esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Feed Source through Three Dimensional Printing
## 4 Result and Discussion
Interestingly, measured directivity and gain values in 10, 11, 12, 13, 14, 15 GHz frequencies are 21.2, 23.2, 23.4, 22.9, 23.9, 23.6 dBi and 20.9, 22.9, 23.1, 22.7, 23.5, 22.6 dBi, respectively. The standard horn antenna [25] with an overall height of around 225 mm was considered to realize the analysis of aperture phase uniformity, which is supportive of understanding the requirement of the full phase of 360\({}^{\circ}\). This will be helpful for understanding the metasurface required for the beam steering and beam deviation phenomena. The use of the proposed superstrate is applicable for standard gain horn antenna with a defined length. The shorter horn with a lower gain was unable to depict the beam steering or deviation phenomena, even though the superstrate enhances the gain. This is due to the aperture phase correction requirement where a standard gain horn antenna with a defined aperture dimension of 98.5 mm \(\times\) 75.6 mm is able to generate more uniformity in phase distribution with a lower side lobe level of around \(-\)17.5 dB. Table 4 shows the preference of the proposed WBP structure as compared with other design aspects. It shows wide band operation in the Ku-band, ranging from 10 GHz to 15 GHz. The aperture dimension is relatively smaller and has higher directivity and gain values with an improved 3 dB bandwidth. Additionally, side lobe levels are comparatively lower, which has generated better radiation patterns. Figure 9: Simulated and measured radiation patterns of overall system with WBP structure above the feed source are shown in Figure 9 (**a–f**) for 10 GHz, 11, GHz, 12 GHz, 13 GHz, 14 GHz, and 15 GHz frequencies.
## 5 Proposed Model Architecture
### Proposed U-IntentCapsNet
#### 5.2.2 WordCaps  
WordCaps is the first capsule layer that learns the contextualized word representation for each word vectorized through the embedding layer, incorporating the context of  
Figure 3: U-IntentCapsNet architecture for intent detection using capsule neural network.  
the query. A bidirectional recurrent neural network with LSTM cells [28] is applied to sequentially encode the backward and forward context of information in the query:  
\[\overset{\rightarrow}{h}_{t}=LSTM_{fw}\;(w_{t},\;\overset{\rightarrow}{h}_{t-1}) \tag{1}\]  
\[\overset{\leftarrow}{h}_{t}=LSTM_{bw}\;(w_{t},\;\overset{\leftarrow}{h}_{t+1}) \tag{2}\]  
To obtain the query level encoding \(h_{t}\) for a given word \(w_{t}\), each forward hidden state vector \(\overset{\rightarrow}{h}_{t}\), obtained from the forward \(LSTM_{fw}\) as given in Equation (1), is concatenated with a backward hidden state vector \(\overset{\leftarrow}{h}_{t}\), obtained from the backward \(LSTM_{bw}\) as given in Equation (2), to obtain a hidden state \(h_{t}\) for the word \(w_{t}\). The complete hidden state matrix is defined as \(=(h_{1},h_{2},h_{3},\ldots,h_{T})\in R^{\;T\times 2D_{H}}\), where \(D_{H}\) is the number of hidden units in each LSTM cell. The output of the WordCaps are low-level vector representations extracted from the queries.
## 2 Measuring Devices and Systems
### Strain Gauge-Type Measurement System  
The strain gauge-type measurement system consists of a strain gauge sensor, transmitter, receiver, RPM sensor and laptop, as shown in Figure 1.  
The installation of the gauges on the shaft required careful treatment due to gauge's high sensitivity to the conditions of installation [21]. Therefore, before fitting the strain gauges, the sensor area on the shaft was polished well. The gauges and the amplifier were powered by batteries mounted on the shaft. For the wireless data acquisition, an antenna was also installed next to the shaft, as shown in Figure 2. To measure the shaft speed, a reflective plate was attached to the shaft line and an optical sensor was mounted close to the shaft. Each time the plate passed the sensor window a voltage signal was transmitted to a recording device, which converted to the shaft speed to rpm.  
A torque measuring full-bridge arrangement with strain gauges at an angle of 45\({}^{\circ}\) referred to the shaft axis and the values are usually attained to a high degree of accuracy [22], with conventional thrust measuring full-bridge arrangement with strain gauges along and rectangular to the shaft axis [23], as shown in Figure 3.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 3 Traffic-Assisted BOF Vulnerability Mining and Exploitation Method in ADASs
### Exploitation of BOF  
The validation of BOF vulnerabilities often only requires the construction of a data packet of the corresponding size or length. However, this is not enough to facilitate advanced exploitation and intrude into an ADAS more deeply. Generally, it is necessary to combine IDA Pro or other decompiling tools to determine the buffer size and its relative position. At the same time, one must use tools or manually write a payload to write executable code into the program's buffer. Additionally, the payload should contain special jump instructions to overwrite the control flow pointer and redirect program execution to the corresponding code for execution.  
For example, typical BOF attack payloads include No Operation (NOP) slide and return-oriented programming attacks. With a NOP slide attack, an attacker adds a chain of NOP instructions before the malicious code, enabling them to slide to a specific location in the buffer and then jump to execute the malicious code. Another technique is ROP, where an attacker overwrites a return address to direct the program flow to a buffer section containing \"gadgets\"--instruction sequences (e.g., shellcode) that the attacker assembles to achieve arbitrary code execution.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 5 Results of the CIR-Based Methods
### CNN Results  
The proposed CNN architecture in Figure 4 is able to directly work on the CIR, which has benefits of reducing the operator influence. The results of the CNN method are shown in Table 3. Compared to the results in Table 1, the CNN results are close to the best performance using traditional features for the KWAUG14 dataset and superior for the more complex SPACE08 dataset. When both datasets are combined into one dataset, the model still performs well, close to or even better than independent models.
## 4 Simulation Study
### Case Study 1: Without Attacks  
In this case study, the IEEE 118 bus system is modeled as a directed graph using the earlier methodology. Then, the proposed method is applied to identify the critical nodes in the IEEE 118 bus system. The nodes are ranked based on their controllability indexes and the nodes are regarded as critical if they have higher values of the controllability index. The normalized controllability indexes of critical nodes of the IEEE 118 bus system are plotted in Figure 4. The results are averaged over 100 realizations. In terms of the controllability index, it can be clearly see that some nodes are given higher ranking in this analysis, and they are considered of criticality. From the point of view of controllability, those nodes with higher values of indexes play an important role in maintaining full control of the system. If they are in faults, the whole power system may not be controllable or may need more external inputs to maintain the controllability. Furthermore, the robustness of the network will be test in case study 2 and 3.  
Figure 3: IEEE 118-bus Test system.
## 4 Results and Discussions
### Acoustic Performance Using Structures with Face sheets of Different Thickness  
The STL curves obtained using three Nomex honeycomb sandwich structures constructed using face sheets of different thickness are shown in Figure 12a. The STL in the 1.6-2.0 kHz region, however, significantly increased as the thickness of the plates was increased.  
Moreover, the figure also shows that the STL valley point of the honeycomb sandwich structures shifted in the low-frequency direction as the thickness of the sheets was increased. The STL greatly improved with increasing thickness of the face sheets, the average STL successively rising by 9 dB (B\({}_{2}\) versus B\({}_{1}\)) and 7 dB (B\({}_{3}\) versus B\({}_{2}\)). Combined with a sandwich skin, the core can provide sample stiffness, and the increase in the face sheet thickness resulted in an increase in the mass per unit area. The overall STL increased as the face sheet thickness was increased after the resonance region.  
Figure 12b shows a comparison of an experimental curve (with \(h_{f}\) = 1.5 mm) and the corresponding results obtained via simulation. It can be seen that the simulation reproduced the general trends in the experimental curve. The curve trend of the BEM simulation result was similar to the test result, but in the frequency band after 5000 Hz, the BEM simulation result was larger than the test. For the FEM  
Figure 11: Simulated mode shapes for the A\({}_{2}\)-type panel: (**a**) mode at 5149 Hz, (**b**) mode at 6818 Hz, (**c**) mode at 6854 Hz.  
Figure 12: Experimental and simulated STL results for type B structures, showing: (**a**) test results for three structures with plates of different thickness; and (**b**) a comparison of the test and simulated results for the B\({}_{3}\) structure.  
simulation result, it was basically consistent after the 3500 Hz frequency band, it had a higher degree of agreement with the test results than the BEM. However, the difference between the FEM and the test results was in the 1.5-2.5 kHz.  
For the impedance tube test, the sound pressure of the test was calculated by calculating the average value of the sound pressure during the test time. It was difficult for the random sound source from the speaker to reach a steady state, because its sound pressure and frequency are not steady. So the sound pressure of the test floated up and down in a steady state, which caused some measurement errors. In the BEM, ignoring the loss of the tube wall, the sound wave generated by the plane sound source can only pass through the coupling mesh. The results of the BEM exhibited some difference to the test results in the resonance region. The FEM results, considering the influence of the tube wall, was in agreement with the test results in the resonance region, but had a difference in the stiffness region.  
Figure 13 shows the shapes of the simulated modes for the B\({}_{3}\)-type panel, which basically conformed to those in Figure 11. This also indicates that the trends observed in the STL curves of the honeycomb sandwich panels were influenced by the structural modes of the panels.
## 5 Implementation and Results
### Experiment 2  
In this experiment, we investigated the impact of incorporating the optimized wrapper feature selection method based on the bio-inspired algorithms (GWO, FPA, BA, and ABC) into the used predictive classifiers (NB, KNN, SVM, and DT) using two datasets (PBC and GE). Table 6 presents the obtained results of the proposed models. Regarding the PBC dataset, Figure 3 shows the obtained accuracy results for the proposed models. It can be seen from Table 6 and Figure 3 that the GWO-SVM and GWO-DT models gave the highest accuracy results of 99.66% and 98.29%, respectively, compared to the GWO-NB model (97.58%), followed by the GWO-KNN model (96.89%). The FPA-SVM model achieved the highest accuracy of 99.56% compared with the FPA-DT model (96.21%) and the FPA-KNN (95.52%), while the lowest accuracy was obtained by the FPA-NB model (94.88%). On the other hand, the BA-based wrapper models gave the highest accuracy with the BA-SVM (98.97%) and BA-NB models (97.60%) compared to the BA-DT model (96.22%), followed by the BA-KNN model (93.14%). For ABC-based wrapper models, the ABC-SVM model gave the highest accuracy (98.63%) compared with the ABC-KNN model (98.27%) and the ABC-DT (97.73%). The ABC-NB model gave the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Data Type** & \multicolumn{4}{c}{**PBC**} & \multicolumn{4}{c}{**GE**} \\ \hline
**Eva. Metrix** & **Acc** & **F1-Score** & **Precision** & **Recall** & **AUC** & **Acc** & **F1-Score** & **Precision** & **Recall** & **AUC** \\ \hline NB & 93.49 & 93.0 & 94.0 & 93.0 & 93.52 & 66.7 & 64.0 & 73.0 & 67.0 & 67.0 \\ \hline KNN & 89.03 & 89.0 & 89.0 & 89.0 & 90.0 & 56.66 & 57.0 & 77.0 & 57.0 & 59.9 \\ \hline SVM & 91.7 & 92.0 & 92.0 & 92.0 & 92.2 & 80.0 & 80.0 & 82.0 & 80.0 & 80.3 \\ \hline DT & 95.5 & 96.0 & 96.0 & 96.0 & 95.9 & 86.6 & 87.0 & 87.0 & 87.0 & 88.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: First Experiment Results. lowest accuracy of 95.54%.According to the obtained results, GWO-SVM had the best classification performance on the PBC dataset compared to the remaining classifiers. Regarding the GE dataset, Figure 4 shows the accuracy results of the proposed models. GWO-SVM had the highest accuracy of 99.34% compared to the GWO-DT model (80.0%), followed by the GWO-KNN (63.34%) and GWO-NB models (63.33%). As for the FPA-based models, the FPA-SVM model gave the highest accuracy (96.67%) compared with the FPA-DT model (76.66%) and the FPA-NB (70.0%). The FPA-KNN model had the lowest accuracy of 60.0%. On the other hand, the BA-based models gave the highest accuracy
## 3 Model Identification
However, when comparing models using a test set, it does not matter how the forecasts were produced, the comparisons are always valid even if the models have different orders of differencing and/or different transformations. This is one of the advantages of the cross-validation procedure used here-- to be able to compare the forecasting performance of models that have different orders of differencing and/or different transformations. The other advantage of the cross-validation procedure used here is that it also tells how accurate the one-step forecasts can be. The procedure based on cross-validation to identify an appropriate state space model and an appropriate ARIMA model for a time series follows these steps:  
1. Raw data and log-transformed data (to stabilize the variance if necessary) are considered and for each one 1. in the case of ARIMA,if the time series is nonseasonal, \((m=1)\) the data are differentiated zero, one and two times \((d=0,1,2)\) and for each type of data, \(p\) and \(q\) vary from 0 to 2 giving 36 models; * if the time series is seasonal \((m>1)\), the data are differentiated considering all of the combinations of \(d=0,1,2\) with \(D=0,1\) (six in total) and for each type of data, \(p\) and \(q\) vary from 0 to 5 and \(P\) and \(Q\) vary from 0 to 2 giving 324 models. 2. in the case of ETS, * if the time series is nonseasonal \((m=1)\), all of the combinations of (E,T,S), where \(\text{Error}=\{\text{A},\text{M}\}\), \(\text{Trend}=\{\text{N},\text{A},\text{A}_{\text{d}},\text{M},\text{M}_{\text {d}}\}\) and \(\text{Seasonality}=\{\text{N}\}\) are considered, giving 10 models; * if the time series is seasonal \((m>1)\), all of the combinations of (E,T,S), where \(\text{Error}=\{\text{A},\text{M}\}\), \(\text{Trend}=\{\text{N},\text{A},\text{A}_{\text{d}},\text{M},\text{M}_{\text {d}}\}\) and \(\text{Seasonality}=\{\text{N},\text{A},\text{M}\}\) are considered, giving 30 models. 2. The data set \((y_{1},\ldots,y_{T})\) is split into a training set \((y_{1},\ldots,y_{N})\) and a test set \((y_{N+1},\ldots,y_{T})\) (about 20%-30% of the data set) and time series cross-validation is performed to all ARIMA and ETS models considered in step 1 as follows: 1. The model is estimated using the observations \(y_{1},\ldots,y_{N+t}\) and then used to forecast the next observation at time \(N+t+1\). The forecast error for time \(N+t+1\) is computed. 2. The step (a) is repeated for \(t=0,1,\ldots,T-N-1\). 3. The value of RMSE is computed based on the errors obtained in step (a). 3. The model which has the lowest RMSE value and passes the Ljung-Box test using all the available data with a significance level of 5% is selected among all ETS and ARIMA models considered. If none of the fitted models passes the Ljung-Box test, the model with the lowest RMSE value is selected among all considered.
## 3 Results
### Description of Response to Perturbation for Kinematic and Kinetic Parameters--Reactive Limb
#### 3.3.3 Kinetic Parameters  
Regarding kinetic parameters for the reactive limb (Figure 11), it is possible to note that extreme values were higher in young people in most cases. The most statistically significant differences were recorded for treadmill gait (all parameters), Strategy III (five parameters), and Strategy I and II (two parameters).  
Non-significantly higher values of muscle torque in the elderly group were for hip extension (Strategy I), dorsiflexors, and knee extensors (Strategy II). Significantly higher values of muscle torque were recorded only for treadmill gait for knee extensors. It is important to note that in Strategy III, all the torque values were lower in the elderly group.  
Table 5 is the last one, which contains the differences between the extreme values recorded for each strategy and treadmill gait. The differences relate to the values recorded for joint torques and look as follows. The first strategy included four minimal values, one mean, and one maximum value. Strategy II: one minimum, two middle, and two maximal values. Strategy III: one minimum, three means, and two maximum values. In the elderly group, the distribution looked different. The first strategy contained four middle and two maximum values. Strategy II comprised two mean and four maximum values. The third strategy had extreme values most similar to those recorded for treadmill gait without perturbation (six minimum values).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Ankle [deg]**} & \multicolumn{2}{c}{**Knee [deg]**} & \multicolumn{2}{c}{**Hip [deg]**} \\ \cline{2-7}  & **min \(-\) pf** & **max \(-\) df** & **min \(-\) ext** & **max \(-\) flx** & **min \(-\) ext** & **max \(-\) flx** \\ \hline \multicolumn{7}{c}{**Young**} \\ \hline I–n & 7.1 (mean) & \(-\)0.88 (max) & 5.65 (min) & \(-\)0.81 (mean) & 25.91 (max) & 30.01 (mean) \\ II–n & 4.24 (min) & 0.13 (min) & 8.45 (mean) & 0.69 (min) & 14.68 (min) & 23.8 (min) \\ III–n & 13.41 (max) & 0.31 (mean) & 9.33 (max) & 3.31 (max) & 25.23 (mean) & 30.41 (max) \\ \hline \multicolumn{7}{c}{**Elderly**} \\ \hline I–n & 10.42 (max) & 4.19 (mean) & 8.21 (mean) & 1.25 (min) & 14.33 (max) & 8.03 (mean) \\ II–n & 0.35 (min) & 3.75 (min) & 4.09 (min) & 3.53 (mean) & 1.41 (min) & 8.2 (max) \\ III–n & 3.68 (mean) & 4.42 (max) & 9.01 (max) & 9.39 (max) & 10.97 (mean) & 8 (min) \\ \hline \end{tabular}
\end{table}
Table 4: Differences between the means of the extreme values adopted in Strategies I, II, III, and those for gait on a treadmill without perturbation (n) for kinematic parameters of the reactive limb with the indication of their spread (min, mean, max) relative to each other. For negative values, the comparison was for absolute values.
# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
## 4 Results
### Terminology & Definitions  
Below, we define various terms [31] that are usually used in computer vision methods:  
Confidence is a value that is determined by the model when it detects an object. The model has X amount of confidence that object Y is exactly that prediction. The confidence is irrelevant to the accuracy of the model's prediction; it only represents the similarity of the predicted class, from the training dataset.  
A True Positive (TP) is when the model's results contain a prediction of an object and it is correct, based on the ground truth information. True positives are also detections of the correct class.  
A False Positive (FP) is when the model's results contain a prediction of an object and it is incorrect, based on the ground truth information. False positives are also detections of the wrong class.  
A True Negative (TN) is when the model's results contain no predictions and this is correct based on the ground truth information.  
A False Negative (FN) is when the model's results contain no prediction and it is incorrect based on the ground truth information. False negatives are detections not given by the model.  
The mean Average Precision (mAP) is calculated using the true positive/negative and false positive/negative values. The mAP determines the performance of the model in a class.  
Precision is the ratio between the correct predictions of the model and the incorrect classifications of the model, limited to the ground truth information:  
\[\textbf{Precision}=\frac{\textbf{True Positives}}{\textbf{True Positives + False Positives}} \tag{2}\]  
The average Intersection over Union (IoU), is a statistical comparison between the bounding boxes of the model's prediction and the ground truth's bounding box. The average IoU value represents the difference between the prediction and the ground truth's bounding box of multiple objects. The higher the value, the better the prediction is.  
As outlined above, the average IoU is calculated using the ground truth and the prediction bounding box coordinates. In the case of Darknet and the YOLO models, we used DarkHelp to extract the coordinates of each prediction of our model and compared those predictions to the ground truth boxes that we labelled.  
In order to calculate the IoU probability, the intersection's box coordinates need to be defined. Using the intersection area, we can also calculate the union's bounding box area. The ratio of the intersection divided by the union is equal to the IoU value.  
\[\textbf{Intersection over Union (IoU)}=\frac{\textbf{Intersection box Coordinates}}{\textbf{Union box Coordinates}} \tag{3}\]
## 4 Application to Earthquake Monitoring
### PPP during the Earthquake  
The seismic displacements associated with the earthquake at station LASA were retrieved using BDS PPP and GPS PPP, respectively. As shown in Figure 5, the displacements from BDS PPP are consistent with those from GPS PPP; the RMS of differences between GPS and BDS displacements are 0.29, 0.38, and 1.08 cm in east, north, and vertical components, respectively. It is worth noting that the RMS of differences during the earthquake is calculated from only 3 min length of data (from 6:14:00 to 6:17:00 UTC). As a comparison, we randomly took five segments of data, each with a length of 3 min, to demonstrate the differences between GPS and BDS solutions for the period before the earthquake. We also computed the RMS for all three components, which are listed in Table 7. The average RMS values in Table 7 clearly show that the differences between GPS and BDS solutions before the earthquake are highly consistent with those from the data during the earthquake.  
Meanwhile, there is a strong motion station, LSA, which records accelerometer data, located closely to station LASA, with a distance of 5.5 km. To further validate the reliability of the displacements derived from LASA, we also derived the displacements from accelerometer recordings observed at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **East** & **North** & **Up** \\ \hline BDS-only & 0.6 & 0.8 & 4.3 \\ GPS-only & 0.9 & 0.9 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMS of BDS PPP and GPS PPP solution errors over the period from 14:00 to 22:00 (unit: cm).  
Figure 5: The displacement waveforms from GPS PPP and BDS PPP at LASA and from strong motion station LSA over the period from 6:14:00 to 6:17:00 (UTC).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sessions (3 min)** & **East** & **North** & **Up** \\ \hline
1 & 0.47 & 0.35 & 1.25 \\
2 & 0.32 & 0.29 & 1.10 \\
3 & 0.39 & 0.35 & 0.81 \\
4 & 0.40 & 0.39 & 0.94 \\
5 & 0.42 & 0.37 & 1.14 \\ Ave & 0.40 & 0.35 & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 7: RMS of differences between GPS and BDS solutions for the period before the earthquake with 3 min length of data (unit: cm).  
station LSA with a double integration and baseline correction scheme described in [27]. Figure 5 shows good agreement between the GNSS and strong motion instruments during the fluctuation associated with the earthquake.