esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Visually guided weight shifting is widely employed in balance rehabilitation, but the underlying visuo-motor integration process leading to balance improvement is still unclear. In this study, we investigated the role of center of pressure (CoP) feedback on the entrainment of active voluntary sway to a moving visual target and on sway's dynamic stability as a function of target predictability. Fifteen young and healthy adult volunteers (height \(175\pm 7\) cm, body mass \(69\pm 12\) kg, age \(32\pm 5\) years) tracked a vertically moving visual target by shifting their body weight antero-posteriorily under two target motion and feedback conditions, namely, predictable and less predictable target motion, with or without visual CoP feedback. Results revealed lower coherence, less gain, and longer phase lag when tracking the less predictable compared to the predictable target motion. Feedback did not affect CoP-target coherence, but feedback removal resulted in greater target overshooting and a shorter phase lag when tracking the less predictable target. These adaptations did not affect the dynamic stability of voluntary sway. It was concluded that CoP feedback improves spatial perception at the cost of time delays, particularly when tracking a less predictable moving target.  
Keywords:posture; balance; weight shifting; target predictability; coherence; dynamic stability +
Footnote †: journal: Applied Sciences
###### Abstract  
Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model's performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively.  
intrusion detection systems; synthetic minority oversampling technique; hierarchical clustering algorithm; fire hawks
## 3 Three-Dimensional Finite Element Simulation  
### Model Establishment  
In this paper, the above theory is verified by finite element analysis software ABAQUS (ABAQUS 6.14.2, DASSAULT SIMULIA, Providence, Rhode Island, USA), and a three-dimensional model 0.25 m long, 0.1 m wide and 0.002 m thick is established. As shown in Figure 8a,b. The Young's modulus is set to \(6.89\times 10^{10}\) N/m\({}^{2}\), the Poisson's ratio is set to 0.330, and the density is set to 2704 kg/m\({}^{3}\). The simulation time is 200 \(\upmu\)s. The crack length is set to 0.014 m. Taking the microcrack center point as the coordinate origin, the microcrack extends along the y-axis direction. The contact method adopts self-contact, the constraint algorithm adopts the penalty contact method, and the contact attribute defaults tangential behavior and legal behavior. The model diagram is as follows:
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 7 Experimental Results
### Box-Counting Test  
We took the path of the robot generated with the initial conditions \(x_{0}=1.1\), \(y_{0}=-0.9\), \(\theta_{0}=1.8^{\circ}\) for this test. Table 4 shows the measured values of \(N\), \(l\), \(\log N(l)\), \(\log 1/1\), and \(D\) for this case.  
When plotting \(\log N(l)\) against \(\log 1/1\) and performing the linear regression to the data shown, we obtained Figure 20. We can see behavior very similar to the line given by the equation \(y=117837x-0.0462\).  
We note that the slope of this line (1.7762) is very similar to the obtained values of \(D\). This result verifies that the trajectory has a fractional dimension and, therefore, is chaotic. Among the conditions that affect the experimental results are the intrinsic physical limitations of the Khepera III robot, as well as some characteri  
\begin{table}
\begin{tabular}{c c c c c} \hline \(N(l)\) & \(l\) & \(\log N(l)\) & \(\log 1/l\) & \(D\) \\ \hline
59 & 0.0909 & 4.0775 & 2.3978 & 1.7004 \\
108 & 0.0714 & 4.6821 & 2.6391 & 1.7742 \\
225 & 0.0476 & 5.4161 & 3.0445 & 1.7789 \\
329 & 0.0384 & 5.7960 & 3.2580 & 1.7789 \\
447 & 0.0322 & 6.1025 & 3.4339 & 1.7770 \\
570 & 0.0277 & 6.3456 & 3.5835 & 1.7707 \\
709 & 0.0256 & 6.5638 & 3.7135 & 1.7675 \\
848 & 0.0217 & 6.7428 & 3.8286 & 1.7611 \\
995 & 0.0196 & 6.9027 & 3.9318 & 1.7556 \\  & & & \(D_{Mean}\) & 1.7750 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental box-counting results for \(n\) robots simultaneously in the same work area.  
Figure 19: Experimental trajectories time series: (**a**) \(x\); (**b**) \(y\).
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 2 Related Work
Here, the intermediate nodes notify the destination node of the route whenever the quality of the link to their downstream nodes degrades. The destination node then sends a new route reply packet (RREP) (containing the updated route stability value) to the source node along each cached route. The source node then switches to an alternate route, which avoids the high overhead and delay of new route discovery. However, this scheme cannot adjust to changes in traffic and congestion and also lacks a route repair mechanism. A hybrid routing protocol [17] discovers routes to the BS on demand (like AODV); here, the source node maintains knowledge of alternate routes and their quality within a two-hop region around the selected route (called a pipe) to perform proactive route switching when the quality of the current route degrades. It selects the best route based on various factors such as hop count, interference, route lifetime, and estimated latency to provide long-lasting and less congested routes. _Topology Control for Robust Routes:_ Some schemes (e.g., [7; 28; 29; 30; 31]) modify the mobility models to provide stable network connectivity. In this section, we review topology control and routing schemes that maintain a stable route between the source and destination UAVs. In [32], the AODV routing protocol [16] and a modified Boids mobility model [33] are used to maintain the links along the route in a highly dynamic autonomous UAV network. In [34], a global joint 3D trajectory and power allocation scheme was designed using spectral graph theory and convex optimization techniques to form interference-aware routes between a single source and destination (BS) using rotary wing UAVs. In [35], a virtual springs-based mobility control system for FANETs maintains an aerial mesh network to ensure ground user coverage and QoS for disaster recovery. In [36], a virtual force-based UAV mobility model and energy-efficient mobility-aware cluster head (CH) UAV selection are used to improve ground-user coverage and network connectivity. The authors also propose a topology-aware routing protocol that uses Q-learning to find stable, low-delay, and energy-efficient routes between CH UAVs and BS. Our proposed routing protocol for autonomous UAV networks combines the advantages of proactive and reactive schemes, along with topology control, to establish robust and low-overhead routes that are aware of mobility, congestion, and energy. This protocol is based on our connectivity-aware pheromone mobility model that balances maintaining connectivity with providing coverage [7].
# Experimental Validation of Non-Marker Simple Image Displacement Measurements for Railway Bridges
## 3 Processing and Test Methods
### Model Experiment Method  
Table 1 and Figure 3 show the specifications and view of the model bridge [9] used for the measurement confirmation in this study. A steel bridge with a total length of \(\sim\)3 m is supported by four bearings with a span length of 2.8 m. Horizontal and rotating mechanisms were introduced to ensure the bearings achieved simple support conditions. The main girder is H-steel with a height and width of 100 mm, the deck is a steel plate with a width of 400 mm and a thickness of 9 mm, and the noise barriers are Al plates with a height of 165 mm and a thickness of 1.5 mm. The goal of this experiment was to confirm the basic performance and measurement accuracy, and to evaluate the marginal performance of the measurement system, such that the amount of deflection at a shooting distance of 2.5 m was set at \(\sim\)0.01-0.1 pixels. The noise barrier was removed, and only two H-beams and the deck were used for the experiments. The frequency of the girder primary mode in this configuration is 37 Hz in the calculated value and \(\sim\)35 Hz in the actual measurement.  
Table 2 shows the measuring equipment specifications, and Figure 4 shows the measuring equipment layout. To measure the deflection shape of the model bridge girder, seven contact displacement gauges were installed at equal intervals (L/8, 2L/8, 3L/8, 4L/8, 5L/8, 6L/8, and 7L/8). These measurement points are called Ch0-Ch6, from the left-hand side of the figure. The video camera was installed 2.5 m from the model bridge, and the displacement was measured at the same location as the displacement meter measurement point. LEDs were used for lighting. The effect of the brightness of the shooting environment on the measurement accuracy, determined by turning the LED light on and off, was examined. A distance of one pixel above and below in the image taken under the measurement conditions corresponds to 1.45 mm above and below the model bridge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Values** \\ \hline Bridge length & 3048 mm \\ Bridge span & 2800 mm \\ Mass (without noise barrier) & 230 kg/197 kg \\ Girder & 2 \(\times\) H-100 \\ Deck & 3048 \(\times\) 400 \(\times\) 9 mm \\ Calculated frequency & 37 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of model bridge.  
Figure 3: Picture of the model bridge.  
The excitation was performed using a shaking machine, and the midspan and the L/4 point of the girder, which was the antinode of the secondary mode, were vibrated. The vibration frequency was set to 35 Hz at the midspan and 120 Hz at the L/4 point. The excitation frequencies corresponded to the resonance frequencies in the primary and secondary deflection modes. The displacement amplitudes were about \(\pm\)30 \(\upmu\)m for the midspan excitation, and about \(\pm\)4 \(\upmu\)m for the L/4-point excitation, which were about \(\pm\)0.02 pixels and \(\pm\)0.003 pixels, respectively. These amounts were set to be close to the limit of displacement measurement accuracy by the DIC.
# Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction
## 2 Data Preparation and Pre-Processing
### Data Pre-Processing  
Input feature vectors have different magnitudes, among which large feature vectors can dominate the training, causing the network to fail to learn from other feature vectors. Thus, the inputs need to be normalized. The normalization of the inputs can facilitate gradient descent and accelerate convergence [24,25]. In this work, min-max normalization is applied to the input feature vectors to map them between 0 and 1.  
The values of currents mainly range from 1 \(\times\) 10\({}^{-12}\) A to 1 \(\times\) 10\({}^{-3}\) A with a large span of magnitude. If the NN is trained using unprocessed current, the error is dominated by the large current range, and the errors in the small current range are not weighted enough, which will lead to a poor fit for small values. We applied a logarithmic operation to the training data to solve this problem, and errors in all regions are weighted equally. However, when \(V_{ds}\) is 0, \(I_{ds}\) is zero current, which cannot take logarithms, so the points with \(V_{ds}\) of 0 were removed from the training set and test set. At the same time, the voltage settings in the data preparation were adjusted, and the output characteristic curves were swept from 0.05 V to 1.0 V for \(V_{ds}\). In the following work, the \(I_{ds}\) at \(V_{ds}\) of 0 was specified as 0 for the convenience of discussion.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 3 Methodology
### Environmental Contextual Signals  
With their built-in sensors, smartphones have proven to be an indispensable component of a lot of HAR research and applications [41, 61, 62, 63, 66]. Interactions between humans and the environment, therefore, can be easily captured using environmental sensors embedded in devices, such as smartphones present in their physical locations. These sensors are good at detecting multi-occupant activities [2]. Examples of environmental sensors include sound, light, humidity, pressure sensors, etc. In this work, the approach is to augment traditional inertial sensing data with rich contextual data captured by environmental sensors to train the classifier to distinguish between simple and complex activities. Several other works used a similar approach to achieve state-of-the-art results [29, 30]. However, their work did not combine the audio and light sensors as proposed in this article. In addition to the inertial signals, we used sensory signals from the audio and light sensors to train and improve the baseline model to create a context-aware model.  
The sound or audio sensors are usually made up of microphones and speakers. The microphone receives ultrasound signals, while the speaker transmits the same. The idea is  
Figure 3: Overview of the proposed context-aware smartphone-based human activity recognition using hybrid CNN–LSTM model.  
that human movement activities and interactions create different levels of environmental sounds, and these can be captured by the microphone as noise level contexts. Likewise, the light sensor captures the intensity of light, i.e., the illumination level at the time these interactions occur. Together, these two sensors provide rich contextual data that can be fed to the CNN-LSTM models.  
Having discussed the sensors used, Figure S1 illustrates how data from these multiple sensors were fed into the hybrid model through the CNN model, which extracts the feature representation of the signals. In Figure S1, the representations of single axis light and microphone signals are depicted as \(i\) and \(n\), respectively, whereas the representations of signals from the triaxial accelerometer, gyroscope, and magnetometer are denoted by \(x_{i}^{j}\), \(y_{i}^{j}\), and \(z_{i}^{j}\) serving as input to the context aware HAR model.
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 4 Brief Introduction of the WMT20 QE Sub-Tasks  
### Sub-Task 1  
Sub-task 1 is a sentence-level direct assessment task. This task consists of scoring MT output according to a perceived quality score called direct assessment. A limitation of human translation error rate (HTER) [41] is that it does not capture the extent to which MT errors affect the overall quality of a sentence. The objective of sub-task 1 is to measure the overall quality of sentences through direct assessment (DA) by translation experts. One of the goals of QE in relation to this task is to investigate the relationship between a model for predicting DA scores and a model trained to predict post-editing tasks [15]. The DA score is a value obtained by evaluating the quality of the MT output from 0 to 100 by at least three professional translators. Using a total of 7K training data and 1K evaluation data, systems participating in this sub-task measure quality by predicting the mean z-standardized DA score of the MT output.
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 2 System Model
### The Proposed Coding Algorithm  
Inspired by the superposition codes concept [19], we developed a simple coding rule that allows a fast soft decoding algorithm which can be easily included in the reader receiver FM0 detector, keeping a significant coding gain. In the FM0 approach, the binary \"0\" bi-phase space has a transition in the middle of a symbol, whereas the binary \"1\" does not.  
We will focus on the code rate equal to 1/2, because the decoding performance will be compared with the output of a Miller 4 modulation scheme, decoded by a Viterbi algorithm [8; 18].  
Let \(\mathbf{x}=(x_{0},x_{2},...,x_{K-1})\) be the encoder input bits, while \(\mathbf{y}=(y_{0},y_{2},...,y_{N-1})\) represents the output, where \(K=\log_{2}(N)\), with \(K=2\), \(N=4\) in the presented results. The output codeword has all bits equal to zero, except the one whose position corresponds to the binary-to-decimal conversion of the input vector \(\mathbf{x}\).  
An example of the coding process is shown in Figure 1, and describes the transmission of the codeword \((0,1)\), which may be converted to the decimal number 2 by considering the first bit as the less-significant one. The output of the coder corresponds to the positional codeword, where the bit equal to 1 in the third position (i.e., \(y_{2}\)) represents the decimal number 2. Actually, any different univocal mapping can be used, by just considering the constraint to have only one bit different from zero in the output codeword in order to keep the same low decoding complexity.  
The transmission process ends by modulating the codeword bits of \(\mathbf{y}\) by using the reference signal waveforms \(S_{ref0}(t)\) and \(S_{ref1}(t)\) described in Figure 2, producing the transmitted continuous time signal \(y(t)\). The described coder could be simply realized, for instance, by employing a low complexity boolean circuit with two signal inputs and four outputs.  
Figure 1: Example of encoder output stream \(y\) given the \(x\) input sequence 01.  
Figure 2: Reference waveforms signals \(S_{ref0}(t)\) and \(S_{ref1}(t)\).
[37] assigned different weights to landslide susceptibility and SBAS-InSAR results, multiplying them to improve the credibility of susceptibility mapping. This study aims to address the mentioned issues by integrating extensive surface deformation monitoring into a machine learning model to rectify misclassified areas and refine landslide susceptibility mapping (LSM). The proposal involves combining synthetic-aperture radar interferometry (InSAR) with the categorical boosting (CatBoost) model to create a refined information value CatBoost integration (IVCI) susceptibility assessment model. The primary contributions and highlights include: (a) supplementing the quantity of disaster points by employing optical remote sensing imagery and traditional statistical models, thereby optimizing the selection of non-landslide points and enhancing the predictive accuracy of machine learning models; (b) introducing a novel model that combines machine learning with InSAR technology, validating its feasibility and accuracy; and (c) employing the SHAP method to interpret the model, providing intuitive insights into the importance and influence of individual features within the model.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 6 Conclusions  
In this article, different non-linear operations employing LDACS receiver architecture to mitigate the DME interference is proposed. With the knowledge of GAE and OBE, we propose simple yet improved pulse peak attenuators GAEPPA, OBEPTA, JGAEPPA and pulse peak limiters OBEPTA, OBEPTL, JOBEPPL. From the results obtained with the comparative study of these proposed methods along with the existing pulse blanking method, we observed the following:  
1. All these proposed methods exhibited improved performance compared to the pulse blanking method.
2. OBE enhanced methods always outperformed GAE enhanced methods.  
Figure 26: Performance comparison of proposed four types of pulse peak Type 2 limiters.  
Figure 25: Performance comparison of proposed four types of pulse peak Type 1 limiters.  
* 3. Pulse peak limiters showed better performance than pulse peak attenuators in most of the cases. JOBEPPL and JOBEPPA showed almost similar performance as an exception, which in turn indicates the accuracy in estimation of JOBEPPA.
* 4. Among all the proposed pulse peak attenuators, JOBE PPA had better received BER than JGAEPPA, OBEPPA and GAEPPA.
* 5. Similarly, among all proposed PPLs, joint OBEPPL exhibited better performance than joint GAEPPL, OBEPPL and GAEPPL.
* 6. JOBEPPL and JOBEPPA are the best nonlinear estimators within the proposed methods.  
The SNR saving of joint OBEPPA and joint OBEPPL at \(10^{-1}\) is around 3 dB. In the future, this work can be extended for en-route channel to address the issues discussed in [49] with suitable equalization techniques [50]. The performance of these methods in LDACS RL can be analyzed. Though these methods are investigated on the LDACS background, the methods are suitable in reducing impulse noise in any OFDM-based communication system.  
Conceptualization, M.K. and R.M.; methodology, M.K. and R.M.; software, M.K.; validation, A.R.and L.K.M.; formal analysis, M.K. and L.K.M.; writing--original draft preparation, writing--review and editing, M.K.; supervision, R.M. and A.R.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Exclude this statement.  
The authors declare no conflict of interest.