esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ML Methodologies Used for LA Prediction Modeling
#### 2.4.2 Support Vector Regression (SVR) Models  
SVR is based on the Vapnik-Chervonenkis (VC) theory which in principle is structural risk minimization [78] and is an effective tool for estimating real-valued functions [79]. The advantages of SVR, which was harnessed in this study, is its highly effective performance for both the linear and non-linear regression dataset and not too large dataset which results in the use of Gaussian RBF kernel hyperparameter (for non-linear regression) thus mapping each training instance to an infinite-dimensional space [80]. The other usefulness of SVR can be summarized as its excellent capability of generalization with high accuracy prediction. Default hyperparameters used in sklearn.svm.SVR are kernel = 'rbf', degree = 3, gamma ='scale', coeff0 = 0.0, tol = 0.001, C = 1.0, epsilon = 0.1, shrinking = True, cache_size = 200, verbose = False, and max_iter = \(-\)1.
## 3 Experimental Results
### Long-Term Settlement Prediction and Consolidation Rate  
The regression of settlement measurements was carried out from the drone-based surveying and the surface settlement, as shown in Figure 11. The cumulative settlement \(\Delta\)S was computed with \(\Delta\)t (date of elapsed) to determine the straight regression line in order to predict a long-term settlement and consolidation.  
Table 5 shows the two predictions from the drone survey data and the surface settlements. The predicted settlement from the surface settlement was 3.162 m, 2.293 m, 1.382 m, and 1.420 m for K-SK-10, K-SK-11, K-SK-14, and K-SK-19, respectively. The consolidation rate was 67.06%, 62.71%, 63.33%, and 53.29% for K-SK-10, K-SK-11, K-SK-14, and K-SK-19, respectively. The difference in the predicted settlements ranged from -906 mm to 192 mm, and the consolidation rate difference was \(-\)5.56% to 8.44%.
# Optical Trapping, Sizing, and Probing Acoustic Modes of a Small Virus
## 2 Materials and Methods
### Sizing  
Trapping experiments were performed on two sizes of commercial polystyrene spheres (Thermo Fisher Scientific Nanosphere 22 \(\pm\) 2 nm and 31 \(\pm\) 3 nm) and PhiX174 coliphage virus sample (Carolina 124425 at 10\({}^{10}\) plaque-forming units/mL). Virus sample was purified via filtration (VWR International 0.2-\(\upmu\)m cellulose acetate sterile syringe filter) and centrifuged at an estimated 30,000\(\times\)_g_[77] for 15 min, before the supernatant was removed. For full details of the sizing technique, see past studies [3; 23; 27]. Each type of sample was trapped multiple times, and autocorrelation of the transmission signal after trapping was calculated. The characteristic time, \(\tau\)2, was extracted by fitting the autocorrelation curve to a double exponential. For this analysis, the particles are taken to be spherical. Since the radius of the particle is comparable to half the distance between two walls of the aperture in the trapping region, only the first-order terms are taken to calculate the Stokes drag coefficient via Faxen's Law. This leads to a simple linear dependence between Stokes drag coefficient and the particle radius. This implies that the time constant, \(\tau\)2, is directly proportional to the radius of trapped spherical particles as well. For each type of sample, the average of the computed \(\tau\)2 value was taken over the multiple trapping events. Finally, this averaged \(\tau\)2 of was plotted against particle size, and fit to a line.
## 4 Experiments
### Omnidirectional Bee Traffic  
In our previous article [5], we proposed a two-tier algorithm for counting bees in videos that couples motion detection with image classification. The algorithm uses motion detection to extract candidate bee regions and applies trained image classifiers (e.g., a convolutional network (ConvNet) or a random forest) to classify all candidate region as containing or not containing a bee. The algorithm is agnostic to motion detection or image classification methods insomuch as different motion detection and image classification algorithms can be paired and tested. We experimented with pairing three motion detection algorithms in OpenCV 3.0.0 (KNN [25], MOG [26], and MOG2 [27]) with manually and automatically designed ConvNets, support vector machines, and random forests to count bees in 4 manually curated videos of bee traffic. We curated these 4 videos in the same way as the 32 videos described in this article. Our experiments identified the following four best combinations MOG2/VGG16, MOG2/ResNet32, MOG2/ConvNetGS3, MOG2/ConvNetGS4 (see our previous article [5] for the details of how we trained and tested the convolutional networks VGG16, ResNet32, ConvNetGS3, and ConvNetGS4).  
We compared the performance of BeePIV with DPIV_A (see Equation (34)), BeePIV with DPIV_B (see Equation (35)), MOG2/VGG16, MOG2/ResNet32, MOG2/ConvNetGS3, and MOG2/ConvNetGS4 on the twelve videos in our evaluation dataset in terms of absolute omnidirectional error. Figure 17 shows the result of our comparison. The absolute omnidirectional error was the smallest for DPIV_B for each video with DPIV_A being close second. The methods MOG2/VGG16, MOG2/ResNet32, MOG2/ConvNetGS3, MOG2/ConvNetGS4 performed on par with DPIV_A and DPIV_B on lower traffic videos but overcounted bee motions in higher traffic videos. Table 2 gives the PIV parameters we used in this experiment.  
Figure 17: Absolute omnidirectional error of six bee counting algorithms on twelve evaluation videos; VGG16 refers to MOG2/VGG16; ResNet32 refers to MOG2/ResNet32; GS3 refers to MOG2/ConvNetGS3; GS4 refers to MOG2/ConvNetGS4.
## 4 The Use of SLM Technology in the Production of Aviation Hydraulic Parts  
### The Process of Manufacturing and Integrating the Hydraulic Actuator  
Based on the research carried out on the strength of the AM samples, the aircraft flight control actuator was designed to achieve the highest level of safety integrity along with the greatest simplicity and lowest weight. The architecture aims to simplify the methods of failure detection, which need to be included within the flight control computer [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. The general schematic of the flight control actuator (FCA) is presented in Figure 9. The servo actuator system is a dual tandem configuration with a hydro-mechanical anti-jam mechanism. It has two direct-drive servo valves with mechanical feedback linkage from the main ram to the secondary outer sleeve of the servo valve.  
The center manifold is a key part of the actuator. It contains a rotary torque motor, two rotary spools, two sleeves, and position sensors coupled on one shaft. The electronic system is a fully quadruplex architecture and is fail operational even in the event of three failures. It includes a redundant three-phase channel motor with independent drives for each control lane. Each of the stator sectors is mechanically separated and capable of driving the load of the rotor in the event of failure. All hydraulic elements inside the valve are pressure- and flow-balanced in order to achieve low friction and hysteresis. The servo valve cross section schema is presented in Figure 10.  
A dual hydraulic arrangement with independent valves allows one failure--either mechanical or hydraulic--without performance loss. The actuator receives four electrical command signals and two hydraulic supplies. The actuator can produce ram displacement in response to electrical command signals and continue operation after the failure of any input or internal part.  
FCAs are complex items. The use of AM technology has significantly influenced the design and manufacture of many complex components included within the assembly of the actuator to meet the rigorous safety-critical requirements. Selected elements of FCAs manufactured by AM technology and the full assembly of the complete actuator are presented in Figures 11-14. The presented FCA is lighter and has fewer parts [42]. It is approximately half the weight of the equivalent mechanical servo and less than half the weight of any FBW alternative.  
Figure 9: General schematic drawing of the FCA; own drawing based on patent [40] and Domin company materials [41].  
Figure 11: Manufacturing of actuator bodies with AM technology.  
Figure 12: Actuator components made with AM technology ready for integration: primary control sleeves.  
Figure 10: General schematic of the servo; own drawing based on patent [40] and Domin company materials [41].
# Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM
## 3 The Proposed Model Architecture
### Named Entity Recognition  
In the NER task, we aim to extract information from the tweets. Illustrated in Figure 3, our model is based on an Iterated Dilated Convolutional Neural Network (IDCNN) [38] and BiLSTM. We used the same word embedding layer of cyber threat event detection, which can map each word to a sequence, and use the IDCNN model and BiLSTM model to extract features. Because the dimensions extracted by the two models are different, the dimensions need to be compressed, transformed, and then spliced. Finally, the spliced result is used as the input of the CRF layer. The BiLSTM has been introduced earlier, next, we will introduce the IDCNN.  
_The IDCNN Model:_ Just as shown in Figure 4, the realization of dilated convolution is the same as that of standard convolution, except that dilated convolution is filled with gaps in the convolution kernel. Therefore, it can expand the convolution kernel's perception horizon and obtain multi-scale information, which is beneficial to the neural network to obtain the context information of tweets and improve the model's performance. In the model, we first conduct two conventional one-dimensional convolution operations on the word embedding layer's output, followed by a dilated convolution operation to replace the maximum pooling layer in the standard convolutional neural network finally connect the output with two full connection layers.  
Figure 2: The architecture of BiLSTM.  
_The layer of Output:_ BiLSTM network can capture the sequence dependence of sentences; simultaneously, the IDCNN network can better capture the sentence context information. The matrix obtained by combining the output results of the two can enrich the sentence information. The two models can only extract statement characteristics, but cannot predict the current entity tags, and cannot consider the dependencies between tags. The CRF algorithm can consider the adjacent relation between tags and obtain the global optimal tag sequence, so we use it as the output layer of the NER task. CRF defines a tag transfer score, where the score from an input sequence to a tag sequence can be expressed as the below Equations (2)-(4),  
\[s(x,y)=\sum_{i=1}^{n}\left(W_{y_{i-1},y_{i}}+P_{i,y_{i}}\right) \tag{2}\]  
where \(x\) is the given sequence and \(y\) is the corresponding tag sequence. \(W\) is the transformation matrix, \(W_{i,j}\) represents the label transfer score, and \(P_{i,y_{i}}\) represents the score of the \(y_{i}\) tag of the character. The maximum likelihood function of \(W\) is as below.  
\[L=\sum_{i=1}^{n}\log\left(P\left(y_{i}\mid x_{i}\right)\right)+\frac{\lambda} {2}\|\theta\|^{2} \tag{3}\]  
where \(\lambda\) and \(\theta\) are the regularization parameters, and \(P\) represents the corresponding probability from the original sequence to the prediction sequence; its formula is expressed as below.  
\[P(y\mid x)=\frac{\mathrm{e}^{s(x,y)}}{\sum_{y\in Y_{x}}\mathrm{e}^{s(x,y)}} \tag{4}\]
## 4 Discussion
### Model Generalization Evaluation  
The trained model was evaluated using a test dataset. However, the accuracy of the trained model depends on the test dataset, making the results unreliable. Therefore, the generalizability of the DNN model should be evaluated. K-fold cross-validation [40] solves this problem by dividing the dataset into k numbers of subsets and ensuring that each subset is used as a test dataset at different folds, which ensures that our model does not depend on training and test datasets.  
The dataset was split into four folds (Figure 8). In the first iteration, the first fold was used to test the model, and the rest were used to train the model. In the second iteration, the second fold was used as the testing dataset, while the rest served as the training dataset. This process was repeated until each fold of the four folds was used as a test set.  
After k-fold cross-validation, SSIM and PSNR were calculated. As shown in Table 12, the accuracy is similar in all four folds, indicating the generalizability of the proposed DNN architecture. It also means that our algorithm is consistent, and we can be confident that by training it on the dataset generated with other simulation parameters can lead to similar performance.
## 4 Solution Method for Optimization Model
### Mopso Algorithm  
MOPSO (Multi-Objective Particle Swarm Optimization) is developed from PSO. The key strategy is using a non-dominated set to maintain and update the frontier solution. Thus, the problem of local optimum can be avoided. The application process of MOPSO in solving the proposed model is shown in Figure 3. The procedures are listed as follows.  
Figure 3: Flow chart of solving proposed model by MOPSO.  
1. Initialize the population: Randomly generate N initial solutions as the population of the first generation. Randomly generate the velocity and position of the initial particle. N is set as 300. The maximum number of iterations is set as 100.
2. Update individual optimal solution \(p_{\text{best}}\) and global optimal solution \(g_{\text{best}}\): The previous best position of a particle is \(p_{\text{best}}\). If the \(p_{\text{best}}\) of the updating particle is non-dominated with the previous best position, make a selection between them with the probability of 0.5. Find the current best particle set by Pareto front. Select the least crowded particle as the best particle.
3. Update the particles' velocities and positions: Update the particles' velocities according to the current velocity, \(p_{\text{best}}\) and \(g_{\text{best}}\). Update the particles' positions.
4. Calculate the objective function value: Calculate the power flow. Check the constraints. Make modifications for individuals not meeting the constraints. Calculate the objective function value.
5. Non-dominated sorting according to the individual fitness: Calculate the crowding degree.
6. Select the particle swarm of the next generation according to the results of step (5).
7. Judge the terminal condition. If the condition is met, end the solving process. If not, repeat step (2)-(6).
# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 4 Experiments and Setup
### Comparison Approaches
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Rows (Samples)** & **Columns (Features)** & **Class 1** & **Class 2** \\ \hline CHOL & 44 & 43,697 & 39 & 5 \\ \hline COAD & 522 & 37,677 & 509 & 13 \\ \hline HNSC & 564 & 35,958 & 535 & 29 \\ \hline KICH & 91 & 43,806 & 60 & 31 \\ \hline KIRC & 613 & 44,909 & 609 & 4 \\ \hline KIRP & 322 & 44,874 & 236 & 86 \\ \hline LIHC & 421 & 35,924 & 322 & 99 \\ \hline LUSC & 553 & 44,894 & 494 & 59 \\ \hline PRAD & 553 & 44,824 & 472 & 81 \\ \hline STAD & 448 & 44,878 & 358 & 80 \\ \hline THCA & 564 & 36,120 & 380 & 184 \\ \hline UCEC & 588 & 36,086 & 345 & 243 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the datasets. **RR (Ridge Regression)**: Ridge Regression integrates feature selection within the training process of a model. It is an example of an embedded method. During model training, Ridge Regression calculates coefficients for each feature, indicating their importance. Features with coefficients above a specified threshold (e.g., 0.2) are selected. This method is efficient and leverages the model's learning process for feature selection. However, it may not eliminate irrelevant features entirely and requires careful parameter tuning to identify the most relevant features accurately [30]. **GA (Genetic Algorithm)**: GA was described in Section 2. **IGPSO**: IGPSO was described in Section 2. **ChaoticMap**: ChaoticMap was described in Section 2. **VLPSO (Variable-length PSO)**: VLPSO was described in Section 2. The last two algorithms listed, ChaoticMap and Variable-length PSO, were implemented for the comparison experiments; however, given the large dataset sizes, neither could be run due to their time complexity. However, all other comparison algorithms were included.
## 3 Results and Discussion
### Sgs Cases
#### 3.2.3 SGS Cases: Acoustics  
The effect of SGS severity on voice is first quantified by calculating the Fourier transform of the acoustic pressure, using the same procedure introduced for the baseline case at the probe 5 cm outside of the mouth, plotted in Figure 10a. This plot shows that the first two peaks remain consistent in frequency for all cases. For the 50% and 75% severities, the behavior of the spectrum and the peaks is similar to the baseline case. However, for 90% and 96% severities, the third and fifth peaks observed in the baseline case are significantly reduced or absent, and the peaks are redistributed. The suppression of these spectral components in severe SGS cases can be attributed to the increased viscous losses in the narrowed region. These acoustic changes align with our earlier POD analysis results. For the 90% and 96% SGS severities, we observed changes in the energy distribution of the first and second POD modes, indicating a shift in the dominant vibration patterns. Furthermore, for the 96% severity case, we noted slight changes in the mode shapes themselves. These findings suggest the idea that severe stenosis alters the fundamental vibration characteristics of the vocal tract, which in turn affects the acoustic output. These combined observations suggest that, similar to the glottal flow results, the SGS severity effect on acoustics is not noticeable up to 75% severity and becomes significant beyond that threshold. To further investigate this, we calculated the sound pressure level and vocal efficiency for different cases (Figure 10b). The results show that for higher SGS severities, sound pressure level decreases by 0.4 and 1.5 dB for 90% and 96% severity cases, respectively. Moreover, using \(\frac{T_{p}Baseline-\eta_{SGS}}{T_{p}Baseline}\times 100\), we found a decrease in vocal efficiency of 2.3% and 21.8% for 90% and 96% SGS severities, respectively.  
These results agree with our previous study's findings [14], where similar changes in these parameters were observed for SGS severities of 90% and above. The more pronounced reduction in vocal efficiency, compared to sound pressure level, for severe SGS cases is likely due to the combined effects of altered aerodynamics and the acoustic properties of the vocal tract.
## 3 Methods and Data
### Field Data
#### 3.1.2 Positioning (GNSS)
At that rate, it is impossible for the GNSS to process differential corrections in real time, so we opted to post-process the data and calculate the baselines using correction data from the VRS network. This process gives us positional data at a precision of \(\pm\)0.05 m or better in XYZ. Measurements with lower estimated precision were discarded in post-processing. The 10-Hz VRS-GNSS positional data were subsequently merged with the 1-Hz flow measurements using the GPS timestamps included in the data from the RiverSurveyor's own GPS. The precision of the timestamp is 0.1 s, so the possible horizontal error introduced by this process is at most \(0.5\times\) boat speed, that is, half the distance between two subsequent flow measurements, which, given boat speeds of <1 \(\mathrm{ms}^{-1}\), amounts to at most \(\pm\)0.05 m. Table 1 gives an overview of the number of GNSS points, ADCP points for each measurement day at the meander bend AOI, as well as the number of points remaining after merging the two datasets.
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
## 3 Mf Signal Impairments
### Atmospheric and Ground Delay Factor (AGDF)  
MF signals propagate along the earth's surface as the ground-wave [18]. This is an essential characteristic, which allows the signal to travel over longer distances in comparison to the line-of-sight propagation limitation, which is typical for higher frequency signals. Above the ground, the atmospheric refractive index causes a propagation speed reduction which becomes visible as a delay of the signal in the time domain. Furthermore, at the earth's surface, the signal encounters different types of land and water segments which are characterized by different materials with specific electrical properties. Each of these segments has a specific ground conductivity and electric permittivity, which produce a damping of the signal's amplitude and a further change of propagation speed [19]. The overall delay experienced by the signal has been defined as AGDF [20].  
It is possible to predict the AGDF for an arbitrary propagation path composed by several sections of different electrical properties by using the ITU World Atlas of Ground Conductivities [21]. For each section, the attenuation and delay of the signal is computed with the method introduced by [PERSON] [22; 23] and [PERSON] [24]. Finally, the overall delay is obtained through the application of the Millington/Pressey method, as explained in [24; 25].  
As an example, Figure 2 shows the phase delay in radians with respect to the propagation in the vacuum for the signal transmitted by the radio beacon located in Gross Mohrdorf, Germany, over the southern Baltic Sea. It is caused by the refractive index of the atmosphere, here assumed to be constant, and the variation of the ground conductivity, as given in the ITU maps.  
Measurements in specific areas can be used to apply an enhanced correction technique on these AGDF maps to improve the R-Mode performance [20].
## 4 Results and Discussion
### Displacement Response of Immersed Tunnel
#### 4.2.3 Global Deformation of Immersed Segments  
Seismic action may cause global bending or torsion of immersed tunnel, resulting in global tensile deformation and shear deformation of segments, thereby affecting the safety of the tunnel. The central axis node in the three segments of the immersed tunnel was selected to draw the vertical and horizontal displacement curves, as shown in Figure 17. It can be seen that the displacements of segment S1 and S3 are symmetrically distributed. When the incident angle is \(30^{\circ}\), the vertical displacements at two ends of S1 are \(-1.68\) cm and \(-2.04\) cm, respectively, which completely correspond to the vertical displacements at both ends of S3. When the incident angle of the SV wave increases, the vertical and horizontal global displacements of the immersed segments increase accordingly. The maximum global displacement in the horizontal direction can reach 3.5 cm (incident angle of \(30^{\circ}\)), which is \(20\%\) higher than the vertical direction, while the maximum vertical global displacement is five times that in the vertical incidence, with the value of \(-2.04\) cm. The global displacement in the vertical direction increases rapidly due to the increase in vertical component caused by the oblique incidence of the SV wave. The vertical and horizontal displacement difference between the two ends of S1 and S3 increase with the increased incident angle, causing the immersed tube to bend vertically and horizontally as a whole. The global bending of the tunnel increases the longitudinal opening on one side of the segment joint, resulting in axial tensile deformation. However, the longitudinal opening of joints needs to be within the deformation range; thus, the joint limit device should be considered in seismic design.