esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
This section presents and discusses the experimental results obtained in the study. The performance of the classification algorithms was evaluated with the following measures: balanced accuracy, sensitivity, specificity, and AUC. The experimental results of the various classifiers are shown in Table 2 and were obtained using the 10-fold cross-validation procedure. Meanwhile, the hyperparameters of the classifiers were tuned to ensure optimal performance. Furthermore, the min-max normalization technique, discussed in Section 2.2, was used to preprocess the data.  
According to the balanced accuracy, sensitivity, and specificity values shown in Table 2, the Adaboost classifier achieved the best classification performance with a balanced accuracy, specificity, and sensitivity of 92%, 91%, and 93%, respectively. Meanwhile, both the decision tree and SVM obtained balanced accuracy of 73%, thereby having the least performance. Moreover, from the results, it can be observed that the ensemble classifiers achieved better performance than the conventional classifiers, with the Adaboost achieving the best-balanced accuracy, followed by XGBoost and Random forest. These performances can be attributed to their robust learning process, which involves building multiple base models and the application of a combination rule, such as majority voting, to make the final prediction. Furthermore, Figure 1 shows the various classifiers' ROC curves and AUC values.  
The ROC curve shows the trade-off between sensitivity and specificity. The more the models get closer to the top-left corner, the better the model is at detecting the positive and negative classes in the data. In contrast, models with a ROC curve close to the diagonal line indicate a model that randomly predicts the class. From the ROC curves, the Adaboost achieved a better AUC than the other models, as depicted in Figure 1. The AUC quantifies the ROC curve and indicates the size of the plot that is located under the curve. Therefore, models with an AUC closer to 1 are usually preferred, implying that they can distinguish between the positive and negative classes. The AUC of the Random Forest, Logistic Regression, and AdaBoost were better at 0.93 over the XGBoost, Support Vector Machine, and Decision Tree at 0.87, 0.84, and 0.62, respectively.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm** & **Balanced Accuracy (\%)** & **Specificity (\%)** & **Sensitivity (\%)** & **AUC (\%)** \\ \hline Decision Tree & 73 & 80 & 66 & 62 \\ \hline Logistic Regression & 82 & 89 & 74 & 93 \\ \hline Support Vector Machine & 73 & 67 & 80 & 84 \\ \hline Random Forest & 86 & 90 & 82 & 93 \\ \hline Adaboost & 92 & 91 & 93 & 93 \\ \hline XGBoost & 90 & 92 & 88 & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance measures of the classifiers.
## 3 Aerodynamic Interactions
### Train Model
#### 3.1.2 Bridge Deck Shielding Effect  
In the bridge-only case, the shear layer thickness of the leading-edge separated flow over the bridge-deck upper surface is limited in \(\alpha=[-16^{\circ},2^{\circ}]\) as evidenced by the middle columns of Figures 3i-o and 4f,g. With an increase in \(\alpha=[4^{\circ},\,8^{\circ}]\), the above shear layer thickness increases noticeably as shown in Figure 4h-j. In the train-bridge case, the above leading-edge separated flow reattaches on the upstream surface of the train model, and then bifurcates into two parts, as shown in the right columns of Figures 3 and 4. With an increase in \(\alpha=[-12^{\circ},\,12^{\circ}]\), the stagnation point (bifurcation) of the leading-edge shear layer is always located at the \(1/4\) height of the train model, which is larger than the train-bridge gap, suggesting that this leading-edge shear layer may have a shielding effect on the train model.  
To quantitatively identify the above-mentioned bridge deck shielding effect, the approaching flow profiles of the train model were monitored by a Cobra probe as shown in Figure 5. Due to the \(\pm 45^{\circ}\) conical measurement area of the Cobra probe, the flow profiles in the range of \(0\leq\Delta y^{\prime}\leq 0.5\) could not be effectively tested and thus are not shown here. In Figure 5a, the streamlined component of the normalized mean velocity \(u/U_{\infty}\) is always smaller than 1.0 in \(\Delta y^{t}=[0.5,1.5]\), evidencing the above achieved shielding effect in all the test ranges of \(\alpha=[-12^{\circ},\,12^{\circ}]\). However, in the range of \(\alpha=[-12^{\circ},\,-6^{\circ}]\), this bridge-deck shielding effect was limited, as is evidenced by the \(u/U_{\infty}\approx 1.0\) flow profile. With an increase in \(\alpha=[-4^{\circ},\,12^{\circ}]\), this bridge deck shielding effect is remarkably enhanced as is evidenced by the noticeably decaying \(u/U_{\infty}\) profile and obviously intensifying turbulence intensities \(I_{u}\) & \(I_{v}\) profiles in \(\Delta y^{t}=[0.5,1.0]\), respectively.  
When compared to the study of [34], two striking differences can be summarized here. Firstly, no speed-up shear layer can be detected in the approaching flow of the train model mounted on the upstream track, which leads to the disappearance of the quasi-Reynolds number effect [34]. Then, the space upstream from the train is compressed when the train model is mounted on the upstream track, which leads to limited room for the development of the leading-edge shear layer of the bridge-deck model. Thus, the stagnation point of the train approaching flow is almost \(\alpha\)-independent. Otherwise, the location of this stagnation point shall be a function of \(\alpha\) as discussed in [34].
## 2 Related Work  
Environmental monitoring in the farmost areas is becoming increasingly desirable largely because many studies have noted the importance of understanding environmental processes and their effect on climate change. Thus, without in situ and long-term monitoring data, the determination of the fingerprints of complex interactions becomes increasingly difficult. Data retrieval in the farmost areas without public networks is one of the difficulties referenced in previous studies.
## 3 Results
### Question 7: Where Do IEQ Models Apply?  
In theory, IEQ models could assess the quality of any indoor environment. However, current IEQ models were mainly built to evaluate IEQ in public buildings, especially in offices and schools. In contrast, IEQ models for residential buildings were relatively few. There are two reasons for this. First, the benefit obtained by improving IEQ in public buildings is straightforward and considerable. Many studies indicated that IEQ in office buildings directly impacted workers' health and productivity, whereby better IEQ could yield economic benefits [25; 26; 27; 90]. For example, a Lawrence Berkeley National Laboratory study found that improving thermal comfort and visual quality in office buildings could reduce sick building syndrome symptoms and absence due to illness, yielding the equivalent of USD 16-44 billion in annual savings [25]. Besides this, it also could increase employees' productivity and gain an additional USD 20-160 billion per year in the US [25]. Similar impacts of IEQ were also observed in educational buildings [32; 91; 92]. For instance, increasing the amount of daylight in classrooms could improve THE students' performance by 20% [93], while reducing traffic noise by 15 dB (A) could improve children's listening tests by almost 30% [94].  
To monetize these impacts, [PERSON] et al. [95] first tried to use the LCA (life cycle assessment) approach to investigate the effects of two different ventilation systems on students' well-being and performance, and found that passive displacement ventilation could reduce the number of students taking sick leave. In contrast, mixing ventilation could improve students' marks and offer more net benefits. However, because residential buildings are non-profit, the benefits of strengthening IEQ in living spaces were relatively less attractive, although people spend most of their time there. The second reason is that researchers have relatively easy access to public facilities, compared to residential buildings, and the measurements conducted in public buildings seem less intrusive. In addition, occupant density in public buildings, especially in office buildings and schools, is more extensive than in residential buildings, making collecting questionnaires easier. This also explains why the sample sizes in studies carried out in residential buildings were smaller than in public buildings. Therefore, fewer financial benefits and difficulty collecting data result in fewer studies on IEQ models in residential buildings. For the same reasons, the research attention paid to elderly care centers and day-care facilities was even more sparse.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 3 Proposed Algorithm
### Algorithm Robustness
The receivers verify that it has already been stored in _buffer\({}_{rx}\)_ or _buffer\({}_{ok}\)_ and therefore they discard it. Nevertheless, node \(v_{i+2}\) will retransmit again the frame which will be discarded by the nodes \(v_{i+1}\), \(v_{i+3}\) and \(v_{i+4}\) and node \(v_{i}\) will store it in _buffer\({}_{ok}\)_ and then eliminate it from _buffer\({}_{tx}\)_.  
Figure 6: Node \(v_{i+1}\) retransmits to node \(v_{i+2}\). Figure 7: Frame does not reach node \(v_{i+2}\). When a node \(v_{i}\) sends information to the control station in the direction of the flow \(Pr\), and there are at least two damaged nodes, as seen in Figure 8, the last node that works correctly (\(v_{k-2}\)) will retransmit the frame in the flow towards the node \(v_{0}\). At that moment, when the frame passes through node \(v_{i}\), this node, knowing that the variable _ChangeE_ is \"TRUE\", will change the flow direction, starting to use \(P_{i}\), as the flow \(Pr\) is interrupted. When a frame arrives at the central station with _ChangeE_ set to \"TRUE\", it means that two adjacent nodes are damaged. It might also happen that the node is in a network segment that has no connection with the end nodes \(v_{0}\) and \(v_{n+1}\), because the nodes \(v_{g}\), \(v_{g+1}\), \(v_{k-1}\) and \(v_{k}\) are damaged, as seen in Figure 9. Therefore, the frame would be circulating through the flows \(P_{r}\) and finally by \(P_{l}\) to be later discarded by \(v_{g+2}\). When two nodes want to transmit simultaneously and the nodes are separated by two or three nodes, the problem of the hidden nodes may appear. The collision of the frames due to the problem of the hidden nodes is resolved according to what is indicated in the noisy link scenario.
# Assessing the Performance of Different Grains in Gluten-Free Bread Applications
## 4 Conclusions  
The composition and physical properties, resistant starch, antioxidant activity and total phenolic content of quinoa, sorghum and millet revealed the huge potential of these flours to be used for developing functional gluten-free bread. All these flours have higher contents of proteins, fat and fiber compared to the rice flour, usually used in the gluten-free products as main ingredient. Compared to the rice bread, the quinoa bread had higher specific volume and lower crumb firmness, while sorghum and millet bread had lower specific volume and higher crumb firmness. All breads had the total phenol contents and antioxidant properties higher compared to the rice bread. Quinoa bread had the best physical properties, and the highest total phenol content. Additionally, quinoa bread had the highest resistant starch content among all investigated bread samples. These results indicate that quinoa demonstrated the highest potential to be used for obtaining high-quality gluten-free breads.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{4}{c}{**Breads Prepared With**} \\ \cline{2-5}  & **Quinoa Flour** & **Sorghum Flour** & **Millet Flour** & **Rice Flour** \\ \hline Resistant starch, \% d.w. & 3.28 \(\pm\) 0.07 & 2.79 \(\pm\) 0.06 & 1.83 \(\pm\) 0.05 & 1.40 \(\pm\) 0.05 \\ Rapidly digestible starch, g/100 g starch d.w. & 48.31 \(\pm\) 0.10 & 60.53 \(\pm\) 0.10 & 63.26 \(\pm\) 0.11 & 73.23 \(\pm\) 0.11 \\ Slowly digestible starch, g/100 g starch d.w. & 15.50 \(\pm\) 0.11 & 11.86 \(\pm\) 0.10 & 6.51 \(\pm\) 0.10 & 8.25 \(\pm\) 0.10 \\ Digestible starch, g/100 g starch d.w. & 63.81 \(\pm\) 0.11 & 72.39 \(\pm\) 0.11 & 69.77 \(\pm\) 0.11 & 81.48 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Rapidly and slowly digestible starch of quinoa, sorghum, millet and rice breads.  
**Author Contributions:** Conceptualization, I.A. and I.B.; methodology, I.B.; validation, I.A.; formal analysis, I.B.; investigation, I.B. and I.A.; writing--original draft preparation, I.B.; writing--review and editing, I.A.; supervision, I.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors thank to Vitanescu Maricel for supplying the quinoa seeds.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Motivation and Background Analysis
### Information-Centric Networking  
The current host-oriented Internet architecture has several drawbacks (e.g., the limited IP address to identify a lot of IoT and other devices, location-dependent content access, and centralized architecture). To overcome these drawbacks, information-centric networking (ICN) [6] was proposed as a new Internet architecture. ICN is a location-independent and content-oriented Internet architecture where an end-user accesses the contents based on the content name instead of content location address. ICN has several characteristics such as in-network content caching, naming, packet-level security, etc., which will be useful to overcome the drawbacks of the existing Internet. ICN is an important and effective candidate for modern communication architecture including IoT and massive machine-type communication in the 5G era.
## 4 Conclusions  
In this study, DB of 316 CNASS was performed as a smoothing process with the same magnitudes of the main governing factors but under four different lubrication and cooling conditions: (1) flood lubrication (process F); (2) dry conditions without cooling (process D); (3) dry, air cooled conditions with a temperature of \(-\)19 \({}^{\circ}\)C (process A); and (4) dry nitrogen cooled conditions with a temperature of \(-\)31 \({}^{\circ}\)C (process N).  
As a result of this work, the following key findings were obtained:  
* All four DB processes provide mirror-finished surfaces, with roughness parameter Ra values from 0.041 to 0.049 \(\mu\)m. The ranking of the processes in terms of decreasing roughness is F, A, D, and N.
* Processes F and D produce the highest microhardness, both on the specimens' surface and at depth.
* All the investigated DB processes introduce zones of significant residual compressive stresses deeper than 0.5 mm. Process D introduces the maximum compressive residual stresses (both axial and hoop) in the surface layer.
* The dry DB processes (D, A, and N) form a submicrocrystalline structure with high atomic density, which is most pronounced for process D.
* The three dry DB processes (D, A, and N) satisfy the criteria for sustainable finishing processes. When high fatigue strength is required, DB with cold air cooling should be chosen, as this configuration provides a more favorable cost/quality ratio. However, dry DB without cooling is the most suitable sustainable finishing process for applications where increased wear resistance is required.  
Conceptualization, J.M. and G.D.; methodology, J.M. and G.D.; software, J.M., G.D., V.D. and Y.A.; validation, J.M. and G.D.; formal analysis, J.M., G.D. and Y.A.; investigation, A.A., V.D., Y.A., K.A., G.D. and J.M.; resources, J.M. and G.D.; data curation, J.M. and G.D.; writing--original draft preparation, J.M. and G.D.; writing--review and editing, J.M. and G.D.; visualization, J.M., G.D., A.A., V.D. and Y.A.; supervision, J.M.; project administration, J.M. and G.D.;funding acquisition, J.M. and G.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Regional Development Fund within the OP. \"Science and Education for Smart Growth 2014-2020\", Project CoC \"Smart Mechatronics, Eco- and Energy Saving Systems and Technologies\", No.BG05M2 OP001-1.002-0023.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 4 Analysis of Spectral Efficiency
### Spectral Efficiency of Proposed HARQ Scheme  
Since \(N\) autonomous retransmissions are performed in the proposed scheme, a packet is repeatedly transmitted at least \((N+1)\) times. Therefore, if we define \(p_{p,m}=P[M=m]\) as the probability that a packet transfer is terminated at the \(m\)-th transmission for the proposed HARQ, \(p_{p,m}=0\) for \(m\leq N\) and \(p_{p,N+1}\) is expressed as  
\[\begin{array}{ll}p_{p,N+1}&=\prod\limits_{k=1}^{K}\left(1-\prod\limits_{n=1 }^{N+1}q_{n,k}\right)\\ &=P[M\leq N+1]\\ &\quad=\sum\limits_{m=1}^{N+1}p_{c,n}\end{array}. \tag{12}\]  
Because \(\sum_{n=1}^{N+1}p_{p,n}=\sum_{n=1}^{N+1}p_{c,n}\) and there is no difference between the conventional HARQ and the proposed HARQ for \(m>N+1\), \(p_{p,m}\) can be expressed as  
\[p_{p,m}=p_{c,m}, \tag{13}\]  
for \(N+1<m\leq M_{\max}\). So, the average number of HARQ transmissions per packet, \(L_{p}(N)\), is denoted as a function of \(N\), as  
\[L_{p}(N)=\sum\limits_{m=N+1}^{M_{\max}}m\cdot p_{p,m}. \tag{14}\]  
Since users do not send ACK messages for the first \(N\) transmissions, the average number of ACK transmissions per packet, \(Q_{p}(N)\), is given by  
\[Q_{p}(N)=L_{p}(N)-N. \tag{15}\]  
By substituting (14) and (15) in (3), the spectral efficiency metric for the proposed algorithm, \(\eta_{p}(N)\), is expressed as  
\[\eta_{p}(N)=\frac{(1-\gamma)\alpha\beta R_{d}}{(R_{d}+KR_{u})L_{p}(N)-KR_{u}N}. \tag{16}\]  
When the value of \(q_{m,k}\) is known, \(p_{p,m}\) can be computed using (11)-(13), and \(L_{p}(N)\) and \(\eta_{p}(N)\) can, in turn, be evaluated by (14) and (16). By calculating the spectral efficiency metric for all possible value of \(N\), the optimal \(N\), \(N^{*}\), and the corresponding maximum spectral efficiency metric, \(\eta_{p,\max}\), are computed as  
\[N^{*}=\operatorname*{arg\,max}_{N}\max_{0\leq N\leq M_{\max}-1}[\eta_{p}(N)], \tag{17}\]  
and  
\[\eta_{p,\max}=\max_{0\leq N\leq M_{\max}-1}[\eta_{p}(N)]. \tag{18}\]  
In (17), the optimal number of autonomous retransmissions is determined as the value corresponding to \(\eta_{p,\max}\). Equations (17) and (18) theoretically provide the \(N^{*}\) and the corresponding maximum spectral efficiency, given multicast environments with HARQ operation. In practical systems, however, it is difficult to find the \(N^{*}\) and the corresponding \(\eta_{p,\max}\), because the computation of \(\eta_{p}(N)\) in (16) requires the knowledge of PER curves for the physical link depending on time-varying channel conditions.
## 5 Conclusions  
The DS-LCC and LCC-S compensation topologies of the WPT systems were analyzed using a mutual inductance model. By combining the advantages of their respective CC and CV outputs, a shunt capacitor, and two ACSs were added on the secondary side, and a switch hybrid compensation topology was proposed. The proposed hybrid topology charger is designed to easily implement the ZPA feature, which contributes to reduce the VA ratio and enhance the WPT systems efficiency. Moreover, the control was applied directly to the secondary side, which avoids sophisticated control logic and is conducive to the robustness of the WPT systems. Additionally, the working frequency of the WPT systems was fixed throughout the charging process, which mitigates the problems caused by frequency hopping. Finally, a 1.1-kW experimental prototype was designed to validate the charging performance of the proposed WPT systems. The results of experimental indicated the proposed switching hybrid compensation topology can well accomplish CC and CV outputs under dynamic loads, and the maximum charging efficiencies were 90.9% and 91%, respectively. The experimental results were consistent with those of the theoretical analysis.  
Conceptualization, G.L. and D.-H.K.; data curation, G.L.; formal analysis, G.L.; funding acquisition, D.-H.K.; investigation, G.L. and D.-H.K.; project administration, D.-H.K.; writing--original draft, G.L.; writing--review and editing, D.-H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2020R1I1A3073169, in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and in part by the Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea under Grant 2019381010001B.  
Figure 20: Transient waveforms switching process from the CC to CV charging mode for \(U_{\text{IN}}\), \(I_{\text{IN}}\), \(U_{\text{BAT}}\), and \(I_{\text{BAT}}\).
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Failure Modes of Railway S&C Systems
#### 2.4.3 Sensor and Control Subsystem  
The different railway S&C systems consist of various sensors and controllers as explained in Section 2.2. The failure of any current or voltage sensor attached to the actuation elements in turn results in failure of the actuator for various point machines such as P80, M63, HW 2000, etc. [26; 44]. For HPSS, it is seen that faults in the LVDT signal and the subsequent control command attached to it cause a considerable amount of failures [51]. In the case of clamplock and HW switches, the mechanical linkages experience wear during operation. Also, any misalignment during the maintenance process leads to failure of the railway S&C system.
## 2 Data and Methods
### Spatiotemporal Data Selection Criteria  
To detect reliable abnormalities in the ionosphere caused by seismic activity, [PERSON] et al. developed a Deviation-Space-Time (DST) criterion, which was also used in this study for analysis and validation [45]. The DST criterion is made up of three components: first, the observed parameter deviates significantly from its expected values, indicating its potential as an anomaly; second, the occurrence of multiple anomalies exhibits quasi-synchronous behavior in time; and third, the reported anomalies are spatially contiguous, indicating geographic adjacency. This DST criterion is used to detect and investigate ionosphere anomalies connected with seismic occurrences while limiting false positives [46]. Large magnitudes have a significant impact on ionospheric disturbances. To ensure compatibility, the size of the prepared seismic zone is computed using the formula (R = 10\({}^{0.43\text{M}}\)), where R represents the radius in km and M indicates the magnitude [47].