefore, L25 FM KPI can be useful to keep FM costs under control and to identify FM strategies. L25 and L17 were ranked by [PERSON] et al. [39]. According to the findings of [PERSON] et al., \"Energy cost per building area\" and \"O&M cost per building area\" were found at the top of the most important indicator. Within this context, Turkey and Hong Kong healthcare facilities show similarities. \"Maintenance cost per building area/per patient bed/by functional area (L5)\" was found to be the third most important KPI in the \"Lifecycle Cost Management in Healthcare Facilities\" group. [PERSON] [186] stated that any decisions related to maintenance works have an impact on cost, quality, duration, and resource allocation. Additionally, maintenance costs correspond to 50% of lifecycle costs [187]. The maintenance cost of buildings is increasing as the age of buildings increases.
## 3 A Pair of Rectangular Slots and Parasitic Patch for the 3 dB ARBW and the Gain Enhancement  
### A Pair of Symmetrical Rectangle Slots (Model 1)  
The CP is converted from the LP antenna (conventional model) by modifying the slot structure [16], and its feedline is positioned to the side of the wide slot [19]. In model 1, the feedline is shifted toward the left side of the \(x\)-axis, and two symmetrical rectangular slots are introduced on the diagonal of the grounded patch. The feedline is shifted to the left side with \(X_{f1}\) of 9 mm. The rectangular slots have a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \(R\) & \(L_{\text{s1}}\) & \(L_{\text{s2}}\) & \(W_{\text{r1}}\) & \(W_{\text{r2}}\) & \(X_{\text{r1}}\) & \(L_{\text{p}}\) & \(W_{\text{p}}\) & \(X_{\text{p}}\) & \(Y_{\text{p}}\) \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline Conventional & 26.5 & - & - & 4 & 4 & 0 & - & - & - & - \\ \hline Model 1 & 26.5 & 40.79 & 40.79 & 4 & 4 & -9 & - & - & - & - \\ \hline Model 2 & 26.5 & 48 & 40 & 4 & 10 & –9 & 12 & 31 & 8 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of CSA models.  
Figure 1: Geometry of a circular-slotted antenna (CSA): (**a**) conventional model and (**b**) model 1.  
Figure 2: The geometry of model 2. The microstrip feedline is located on the right side, and the edge profile of the antenna is drawn on the bottom.  
length \(L_{s1}\) of 40.79 mm, \(W_{s1}\) of 16 mm, \(L_{s2}\) of 40.79 mm, and \(W_{s2}\) of 16 mm. This design was used to generate the CP wave and increase the antenna gain, as shown in Figure 1b. The lengths \(L_{s1}\) and \(L_{s2}\) also influence the resonant frequency by changing the slot size. The larger the value of \(L_{s1}\), the higher the resonant frequency, and the lower the axial ratio. A wider impedance bandwidth compared with the conventional model was produced.  
With a slot length of 0 mm, the center frequency \(S_{11}\) shifts to a higher frequency of 2.37 GHz with an impedance bandwidth of 1.11 GHz. The antenna gain is 1.92 dBic at a frequency of 2.2 GHz, with a peak gain of 2.4 dBic at a frequency of 2.6 GHz. When the slot length is 30 mm, the frequency center shifts to a higher frequency of 2.59 GHz. This model achieves a peak gain of 4.06 dBic, but fails to generate the 3 dB axial ratio. When the rectangular slot length is 40.79 mm, a 3 dB axial ratio is generated at the center frequency of 2.34 GHz with an ARBW of 270 MHz. If the slot length is more than 40.79 mm, the axial ratio is better (lower), but the center frequency \(S_{11}\) shifts to a higher frequency. The influence of slot lengths \(L_{s1}\) and \(L_{s2}\) on \(S_{11}\), the axial ratio, and the gain are depicted in Figure 3a-c, respectively.
## 5 Discussion
Each of these three protocols exhibit unique advantages and disadvantages, reflecting the varied requirements of different sectors and environments within critical infrastructures. Additionally, it is noteworthy that a general approach (not to mention a specific protocol) is often adopted in publications that investigate security or topology in wireless sensor networks. Another significant finding is the infrequent mention of standards, particularly those pertaining to the quality of equipment and components suitable for use in sensors, nodes, gateways, and other WSN equipment in a critical infrastructure. References to such quality standards are exceedingly rare, in fact almost non-existent. This gap highlights a potential area of concern in ensuring the reliability and effectiveness of WSNs within critical infrastructural systems. This notable absence of information gives the opportunity for future work and study in the field of critical infrastructures. Finally, CI encompasses a broad spectrum of sectors, each with diverse and specific infrastructures. This diversity poses challenges in conducting studies that precisely match a type of WSN with a specific CI sector. Future research focusing on the development of WSNs with stringent standards and specifications tailored to particular CI sectors and subcategories holds significant potential and interest. This approach could lead to more targeted and effective integration of WSNs in critical infrastructure management. Figure 6: Bibliography results of CI communication protocols. **Author Contributions:** Conceptualization, S.D., N.P., P.P. and D.D.P. ; methodology, S.D., N.P., P.P. and D.D.P. ; validation, S.D., N.P., V.C., P.P., D.D.P. and R.A.M. ; formal analysis, S.D., N.P., P.P., D.D.P. and R.A.M. ; investigation, S.D., N.P., V.C., P.P., D.D.P. and R.A.M. ; resources, S.D., N.P., V.C., P.P. and D.D.P. ; data curation, S.D., N.P., V.C., P.P., D.D.P. and R.A.M. ; writing--original draft preparation, S.D., N.P. and V.C. ; writing--review and editing, S.D., N.P., V.C., P.P., D.D.P. and R.A.M. ; visualization, S.D., N.P., V.C., P.P. and D.D.P. ; supervision, P.P. and D.D.P. ; project administration, S.D., N.P., P.P., D.D.P. and R.A.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Any data presented in this study are available within the article. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Thermodynamic Design and Optimization
### Optimization Results
The impact of the adoption of the turbo-expander is significant, since it leads to a reduction of the total specific energy consumption between 27% and 18% (the percentage of energy savings decreases with the supply pressure reduction, see Table 2). The plots highlight a further advantage of replacing the lamination valve with an expander: the first solution, indeed, involves an isenthalpic process, while the second one causes a reduction of the enthalpy value, which implies also a lower quality of the natural gas at the end of the expansion, with consequent saving in energy consumption for the liquefaction process. However, the major impact on the specific energy consumption \(e\) is given by the storage pressure p\({}_{10}\): in fact, regardless of the maximum pressure p\({}_{6}\), the vapor quality in the flash tank decreases considerably with the increase of the storage pressure (p\({}_{10}\)), reducing the fraction of recirculated NG as vapor and so the consumption of the compressors. This is due to the shape of the saturation curve and to the consistent reduction of the heat of vaporization, as the pressure rises from 3 bar to 15 bar. Obviously, also the power required by the compression chiller rises with the increment of the NG quality. It follows that, in all the scenarios, the optimized solution involves the maximum value of storage pressure within the considered range (3-15 bar). Figure 3: Total specific electric energy consumption as function of maximum pressure p\({}_{6}\) and storage pressure p\({}_{10}\): (**a**) HPN, (**b**) MHPN, (**c**) MLPN, (**d**) LPN. The influence of maximum pressure of the circuit, p\({}_{6}\), appears less intuitive: being the total specific consumption a result of the sum of three different components having opposite characteristic curves, the representative equation is not a monotonic function and has a global minimum. Looking at the pressure-enthalpy diagrams in Figure 4a-d, at given temperature at the inlet of the expansion device, the consumption of the compressors is affected by the pressure ratio, which increases with p\({}_{6}\), and by the mass flow rate of NG over the entire cycle, that is decreased because of the quality reduction. This occurs for values of pressure up to 220 bar, above which the advantage is no more relevant. The same trend is obtained both with the expansion valve and with the turbine.
# A Coarse-Grained Molecular Model for Simulating Self-Healing of Bitumen
## 2 Establishment of Molecular Model
### Coarse-Grained Force Field  
The force field determines the motion laws of the simulated system, which is used to describe the inter-particle interactions, including the bonding interaction between particles, the van der Waals force between molecules, the hydrogen bond interaction, the charge force, etc. The basic computational particles of MD are atoms, while CGMD is based on coarse-grained beads, so the all-atom force field is not applicable to the simulation of coarse-grained molecular models.  
The CG force field describes all the interaction potential energy functions of coarse-grained beads in the form of potential energy functions, including non-bond potential energy functions and bond potential energy functions:  
1. The non-bond potential is the energy that mainly affects the determination of macroscopic properties of the bitumen system, including van der Waals potential, hydrogen bond potential and charge energy. Among the non-bonded potential, van der Waals potential plays a major role in the bitumen system. Hydrogen bonds only exist between hydrogen atoms, oxygen atoms, fluorine atoms and nitrogen atoms and must conform to a certain structural general formula; the bitumen molecular model in this study has few oxygen atoms and nitrogen atoms and little influence of hydrogen bonds, so hydrogen bond potential is not considered in this study. The CG beads divided in this study are all neutral particles without charge, so charge energy is not considered in this study.
2. The bond potential energy function includes simple harmonic vibration potential energy function, plane bending potential energy function and out-of-plane rocking potential energy function. The bond potential energy plays a minor role in the determination of the macroscopic properties of the bitumen system. In this study, the out-of-plane rocking potential energy function with the least influence on the bitumen system is not considered. The assumed interaction potential energy is: \[U_{CG}=U_{bond}(r)+U_{angle}(\theta)+U_{non}(r)\] (1) where \(U_{CG}\) is the CG potential, \(U_{bond}(r)\) is the bond energy (assumed harmonic), \(U_{angle}(\theta)\) is the angle potential, \(U_{non}(r)\) is the non-bond potential, \(r\) is the distance between beads, and \(\theta\) is the plane angle between three coarse-grained beads.  
Figure 2: The mapping scheme of the bitumen molecule model. (**a**) Mapping scheme of asphaltenes. (**b**) Mapping scheme of saturates. (**c**) Mapping scheme of aromatics. (**d**) Mapping scheme of polar aromatics.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 1 Introduction
### Motivation  
The \(m\)-sequence features a relatively simple structure and has been extensively studied, making its engineering implementation particularly straightforward and widely adopted in engineering practice. Consequently, the generation mechanism of FHS sets based on \(m\)-sequences is simple and direct with strong technical adaptability, low engineering costs, and highly flexible scalability. These characteristics facilitate achieving a favorable balance between the performance of FHS and the complexity of system implementation. These motivate us to design optimal LHZ FHS sets from \(m\)-sequences and their \(d\)-decimated sequences to enhance the overall communication quality and performance of QS-FHMA systems as well as improve flexibility in engineering applications.