esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Instrumental Set-Up  
GC-MS analysis was performed using a similar instrumental system and method as already reported [17,19], except, in this case, we used the splitless injection mode at 250 \({}^{\circ}\)C. The injection volume and speed were set at 2 mL and 100 mL min\({}^{-1}\), respectively. The oven temperature was programmed from 60 \({}^{\circ}\)C to 180 \({}^{\circ}\)C at 30 \({}^{\circ}\)C min\({}^{-1}\) and to 300 \({}^{\circ}\)C (held for 1 min) at 10 \({}^{\circ}\)C min\({}^{-1}\) for a total running time of 18.0 min (solvent delay of 7 min). Three qualifier ions were chosen (Table 1), one of which was selected for quantification purposes, according to the characteristic features of the mass spectra obtained in the full-scan mode by comparison with the Wiley's library spectral data bank (G1035B; Rev D.02.00; Agilent Technologies, Waldbronn, Germany). The recovery data of all the performed assays were calculated through the comparison of the average peak areas of the extracted analytes with standard controls.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SCs** & **Chemical Structure** & **SIM ions** & **log** & **RV\({}_{\text{a}}\)** & **RT** & **Memory** & **RSD** \\  & **(m/z)** & **K\({}_{\text{O}\text{W}}\)**1 ** & **3.36** & **7.49** & **8.9** & **43.1** \(\pm\) **13.2** \\ \hline \hline \(\alpha\)-PVP & & 84, 111, 126 * & 3.28 & 7.59 & 9.2 & 63.7 \(\pm\) 10.8 \\ \hline \hline MDPV & & 121, 126 * & 2.99 & 7.31 & 12.2 & 52.3 \(\pm\) 13.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structures, log \(K_{\text{O}\text{/}\text{W}}\), pK\({}_{\text{a}}\), retention times (RT), average recovery yields, and the precision (expressed in RSD) obtained for the three SCs by BA\(\mu\)E(Strata-X)-\(\mu\)LD/GC-MS(SIM) in oral fluid samples under optimized experimental conditions.
## 5 Conclusions  
1. The influence of the acoustic dispersion on the reduced error of AE source location has been considered. AE monitoring was performed using a thresholding approach. The location error calculated at a distance of less than 100 mm from the AE transducer reached 15% in respect to the whole investigated area;
2. Finite element modeling of AE signals allowed to estimate with high accuracy the positioning error of the linear location technique applied to determine the AE source coordinates;
3. The experimentally obtained data corresponded to the simulation results (see Figure 11). It implies that the developed FE model of acoustic wave propagation in a steel plate can be implemented to determine the AE source location with the required level of accuracy. Moreover, only a few preliminary tests should be carried out for a model construction;
4. The optimization method for assessing the reduced error of the 1D linear location technique of AE sources consists of several stages. At the initial stage, it is necessary to generate acoustic signals of various amplitudes using an electronic simulator. Then it is alternately installed at a distance of 0.1 \(B\) and 0.3 \(B\) (\(B\)--distance between two AETs) relative to one of the receiving transducers. Based on the results of the two experiments, there is a need to evaluate the spectral characteristics of the recorded AE signals, as well as to construct FE models of receiving AEs. Finally, a comparison between the error obtained by simulation and the results of preliminary tests should be done.
5. The obtained results can be effectively used in the framework of acoustic emission monitoring of isotropic metal structures. There are many more potential applications offering benefits to the diagnostics adopting this approach. Especially it concerns maintenance inspections of extended objects with complex geometry.  
Conceptualization, A.M, I.V. and D.C.; methodology, I.V.; experiment, D.C., I.M.; validation, A.M.; formal analysis, I.V.; investigation, D.C.; data curation, E.K. and D.M.; writing--original draft preparation, D.C., A.M.; writing--review and editing, D.C., D.Z.; visualization, D.Z.; supervision, A.M., I.V.; project administration, D.Z.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
The reported study was funded by the Russian Science Foundation (project No. 21-79-00232).  
Not applicable.  
This research was carried out in in National Research University \"Moscow Power Engineering Institute\". The experimental results were obtained using the equipment provided by the center \"Science-intensive technologies for creating machines of the future\" (IMASH RAS).  
The authors declare no conflict of interest.
## 3 Proposed C-HARQ Protocol Design
### Scheme Description
As usual, a Viterbi decoding is carried out using all the packets saved in the buffer including the new one. A successful recovery results in termination of the cooperation by sending an _ACK_ to the source, otherwise, the attempts of error correction continues for other FEC packets and with other available-_CNodes_. The second case, where all the FEC packets have been correctly saved (_i.e._, _status_\(=1\) for all FEC packets), implies that no more cooperation is required. Thus to terminate the current cooperation process, and to ask the source for a new copy of _Pac_-0 (so as to replace the _saved_Pac_-0), a _NACK_ is sent to the source. Upon receiving the _NACK_, the source comes out of the idle mode and transmits a fresh copy of _Pac_-0, which is then utilized by the destination in the following error correction attempt. If the error correction fails again, a _NACK_ is sent to the source again. Here, the destination requests for no further cooperation as all the FEC packets are already available. Now, the destination and the source continue to work in this S&W ARQ manner, with the error correction information available at the destination, until the data is successfully recovered. In another special case where the last available-_CNode_ has been contacted but some of the saved FEC packets still require retransmission, a _NACK_ is sent to the source, so as to eventually start the next cooperation process for acquiring the remaining FEC packets. On hearing the _NACK_, source simply retransmits _Pac_-0, with which the destination first attempts Viterbi decoding utilizing the previously saved FEC packets; if the data is recovered successfully an _ACK_ is sent, otherwise the next cooperation process is initialized from the stage of _C_\({}_{Req}\) transmission so as to obtain the remaining FEC packets.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 4 Solution Approach
### Using E-Optimality Technique  
As noted by [PERSON] and [PERSON] [25] and [PERSON] [53] in the BD, it is not needed for the MP and SP to be solved to optimality, but finding a relatively good solution is also fine, and even the convergence of the algorithm is proven in this case. This is also discussed in early iterations for the algorithm when there is not much information about the solution, and solving the MP and SP to optimality is not only a time-consuming task but would return not much information. Based on this, we define a variable for the optimality gap of MP and SP, which can be tuned. We also consider periodically solving MP and SP to optimality, not to fall far from the final solution. Our numerical results confirm the efficiency of applying such a technique to speed up the BD algorithm. The following is a pseudo-code of the Algorithm 2:  
```
Determine n1 and n2, to be the periodic number to optimally solve SP and MP, determine the initial solution gap for SP and MP as G1 and G2, for (i = 1:m) do  if \(div\Big{(}\frac{i}{n_{1}}\Big{)}=0\) \(G1_{i}\gets 0\);  Run MP;  else \(G1_{i}\leftarrow\frac{G1}{i}\) Run MP;  end if if \(div\Big{(}\frac{i}{n_{2}}\Big{)}=0\) \(G2_{i}\gets 0\);  Run SP; else \(G2_{i}\leftarrow\frac{G2}{i}\) Run SP;  end if end for
```  
**Algorithm 2** E-Optimality Technique
# A Numerical Study of the Vibration Characteristics of a Haptic Actuator for a Dial Gear Shifter
## 2 Materials and Methods  
### Structure of the Haptic Actuator  
The haptic actuator (Figure 1) comprised a housing structure, an electromagnetic system, a vibration system, and a damper. The vibration system consisted of a spring and a weight to convert the electromagnetic force derived from the electromagnetic system into kinetic energy. Specifically, the application of an electric current to the coil creates a magnetic field, making the yoke polar and causing the magnet to oscillate. Finally, the housing structure of the actuator was a metallic case, and a bracket protected the internal parts on the bottom side of the actuator.
## 5 Conclusions  
This paper presents DFFormer, a novel Transformer-based framework designed to enhance control parameter prediction for underwater gliders (UGs) by leveraging advanced signal processing and neural network techniques. DFFormer utilizes discrete wavelet transform (DWT) and fast Fourier transform-based attention (FFT-Attention) to effectively analyze the frequency- and time-domain features critical for UG operation. Its Transformer-based architecture processes signals through multiple parallel pathways, significantly improving its ability to manage complex control parameters, particularly in precisely adjusting rudder angles. Extensive testing with actual sea trial data demonstrates that DFFormer outperforms traditional predictive methods in both accuracy and computational efficiency. Furthermore, the integration of recursive layers, such as multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), enhances the model's feature extraction capabilities, enabling the effective capture of detailed inference features. The inclusion of a fusion block for integrating these extracted features further improves the robustness and reliability of DFFormer's predictions. The use of the Huber loss function refines the neural network structure, providing robustness against outliers and increasing precision. Overall, this research advances UG control parameter prediction, offering valuable insights into the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Recurrent Layer Type** & **Training Time (s)** &
\begin{tabular}{c} **Inference Time per** \\ **Prediction (ms)** \\ \end{tabular} & **Memory Usage (MB)** \\ \hline MLP & 1325 & 9.8 & 2340 \\ RNN & 1563 & 12.1 & 2475 \\ GRU & 1489 & 10.7 & 2395 \\ LSTM & 1678 & 13.4 & 2585 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational resource comparison for DFFormer with different recurrent layers.  
application of machine learning in autonomous systems. DFFormer improves course-keeping and control efficiency, laying a strong foundation for future developments in UG control technology.  
Conceptualization, Y.J. and J.A.; methodology, Y.J. and C.M.; software, W.G.; validation, W.G., Z.L. and X.S.; formal analysis, J.A. and Y.J.; investigation, W.G.; resources, J.A. and C.M.; data curation, Z.L.; writing--original draft preparation, Y.J.; writing--review and editing, W.G. and C.M.; visualization, [PERSON]; supervision, [PERSON] and C.M.; project administration, [PERSON]; funding acquisition, [PERSON] and C.M. All authors have read and agreed to the published version of the manuscript.  
This work is jointly supported by the National Key R&D Program of China, the National Natural Science Foundation of China under grant nos. 62373278 and 52005365, and the Natural Science Foundation of Tianjin, China, under grant no. 21 JCJQJC00130.  
Not applicable.  
The data are available from the corresponding authors upon reasonable request.  
The authors would like to express their sincere thanks to [PERSON] for providing valuable support with the sea trial data.  
The authors declare no conflicts of interest.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 6 Conclusions and Future Work  
In this study, we propose a novel pre-trained model targeted specifically for sensor-based HAR problems. To perform the model selection, nine popular CNN architectures were tested using eight CWT-generated scalogram sets. As a result, 60 possible combinations and over 300 models were trained and evaluated. We performed the analysis of the impact of CWT parameters, such as mother wavelet and scale values, on the performance of network architectures.  
It was determined that the model with the DenseNet121 architecture, Morlet mother wavelet, and scale values ranging from 0 to 256 produced the best results on the target KU-HAR dataset. The classification accuracy and F1 score of the selected model reached 97.48% and 97.52%, respectively, outperforming the majority of state-of-the-art works employing this dataset.  
The selected model was tested using the UCI-HAPT dataset and its subset to determine its performance on target datasets of varying sizes and with substantial differences from the source dataset. Usage of the proposed model resulted in a maximal accuracy and F1 score increase of 0.21% and 0.33%, respectively, on the entire UCI-HAPT dataset and 2.82% and 2.89%, respectively, on the subset compared to the non-pre-trained models.  
It was concluded that the usage of the proposed model, especially with layer freezing, results in a more stable gradient descent, faster training, and improved performance on small datasets. However, the usage of the pre-trained model with datasets of medium and large sizes may result in an accuracy decrease and, therefore, a negative transfer.  
In the upcoming studies, we intend to design and analyze heterogeneous pre-trained models using the gated recurrent unit (GRU) or long short-term memory (LSTM) layers. Additionally, the construction of models with the separated handling of static activities is promising, which may significantly increase the performance on sensor-based HAR data.  
Conceptualization, O.P.; methodology, M.M.; software, M.M.; validation, C.S.; formal analysis, M.M.; investigation, C.S.; resources, M.M.; data curation, O.P.; writing--original draft preparation, M.M.; writing--review and editing, O.P.; visualization, M.M.; supervision, O.P.; project administration, C.S.; funding acquisition, O.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Polish-Ukrainian grant \"Automated Guided Vehicles integrated with Collaborative Robots--energy consumption models for logistics tasks planning\", grant number 02/110/ZZB22/1022.  
All used datasets are publicly available and can be accessed using the provided references.  
Open Access Funding by the University of Vienna.  
The authors declare no conflict of interest.
## 5 Field Application and Effect Evaluation
### Analysis of Mine Pressure Appearance  
#### 5.2.1 Force Analysis of Anchor  
The monitoring of the working resistance began when the working face was 200 m away from the measurement point. The monitoring was terminated when the working face approached the measurement point. Figure 17 displays the variation curves of the working resistance of the bolt-anchor cable at measurement points 1# to 8#.  
Figure 16: Layout of displacement measuring points.  
Figure 17: Variation curve of working resistance of bolt-anchor cable.  
As the working face approaches the measuring point to about 30 m, the supporting resistance of the bolt and cable increases significantly, and the working resistance increases to a certain value, after which it reaches the maximum. The roof rock is well supported during mining, and the surrounding rock's two ribs are anchored, ensuring the stability of the roadway.
# MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture
## MLUTNet  
### Network Definition  
Assuming an end-to-end approach to train directly on full precision neural networks, i.e., using the input-target data pairs of data as training data and then mapping the trained neural networks to MRLD, we will face many potential problems. (1) The hidden layers of standard neural networks are usually fully connected, the size of individual weight matrix is large, and the number of connections grows exponentially with the size of the weight matrix. From the SRAM storage point of view, it is unrealistic to achieve a reasonable mapping on MRLD without limiting the size of the weight matrix and the number of connections. (2) The parameter storage and arithmetic processes inside the standard neural network are executed at full precision by default. However, most programmable logic devices support limited precision, and the operational expenses increase dramatically when the precision is higher. Therefore, it is important to use a neural network model with limited precision to find a balance between accuracy and cost. (3) The non-linear activation functions commonly used in neural networks, such as _Sigmoid_ and _tanh_, usually require proprietary resources for implementation on programmable logic devices and can only be approximated with limited accuracy. Although the expense of a single operation is not significant, considering that each parameter of the hidden layer weights is involved in the activation operation, a more sensible measure is to switch to other activation functions that are more friendly to hardware mapping.  
To address the above issues, we use the following measures to improve them. For (1), we import the methods of splitting the hidden layer and neighbor-only connections. Since MRLD devices are composed of multiple MLUTs as basic cells, each MLUT can be regarded as a small SRAM array with data storage or logic wiring or both. We split the originally larger hidden layers according to the MRLD topology, perform the computation and parameter update separately, and merge them into the final result after the computation is completed. For the connections between the split weight matrices, we utilize neighbor connections to reduce the number of connections and also play a role in preventing over-fitting. For (2), we import the idea of low precision neural network, BNN, as the comparative alternative to the full-precision standard neural network. BNN uses a 1-bit representation for the weights and XNOR-bit operation instead of the usual multiplication operation. Compared with full-precision multiplication, both the software training cost and the hardware computation cost can be effectively reduced. For (3), we use the _ReLU_ function [12] and _Htanh_ functions as activation functions instead of the traditional _Sigmoid_ and _tanh_. Benefiting from their simple numerical logic, both can be implemented by linear combinations of logic gates.  
\[\text{ReLU}(x)=\max(0,x) \tag{3}\] \[\text{Htanh}(x)=\max(-1,\min(1,x)) \tag{4}\]
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 4 Implemented Statistical and AI Methods  
In the current research, Rolling Average is used for raw data time series smoothing and Polynomial Fitting is employed to model a polynomial that accurately represents the dataset obtained from the testbed during disturbance scenario testing.  
Polynomial fitting is a regression technique that fits a polynomial (a mathematical expression consisting of variables raised to powers and multiplied by coefficients) to a set of data points. The goal is to find the polynomial that best captures the relationship between the independent variable(s) and the dependent variable. The degree of the polynomial determines its complexity, and higher-degree polynomials can capture more complex relationships but may also be prone to overfitting.  
For Polynomial Curve Fitting, Least Squares Regression and Support Vector Regression (SVR) with a Radial Basis Function (RBF) Kernel model are used. Support Vector Regression (SVR) is a regression algorithm that uses support vector machines to model the relationship between variables. In SVR, the goal is to find a hyperplane that best fits the data while allowing for a certain degree of error. The RBF kernel is a specific type of kernel function used in SVR, and it is particularly effective in capturing non-linear relationships. In the following section, the theoretical foundation of the methods is explained.
###### Abstract  
This study aimed to evaluate how wearable hip exoskeleton assistance affects phase-dependent gait stability in healthy adults using a novel visualization technique known as gait tube analysis. Hip exoskeletons offer significant potential to enhance human locomotion through joint torque augmentation, yet their effects on gait stability across the gait cycle remain underexplored. This study introduces gait tube analysis, a novel method for visualizing center of mass velocity trajectories in three-dimensional state space, to quantify phase-dependent gait stability under hip exoskeleton assistance. We analyzed data from ten healthy adults walking under twelve conditions (ten powered with varying torque magnitude and timing, one passive, and one unassisted), assessing variability via covariance-based ellipsoid volumes. Powered conditions, notably HighLater and HighLatest, significantly increased vertical variability (VT) during early-to-mid stance (10-50% of the gait cycle), with HighLater showing the highest mean ellipsoid volume (99,937 mm\({}^{3}\)/s\({}^{3}\); z = 2.3). Conversely, the passive PowerOff condition exhibited the lowest variability (47,285 mm\({}^{3}\)/s\({}^{3}\); z = -1.7) but higher metabolic cost, highlighting a stability-efficiency trade-off. VT was elevated in 11 of 12 conditions (\(p\leq 0.0059\)), and strong correlations (r \(\geq\) 0.65) between ellipsoid volume and total variability validated the method's robustness. These findings reveal phase-specific stability challenges and metabolic cost variations induced by exoskeleton assistance, providing a foundation for designing adaptive controllers to balance stability and efficiency in rehabilitation and performance enhancement contexts.  
gait stability; hip exoskeleton; gait tube analysis; center of mass variability; phase-dependent variability; wearable robotics; metabolic cost
## 2 Characteristics of Satellite Signals and Framework of GNSS Receivers  
### Characteristics of Satellite Signals  
In this paper, we take the GPS L2C signal as an example of GNSS signals. GPS L2C signal is the civil signal, newly added on the existing L2 frequency band and modulated by civil-moderate (CM) and civil-long (CL) codes. The mechanism of the GPS L2C signal is described as follows: Firstly, the navigation message is encoded by forward error correction (FEC) coder and modulated on CM code to comprise the data channel. Meanwhile, without navigation message, CL code acts as the pilot channel by direct sequence spread spectrum (DSSS) method. After that, signals of data and pilot channels are separately added on the baseband signal by time division multiplexing (TDM) method, and further modulated by binary phase shift keying (BPSK) method on carrier wave. Finally, the generated signal is transmitted by satellites. GPS L2C signal received from satellites can be modeled as [4; 22]:  
\[S_{L2}(t)=A[D(t-\theta)C_{M}(t-\theta+kT_{M})+C_{L}(t-\theta)]cos[2\pi(f_{IF}+f _{D})t+\varphi]+n(t) \tag{1}\]  
where \(A\) is the signal amplitude and \(D(t)\) is the navigation message. \(C_{M}(t)\) and \(C_{L}(t)\) represent CM and CL codes, respectively. \(\theta\) is code phase in the received signal. \(T_{M}\) denotes the period of CM code and \(k\) indicates the number of CM epochs in the current CL epoch, \(0\leq k\leq 74\). The carrier frequency of the received signal consists of the intermediate frequency \(f_{IF}\) and Doppler frequency shift \(f_{D}\), which results from the relative motion between the satellite and the receiver. \(\varphi\) refers to the initial carrier phase. Besides, \(n(t)\) is the thermal noise in the received signal, denoted as the additive white Gaussian noise. It is noticeable that the signal is just composed of the cosine term on account of the BPSK method.