esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Behavior Attributed to Internal Erosion Caused by Conduit Cracks in Homogeneous Embarment
## 3 Results and Discussion
### Pore-Water Pressure (PWP) Variations  
Measurement data are important for tracking the failure process via changes within the embankment. This study analyzed the effects of leaks in the conduit on the failure that occurred inside the embankment based on the pore-water pressure (PWP). Figure 6 shows the results of measuring the PWP by maintaining the HWL (55.7 cm, \(t\) = 20 min) on the upstream slope to the failure at the downstream slope (see Figure 4). For water level conditions, the FWL was maintained by using a water supply device before deformation appeared on the downstream slope (\(t\) = 20-155 min). Under conditions wherein deformation was observed on the slope, the water supply was stopped (\(t\) = 155-180 min). After reaching the HWL on the upstream slope, the PWP in the bottom conduit (P1, P3, and P5) exhibited an extremely small increase that maintained an almost constant level. However, the PWP above the conduit (P2, P4, and P6) continued to increase. These differences indicate that the seepage force constantly affects the parts above the conduit, which suggests that the surface above the conduit may be vulnerable to internal erosion. At the HWL, the PWP first began to decrease after 144 min at P5 and P6, which were located in the downstream conduit. These results reveal that the deformation of the downstream slope occurred slightly earlier than the PWP reduction in the model experiment, but they both occurred over a similar time range (see Figure 4).  
Figure 5: Erosion width (W), erosion depth (D), and expanded length (L) at the downstream slope.
## 2 Methods
### Evaluation Confusion Matrix  
In this study, accuracy served as the principal metric for performance evaluation. However, we also utilized the confusion matrix, a robust tool that provides a comprehensive representation of a classification model's performance. The confusion matrix offers a detailed overview of the model's predictions in comparison to the actual labels.  
For the purpose of this research, a \"positive instance\" refers to an image that has been correctly identified as depicting diabetic retinopathy (DR), whereas a \"negative instance\" refers to an image that has been correctly identified as not depicting DR. With these definitions in mind, the confusion matrix allows us to derive the following performance metrics:  
* True Positive (TP): The count of positive instances correctly identified as positive.
* True Negative (TN): The count of negative instances correctly identified as negative.
* False Positive (FP): The count of negative instances incorrectly identified as positive (Type I error).
* False Negative (FN): The count of positive instances incorrectly identified as negative (Type II error).  
Using these values, we calculated the following performance measures:  
\[\text{Accuracy (ACC\%)}=\text{(TP+TN)}/\text{(TP+TN+FP+FN)}\times 100 \tag{1}\]  
\[\text{Precision (PRE)}=\text{TP}/\text{(TP+FP)}\times 100 \tag{2}\]  
\[\text{Sensitivity (SEN), Recall, True Positive Rate (TPR)}=\text{TP}/\text{(TP+FN)}\times 100 \tag{3}\]  
\[\text{Specificity (SPE)}=\text{TN}/\text{(TN+FP)}\times 100 \tag{4}\]  
\[\text{F1-score}=2\times\text{(SEN\times PRE)}/\text{(SEN+PRE)} \tag{5}\]  
By considering these metrics, we obtained a comprehensive understanding of the model's performance, taking into account both positive and negative predictions. The confusion matrix is crucial in unveiling the model's strengths and weaknesses in distinguishing between different classes. For the purposes of this research, both the confusion matrix and validation accuracy were used to interpret the results.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 2 UWAC Physical Layer Techniques
### Multicarrier Modulation for the UWAC
#### 2.1.1 Orthogonal Frequency Division Multiplexing
The basic DTMB methodology in Chinese digital television terrestrial broadcasting (DTTB) [86] is the TDS-OFDM (well recognized as the known symbol padding KSP-OFDM) [92]. TDS-OFDM is recommended to circumvent the use of a pilot signal for bandwidth and energy efficiency as in ZP-OFDM and CP-OFDM. Yet, the energy and spectrum efficiency of TDS-OFDM can result in significant deterioration in BER performance due to the IBIs between OFDM data blocks and the sequence of training [93]. Figure 10 illustrates the relationship between the time and frequency domains of CP-OFDM, TDS-OFDM, and ZP-OFDM signals. In TDS-OFDM, the CP used in CP-OFDM is replaced by a PN training sequence while in ZP-OFDM it is substituted by a zero-padding segment. The \(ith\) TDS-OFDM signal transmitted, given by \(s_{i}=\left[s_{i,0},s_{i,1},\ldots,s_{i,q-1}\right]^{{}^{\prime}}\), comprises two separate parts; OFDM data blocks of length \(N\), i.e., \(x_{i}=\left[x_{i,0},x_{i,1},\ldots,x_{i,q-1}\right]^{{}^{\prime}}\) and a known PN sequence of length \(M\), i.e., \(c_{i}=\left[c_{i,0},\ldots\ldots,c_{i,q-1}\right]^{{}^{\prime}}\) which can be written as,  
Figure 2.5  
where, \(Q=M+N\) gives the TDS-OFDM frame length. Figure 11 illustrates IBIs between PN sequences and the OFDM data blocks in multipath channels. Such a type of mutual interference can be removed from the TDS-OFDM data block with a perfect estimation of a channel. TDS-OFDM is equivalent to ZP-OFDM after subtraction of the PN sequence. The DPN-OFDM method is used to address the influence of mutual interference between the PN sequence and TDS-OFDM data blocks that can occur in traditional TDS-OFDM systems. In a DPN-OFDM frame, impeccable estimation of the channel can be obtained by duplicating the guard interval to attain a PN sequence free of IBI. Due to its battery-based nature and limited bandwidth, DPN is nevertheless unprepared for the UWAC. To avoid such problems, the compressive sensing (CS) theory algorithm using the sparse nature of the UWA channels gets the time-varying channel state information (CSI). The IBI-free region helps to approximate the precise impulse response of the UWA channel and less its interference. Figure 2.6 TDS-OFDM for the UWA channel [81]. It is suggested to apply the look-ahead backtracking orthogonal matching pursuit-based sparse channel estimation method for underwater TDS-OFDM in a real sparse time-varying multipath channel in which there is a random distribution of channel taps. The PN sequence in the time domain is used to compensate for and estimate the Doppler shift of the UWA channel [94]. The variance among the frames received for the schemes of TDS-OFDM upon multipath channels is shown in Figure 2.7
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 4 Sensors
Such complex interferometers are very sensitive to misalignment and vibrations, therefore the use of integrated optics as telescope recombiners have been studied. [PERSON] et al. [101] used a silver-sodium ion-exchange on a silicate glass to realize a two telescope beam combiner operating on the H atmospheric band (from \(\lambda\) = 1.43 \(\upmu\)m to \(\lambda\) = 1.77 \(\upmu\)m). Consisting of a proper arrangement of three Y-junctions, the device had two photometric and one interferometric outputs. The fringe contrast obtained in the laboratory was 92% and the device was included in the Integrated Optic Near infrared Interferometric Camera (IONIC) put into a cryostat and successfully qualified on the sky [102]. Figure 10 shows the MAFL chip [103] that was developed for the interferometric combination of three telescopes. The pigtailed instrument contained not only the science interferometers but also three other ones dedicated to metrology, which permitted measuring of the different optical paths. The functions multiplexing and demultiplexing the metrology signal and the science ones were also implemented on the chip. The chemical durability of silicate glasses is a major advantage when a use in harsh environment is required. The opto-fluidic sensor developed by [PERSON] et al. [104] represents a quite extreme example of this. Indeed, the ion-exchange technology developed by [PERSON] et al. [35] on BF33 glass has been employed to realize a sensor for the detection of plutonium in a nuclear plant environment. The fully pigtailed and packaged device that is depicted on Figure 11, has been successfully tested in a nuclearized glove box, detecting plutonium dissolved in 2 Mol nitric acid without a failure over a period of one month. Such a reliability was achieved by co-integrating microfluidic channels fabricated by HF wet etching on one BF33 wafer with silver ion-exchanged waveguides realized on another wafer. The two wafers have been assembled by molecular adherence avoiding hence the use of radiation sensitive epoxy glues. Figure 10: Picture of the MAFL combining module. The optical chip contains waveguides made by a silver sodium ion-exchange.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Use Case
#### 3.1.2 Navigation  
This test assessed the route deviation of the RPSC during autonomous operations, analysing the precision and stability of its navigation. A route was defined using waypoints based on GCPs. The RPSC was deployed to follow this route under different sensor  
Figure 6: RPSC testing scenarios: (**a**) RPSC conducts navigation tests; (**b**) RPSC performs positioning tests.  
configurations to evaluate their impact on navigation accuracy. The tested configurations are shown in Table 5.  
Configuration 3 utilised RTK corrections from the positioning service, which achieved the best metrics in the positioning test.
###### Abstract  
An implicit assumption of risk perception studies is that concerns expressed in questionnaires reflect concerns in everyday life. The aim of the present study is to check this assumption, i.e., the extrapolability of risk perceptions expressed in a survey, to risk perceptions in everyday life. To that end, risk perceptions were measured by a multidimensional approach. In addition to the traditional focus on measuring the magnitude of risk perceptions, the thematic relevance (how often people think about a risk issue) and the discursive relevance (how often people think about or discuss a risk issue) of risk perceptions were also collected. Taking into account this extended view of risk perception, an online survey was conducted in six European countries with 2454 respondents, referring to radio frequency electromagnetic field (RF EMF) risk potentials from base stations, and access points, such as WiFi routers and cell phones. The findings reveal that the present study's multidimensional approach to measuring risk perception provides a more differentiated understanding of RF EMF risk perception. High levels of concerns expressed in questionnaires do not automatically imply that these concerns are thematically relevant in everyday life. We use thematic relevance to distinguish between enduringly concerned (high concern according to both questionnaire and thematic relevance) and not enduringly concerned participants (high concern according to questionnaire but no thematic relevance). Furthermore, we provide data for the empirical value of this distinction: Compared to other participants, enduringly concerned subjects consider radio frequency electromagnetic field exposure to a greater extent as a moral and affective issue. They also see themselves as highly exposed to radio frequency electromagnetic fields. However, despite these differences, subjects with high levels of thematic relevance are nevertheless sensitive to exposure reduction as a means for improving the acceptance of base stations in their neighborhood. This underlines the value of exposure reduction for the acceptance of radio frequency electromagnetic field communication technologies.  
RF EMF; exposure perception; risk perception; risk communication; base stations; mobile phones; questionnaire design; survey methodology; thematic relevance
# Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
## 5 Results and Discussion
### Decision Function Performance  
As mentioned before, this study chose to lower the implementation cost and only deploy the ML model's decision functions (DFs). Table 7 shows SVM and MLP performance details, including the execution cost and the elapsed time with average of 10 runs.  
A similar script was applied to time the completion of each deployed classifier prediction to calculate the smart contract DF. Figure 4 shows the elapsed time in seconds of both SVM and MLP in comparison with the built-in functions executed on the test machine.  
MLP performed better than SVM regarding deployed DF time-efficiency and cost-efficiency. While obtaining a classification with the SVM's smart contract DF takes almost 10 s, it takes less than a second to obtain a classification with MLP. However, both classifiers' DFs fell behind in comparison with the client test machine's performance, which could be because of the humble EVM abilities to execute complex math methods in contrast with the test machine's abilities.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Procedure** & **Method** & **Run Time (s)** & \multicolumn{2}{c}{**Expected (s)**} & **Time Avg (s)** \\ \cline{3-5}  & & **Runs** & **Time** & \\ \hline \multirow{2}{*}{Upload Dataset} & writeRecord uploadDataset & 0.57844 & 768 & 444.24  & 934.95  \\ \hline \multirow{2}{*}{Load Dataset} & readRecord loadDataset & 0.16492 & 768 & 129.6246  & 114.41  \\ \hline \multirow{2}{*}{Write Scalar Parameters} & setScalar & 20.05179 & - & 20.05 \\ \hline \multirow{3}{*}{Write SVM Parameters} & setSupportVector & 1.68084 & 395 & 663.9318  & 634.5  \\ \cline{3-5}  & setSVM & 0.47049 & - & 0.47049 \\  & setDualCoef & 46.240966 & - & 46.240966 \\ \hline \multirow{6}{*}{Write MLP Parameters} & setFirstWeights setSecondWeights setThirdWeights setBiases set
## 3 Results
### Accuracy Analyses of Experimental Results
#### 3.2.4 Effect of Gender Voice Differences on Accuracy  
In the experimental process, the system simultaneously sent a pair of audio stories through the left and right sides of the headphones for participants to listen to during each trial. There are twelve audio stories, six recorded in a male voice and six in a female voice. The auditory stimuli may or may not be the same-gender voice combination. A different-gender voice combination (DG) sends one male voice and one female voice simultaneously. The same-gender voice combination (SG) presents two male (or female) voices simultaneously. Next, we compared whether the auditory stimuli of different-gender voices or same-gender voices affected the accuracy, as shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subjects**} & \multicolumn{2}{c}{**N200**} & \multicolumn{2}{c}{**P300**} & \multicolumn{2}{c}{**N2P3**} \\  & **DG** & **SG** & **DG** & **SG** & **DG** & **SG** \\ \hline N01 & 46.00 & 64.00 & 82.00 & 62.00 & 88.00 & 60.00 \\ N02 & 68.00 & 72.00 & 48.00 & 32.00 & 72.00 & 66.00 \\ N03 & 44.00 & 60.00 & 60.00 & 64.00 & 60.00 & 70.00 \\ N04 & 62.00 & 74.00 & 60.00 & 50.00 & 82.00 & 80.00 \\ N05 & 46.00 & 58.00 & 52.00 & 54.00 & 60.00 & 68.00 \\ N06 & 62.00 & 48.00 & 44.00 & 66.00 & 64.00 & 66.00 \\ N07 & 64.00 & 54.00 & 56.00 & 60.00 & 60.00 & 70.00 \\ N08 & 60.00 & 56.00 & 68.00 & 68.00 & 70.00 & 76.00 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The average accuracies using different-gender (DG) and same-gender (SG) voices.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dependent Variable: Average Accuracies** & & & & **Unit \%** \\ \hline
**Components** & **T3** & **T4** & **Fz** & **Cz** & **Pz** & **NN Technology** \\ \hline N200 & 54.79 & 58.33 & 63.96 & 60.83 & 54.58 & 66.67 \\ P300 & 62.50 & 59.79 & 58.54 & 60.63 & 62.71 & 70.42 \\ N2P3 & 67.71 & 66.67 & 68.75 & 71.04 & 71.46 & 78.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the average accuracy of three ERP components (N2P3, P300, and N200).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Electrode(I)** & **Electrode(J)** & **Mean Discrepancy(I-J)** & _p-Value_ \\ \hline \multirow{3}{*}{N2P3} & N200 & 12.29167 *** & 0.000 *** \\ \cline{2-5}  & P300 & 8.54167 * & 0.011 * \\ \hline P300 & N200 & 3.75000 & 0.400 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Multiple comparison of the average accuracies calculated by the NN technology.  
After the paired samples t-test, there are no significant difference between the accuracies of different-gender voice combinations and same-gender voice combinations using components N200 and N2P3 except for P300 (\(p\) = 0.0353 * \(<\) 0.05). Additionally, the paired samples \(t\)-test results of the correct chosen R and the correct chosen L for all trials show no significant difference between the accuracies (Table 11).  
This result implies that accuracy is not affected by the gender voice combination played in each headphone if the system uses component N2P3 to distinguish EEG output data.
## 4 Prevention Mechanisms of the Proposed Scheme
### Trust Provision in the Proposed Scheme  
In order to provide trust in the network, it is anticipated that hybrid DoS attacks (HDSA), including DoS JSA and other attacks that may be hard to detect in the proposed scheme models, such as HDAM, PSAM, and PESATRM, have one solution that can also be devised to evaluate the probability information received through a consensus mechanism [46]. Thus, false information reaction due to the HDSAs, including DoS JSA and other attack, would require a vehicle to wait to receive given information based upon binary numbers (ones and zeroes).  
Let us consider a vehicle that transmits the information or message, where during the transmission, HDSAs, including DoS JSA and all forms of attacks, occurs because of the neighboring vehicles that disturb or amend the actual information. To secure the network, it is necessary to protect the network from all other forms of external attacks as well. In order to determine the attacks in the network, past information of the transmitting vehicles in the form of binary numbers are considered, on the basis of which the genuine vehicle makes a decision if whether the driver should consider the message as trusted for the vehicle. When the number of zeros is less than ones, the driver would consider the message as the genuine message, or otherwise would ignore the message [47].  
\[Vechile_{trust}=\sum number\ of\ ones \tag{12}\]  
To decide how instantly the receiving vehicle would trust the vehicle that transmits the message to the base station (RSU and FS), the following equation has been used:  
\[t\ (rv)=\sum_{i}t(rv\&ap)+t(ap\&S)+t(s\&frv)+t(p) \tag{13}\]As shown in the above equation, _t (rv)_ is the time to choose whether _rv_ (receiving vehicle) could trust the _sv_ (sending vehicle), _t(rv&ap)_ is the time of transmission and reception with the access points and vehicles, _t(ap&S)_ is the time of transmission and reception with the access points and server, _t(s&ftv)_ is the time of transmission and reception with the fog server and feedback of reporting vehicles, and _t(p)_ is the server's processing time [48].  
In the proposed scheme model, namely PSAM, the communication ranges from 250 to 500 m and the information is transmitted at 30 Mbps [49]. Therefore, the transmission time can be determined by using the following equation:  
\[\text{Time }=\frac{\text{Distance}}{\text{Speed}} \tag{14}\]  
Distance (d) can be computed by using the following equation:  
\[\text{D }=\sqrt{\left(y_{m}-y_{n}\right)^{2}+\left(x_{m}-x_{n}\right)^{2}} \tag{15}\]  
As shown in the above equation, \(\left(y_{m}-y_{n}\right)\) and \(\left(x_{m}-x_{n}\right)\) show the graph co-ordinates.
## 3 Results
### Elastic Analysis Results and Assessment  
The von Mises stress distribution of the structural components in the BB cap region is plotted in Figure 8. After neglecting the non-physical stress concentrations near the boundary conditions and the bonded contact regions, it is observed that some locations of the _first wall_, _breeder pins_, and _tubes_ are under relatively high stress (>350 MPa). The assessment following the elastic criteria for IPFL and ILFED damage modes is performed at a few selected locations and the results are presented in Table 1. Safety margins for both criteria are estimated as (Stress Limit--linearized stress value)/Stress Limit and expressed as a percentage. Identified high stressed regions and components are shown in Figure 9. It can be seen that both IPFL and ILFED margins are not satisfied in regions of _first wall_ and _breeder pins_. Due to coolant flow with a large temperature range, there are high thermal gradients between the components. The resulting secondary thermal stresses increase the total stresses higher than the elastic margins recommended by the elastic criteria.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Part**} & \multirow{2}{*}{**Loc.**} & \multirow{2}{*}{\begin{tabular}{c} **T\({}_{\text{average}}\)** \\ (\({}^{\circ}\)C) \\ \end{tabular} } & \(\overline{P_{L}\)+\(Q_{L}}\) & \(S_{e}^{D}\) & \begin{tabular}{c} **IPFL** \\ **(MPa)** \\ \end{tabular} & \(\mathbf{T_{point}}\) & \(\overline{P_{L}\)+\(P_{b}\)+\(Q}\) & \(S_{d}^{D}\) & \
\begin{tabular}{c} **ILFED** \\ **Margin** \\ \end{tabular} \\ \hline \multirow{5}{*}{
\begin{tabular}{c} **C\({}^{\circ}\)C** \\ \end{tabular} } & **A** & 480 & 68 & 287 & 76\% & 514 & 211 & 388 & 46\% \\ \cline{2-9}  & **B** & 488 & 39 & 283 & 86\% & 521 & 199 & 382 & 48\% \\ \cline{2-9}  & **C** & 465 & 201 & 295 & 32\% & 466 & 300 & 398 & 25\% \\ \cline{2-9}  & **D** & 471 & 276 & 292 & 5\% & 474 & 308 & 394 & 22\% \\ \cline{2-9}  & **E** & 384 & 282 & 329 & 14\% & 386 & 311 & 444 & 30\% \\ \cline{2-9}  & **F** & 551 & 94 & 244 & 62\% & 572 & 104 & 330 & 68\% \\ \cline{2-9}  & **G** & 518 & 167 & 269 & 38\% & 524 & 237 & 354 & 33\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assessment against IPFL and ILFED damage modes according to elastic criteria.  
Figure 7: Temperature distribution (**a**) on the whole BB cap region, and (**b**) on Eurofer97 structures.
## 4 Results and Discussion  
### Full-Scale Model Results  
#### 4.1.1 MSE Wall Loading  
The fully instrumented model was completed and incrementally loaded until failure occurred in the MSE wall structure (either fast displacement occurred or a steel bar ruptured). For each interval, the load increment was maintained for 30 min. The total dead loads for the steel beams and concrete slab were both 12 kN/m\({}^{2}\). The design load of the model was 20 kN/m\({}^{2}\)[12]. The maximum load (when the steel reinforcement bars were ruptured) was 302 kN/m\({}^{2}\), approximately 15 times greater than the normal traffic loading of 20 kN/m\({}^{2}\). The stress, strain, and displacement of the wall, the longitudinal reinforcement bars, and the reinforced soil mass were recorded at load levels of 12, 20, 50, 75, 100, 150, 200, 250, 275, 300, and 302 kN/m\({}^{2}\).