esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow-Induced Fatigue Damage of Large Francis Turbines Under Multiple Operating Loads
## 5 Investigation Limitations and Future Research  
For Francis units that have been put into service, after a number of years of operation, especially under partial loads, the studied turbines will gradually age, their performance will deteriorate, and runner stresses may increase. This cannot be analyzed with the current approach. Pressure sensors and vibration sensors are needed to monitor the dynamic behavior of the runners in real-time, and acoustic sensors are needed to detect the signals of early crack formation and crack expansion in the runner blades.  
Figure 12: Stress of the runner.  
The next step requires increased periodic inspection, overhaul and maintenance, and regular non-destructive testing of runners, including ultrasound, magnetic particle, or dye penetration, to detect cracks and material failures. Cracked blades should be repaired or replaced promptly, and the integrity of the runner should be restored by welding or replacement of the blades.  
For long-term considerations, it is recommended to design a new type of Francis runner to measure the pressure distribution and pressure pulsation in the flow field of the hydraulic turbine using numerical calculations and model test simulations and to optimize the blade design to reduce the flow inhomogeneity and the degree of pressure pulsation, as well as to further reduce the stress concentration on the runner blades.  
For runner structural design, it is necessary to ensure that the natural frequencies of the runner are far away from possible hydraulic excitation frequencies, such as the guide vane passing frequency, to avoid structural resonance. The thickness of the blade trailing edge needs to be increased to enhance the deformation resistance and reduce stress concentration on the blade roots. The entire runner can even be manufactured using 3D printing technology to improve the connection design and avoid the high residual stress caused by blade welding. Furthermore, the runner can be made of high-strength stainless steel with better fatigue resistance and surface-strengthening technology to improve the fatigue strength of the blades.
## 3 Control Strategies
### Model Structure of CNN-BLSF  
#### 3.3.1 Physical Model Structure of CNN-BLSF  
In order to address the effects caused by physiological tremors, a novel CNN-BLS-based filter is proposed in this work. The physical model of CNN-BLSF contains: (i) sampling unit; (ii) tremor filtering unit; (iii) control unit. In Figure 4, each unit of the whole CNN-BLSF system is shown.  
* As the central part of the sampling module, the internal measurement unit (IMU) is used to sample the operator's real-time hand movements. It can obtain a three-axis position acceleration and a three-axis joint ang  
Figure 4: Block diagram of the tremor filter.  
Figure 3: Control structure of the controller.  
In the tremor filtering unit module, the CNN-BLS network algorithm is integrated to forecast the tremor signals. The compensation tremor signals \(x_{p}\), \(y_{p}\), \(z_{p}\) and \(\theta_{xp}\), \(\theta_{yp}\), \(\theta_{zp}\) have the same magnitude but opposite phase compared with tremor signals, which can neutralize the tremor signals in the actual signals \(x\), \(y\), and \(z\).
* The control module integrates the calculation of inverse kinematics, the driver of a single joint, and the motion feedback of deflection sensors, which converts inverse kinematics into motion control variables for the robot manipulator.
## 4 Evaluation and Results
### Features Evaluation and Discussion
\begin{table}
\begin{tabular}{c l l} \hline \hline
**FHR Segment** & **Optimal Feature Sets** & **QI (\%)** \\ \hline CS & CS\({}_{4}\), CS\({}_{10}\), CS\({}_{11}\), CS\({}_{12}\) & 81.7 \\ DD & DD\({}_{1}\), DD\({}_{3}\), DD\({}_{8}\) & 66.8 \\ DR & DR\({}_{8}\), DR\({}_{9}\), DR\({}_{10}\) & 76.3 \\ \hline DD\_DR & DR\({}_{8}\), DR\({}_{9}\), DR\({}_{10}\) & 76.3 \\ CS\_DD & CS\({}_{4}\), CS\({}_{11}\), CS\({}_{12}\), DD\({}_{4}\), DD\({}_{8}\) & 82.5 \\ CS\_DR & CS\({}_{4}\), CS\({}_{11}\), CS\({}_{12}\), DR\({}_{5}\) & **83.2**\(\leftarrow\) \\ CS\_DD\_DR & CS\({}_{4}\), CS\({}_{11}\), CS\({}_{12}\), DR\({}_{5}\) & 83.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance obtained by considering CS, DD, and DR.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 3 Neural Network (NN) Based Elasticity
### Data Collection  
Determination of the input and output variables for the neural network is the first task for the approximation of elastic behavior by the FFNNs for finite element applications. The strain-stress mapping can be achieved approximated by the FFNNs without considering the loading history since, for the small strain elasticity (elastic deformation), the loading and unloading curve coincide with each other.
# Real-Time Path Planning for Strategic Missions
## 5 Results and Discussion  
This section presents the simulation and experimental results, which validate the proposed navigation in a changeable environment. The objective is to simulate war or natural disaster scenarios, where the previously defined route must be changed due to unexpected obstructions.  
Before validating the proposed algorithm in dynamic scenarios, it was compared with others that have a similar behavior.
## 3 Results and Discussion
### AE Method Application for Bridge Monitoring
#### 3.2.3 Analysis of AE Signals Propagation  
Hsu-Nielsen source was used to estimate signal propagation properties. The measurements in this experiment were carried out using the A-Line 32D system (Interunis-IT) with threshold data acquisition. The ADC sampling frequency was 2 MHz. The following values were obtained: AE wave propagation speed is 2838 m/s, attenuation coefficient in the far field zone is 1.65 dB/m. Based on the obtained values of the attenuation coefficient, noise level and amplitudes of signals from the AE signal simulator, the maximum distance between the AE sensors for linear or planar location was determined. This value lies in the range from 11 to 16 m.  
An acoustic contact quality was also estimated in zone II (Figure 5b). In the course of the experiment it was revealed that the signals simulated in the girder region are not detected by the AE sensor mounted on the support, which indicates that there is no acoustic contact. Thus, for a full  
Figure 7: AE signals caused by impacts of structural elements: (**a**) Zone I; (**b**) Zone II; (**c**) Zone III.  
monitoring of the bridge, separate AE testing of both the supports and the main upper part of the bridge is necessary.
# Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
## 5 Conclusions  
Although the mesh dependence of the waveform is verified in this paper, a finer mesh is still needed to capture the vortex phenomenon of two-wave collision more accurately. The Courant number of the flow field stays within 0.001 in most of the calculated area, but the Courant number increases locally near the wall of the riser. Different turbulence calculation methods may affect the results to some extent, and the results obtained by different methods should be compared and analyzed in future analysis to further enhance their credibility.  
The results suggest that the proposed numerical simulation of the interaction between multiple internal solitary waves and the riser is reliable and provides accurate estimates and that the influence of right-side waves on the flow field is significant. Unlike the traditional concept of wave collision canceling each other, the force on the riser is not apparent when the amplitude of the two internal solitary waves is the same but in opposite directions. When the amplitude difference of the two internal solitary waves is significant, it has a positive impact on the opposite tube. While the interface change in the two-wave collision fusion is small, it does not mean that the change in the whole flow field is not complex. The vortex phenomenon generated by the collision of the two waves in the lower fluid area significantly impacts the action of the riser. As the amplitude on the right continuous to increase, the collision of the two waves gradually approaches the riser. In this process, the force on the riser increases first and then decreases.
# Common-Mode Voltage Analysis and Reduction for the Quasi-Z-Source Inverter with a Split Inductor
## 4 Modified qZSI for CMV Reduction
Then, the following is obtained:  
\[v_{\text{cmv\_avg}}-xDV_{\text{DC}}=x(1-D)V_{\text{DC}} \tag{18}\]  
Combining (12) and (18), the proportional coefficient \(x\) can be calculated as  
\[x=\frac{0\cdot t_{zero\_0}+1\cdot t_{odd}+2\cdot t_{even}+3\cdot t_{zero\_7}} {3\cdot(t_{zero}+t_{odd}+t_{even})} \tag{19}\]  
The average CMV \(\bar{v}_{CM}\) during one cycle is then obtained as  
\[\bar{v}_{\text{cmv}}=\frac{(1-D)(t_{odd}+2t_{even}+3t_{zero\_7})}{3\cdot(t_{ zero}+t_{odd}+t_{even})}V_{\text{DC}} \tag{20}\]  
The average CMV in (12) during the non-ST state is reduced to the average CMV in (20), while the CMV during the ST state, i.e., 0 V, is increased to the average CMV in (20) with the proposed CMV reduction strategy. The CMV amplitude of the inductor-split qZSI becomes lower by shifting the CMV effect of the ST state, which reduces the high-frequency components. With the proposed CMV reduction strategy, the CMV reduction technique for the conventional VSI can be used for the qZSI system directly with appropriate ST state implementation. Figure 5: Proposed CMV reduction strategies with switching patterns and CMV when using DPWM, AZPWM, RSPWM and NSPWM strategies. Figure 6: Modified quasi-Z source inverter system with a split inductor. According to (14), the inductance variation of \(L_{11}\) and \(L_{12}\) will also be reflected in the ratio of the split inductor, i.e., \(x\). The CMV during the non-ST state and ST state shown in (16) and (17) can be obtained as \(v_{\text{cmv\_avg}}-x^{\prime}DV_{\text{DC}}\) and \(x^{\prime}(1-D)V_{\text{DC}}\) with \(x^{\prime}\) being the new ratio of the split inductor. The inductance tolerance, e.g., \(\pm 5\%\) in \(x\), will make the average CMV vary around \(\pm 5\%\)\(DV_{\text{DC}}\), which is almost negligible.
## 2 Related Work  
### Spiking Neural Network  
SNNs are often referred to as third-generation neural networks because they more closely mimic the behavior of biological neurons than traditional ANNs. Unlike ANNs, which process continuous-valued signals, SNNs transmit information over time using discrete binary spike events [17]. To enable this form of computation, SNNs rely on specialized spiking neurons that convert input signals into binary spikes. Among the commonly used spiking neuron models, the leaky integrate-and-fire (LIF) and the more recent parametric leaky integrate-and-fire (PLIF) neurons are widely adopted [31]. The PLIF model incorporated a learnable membrane time constant, allowing the neuron to adapt its temporal dynamics during training. Its behavior is mathematically described as follows:  
\[V[t]=V[t-1]+\frac{1}{\tau}\Big{(}X[t]-\big{(}V[t-1]-V_{\text{reset}}\big{)} \Big{)} \tag{1}\]  
where \(V[t]\) represents the membrane potential at time step \(t\), \(X[t]\) denotes the input synaptic current, \(\tau\) is a learnable time constant, and \(V_{\text{reset}}\) is the resting membrane potential. When \(V[t]\) exceeds a predefined threshold, the neuron emits a spike and resets its membrane potential.  
SNNs can be trained using two primary strategies: direct training [23] and ANN-to-SNN conversion [32]. In the direct training approach [23], the SNN is optimized end-to-end using backpropagation through time (BPTT). However, because the spiking function is inherently non-differentiable, surrogate gradient techniques [25] are employed to approximate gradients and allow effective training via gradient descent. Alternatively, the ANN-to-SNN conversion method [32] first trains a conventional ANN with continuous activation functions, which are then replaced with spiking neuron models such as LIF or PLIF. The trained weights are mapped directly to the corresponding SNN architecture. Although this approach benefits from the mature training techniques of ANNs, it often struggles to preserve fine-grained temporal dynamics and typically requires a large number of time steps during inference in increased latency.
## 2 Seafarer Fatigue, Sleep and Ship Noise
### Seafarers' Fatigue and Ship Noise
#### 2.3.2 Ship Noise and Sleep Quality  
Research shows that noise in the maritime setting harms seafarers' sleep quality. Noise breaks sleep structure, causing more awakenings and lower sleep efficiency. The resultsreview the negative effects of noise on seafarers' sleep and suggest ways to reduce noise-related sleep problems [41]. The paper concluded that noise disturbances during sleep lead to an increased frequency of awakenings among seafarers. The meta-analysis revealed that exposure to high noise levels in the maritime environment significantly disrupts the sleep structure, causing more frequent but often brief awakenings. The findings figure out that noise-induced awakenings have on the overall sleep quality of seafarers. The paper stresses out the importance of addressing noise as a contributing factor to sleep disturbances among seafarers and emphasizes the need for effective interventions to mitigate noise-related sleep fragmentation in maritime settings [42].  
[PERSON] et al. found that the presence of noise in the maritime environment contributes to decreased sleep efficiency among seafarers. The study's cross-sectional analysis revealed that noise negatively impacts the continuity and quality of sleep, resulting in lower sleep efficiency. Sleep efficiency is defined as the proportion of time spent asleep compared to the total time spent in bed [43].  
[PERSON] et al. revealed that noise interference significantly impacts the different stages of sleep among seafarers. The study found that noise disrupts both rapid eye movement (REM) sleep and non-REM sleep, with an average reduction of 20% in REM sleep duration and 15% in non-REM sleep duration. These disruptions in sleep stages can lead to fragmented and less restorative sleep for seafarers, as indicated by a 30% increase in the number of sleep stage transitions and a 25% decrease in slow-wave sleep, respectively. The paper underscores the detrimental effects of noise on sleep quantity and highlights the need for measures to mitigate noise exposure to promote more consolidated and restful sleep for seafarers [44].  
IMO highlighted that the cumulative effect of noise disturbances has a significant impact on seafarers' sleep quality. The guideline emphasizes that sleep quality encompasses various aspects such as depth, continuity, and subjective satisfaction with sleep. According to the guidelines, seafarers experience a substantial decrease in the depth of sleep, with an average reduction of 25% in slow-wave sleep duration. The data also reveal a 35% increase in the number of sleep interruptions, contributing to a fragmented sleep pattern. Subjective satisfaction with sleep is significantly affected, with seafarers reporting a 40% decrease in perceived sleep quality. The guideline underscores the importance of managing noise-related sleep disturbances to improve overall sleep quality and well-being for seafarers in the maritime industry [45].
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 3 Gait Recognition Using GERF
### Representation of Global GERF  
We apply the Global GERF for the most widely employed appearance-based gait representation, i.e., GEI. Therefore, we first introduce the concept of GEI. The GEI [11] is a gait template generated by averaging registered and size-normalized silhouettes within a complete gait period \(T\) as  
\[I(x,y)=\frac{1}{T}\sum_{t=1}^{T}B(x,y,t), \tag{1}\]  
where \(B(x,y,t)\) is a registered and size-normalized binary silhouette value at the position \((x,y)\) in the \(t\)-th frame (with binary value 0 and \(I_{\text{max}}\) (\(I_{\text{max}}\) is usually 255 for an image with 8-bit depth.) for background and foreground, respectively), and \(I(x,y)\) is a gait energy at the same position. We approximate \(I(x,y)\) as an integer for simplicity, i.e., \(I(x,y)\in\{0,1,\ldots,I_{\text{max}}\}\), since the domain of gait energy is real numbers.  
Figure 2: Pipeline of the proposed method. The learned GERF from the training set can be replaced by the extended spatially dependent GERF (SD-GERF). Here just take Global GERF as an example.  
The Global GERF \(f\) is defined as a transformation from an original gait energy \(I(x,y)\) to a transformed one \(I^{\prime}(x,y)\) as follows  
\[I^{\prime}(x,y)=f(I(x,y))\ \forall(x,y). \tag{2}\]  
Because the original gait energy takes one of \((I_{\text{max}}+1)\) integers from 0 to \(I_{\text{max}}\), the Global GERF is represented as a lookup table \(\mathbf{f}=[f_{0},...,f_{I_{\text{max}}}]^{T}\in\mathbb{R}^{I_{\text{max}}+1}\), where \(f_{i}\) represents a transformed gait energy from an original gait energy \(i\).  
We then define a dissimilarity measure between a pair of transformed GEIs from original GEIs \(I_{1}\) and \(I_{2}\). We simply adopt the Euclidean distance between them and define its squared distance \(d_{I_{1},I_{2}}^{2}\) and further formulate it in the quadratic form of \(\mathbf{f}\) as  
\[d_{I_{1},I_{2}}^{2}=\sum_{x,y}(f_{I_{1}(x,y)}-f_{I_{2}(x,y)})^{2}=\mathbf{f}^{T}A_ {I_{1},I_{2}}\mathbf{f}\, \tag{3}\]  
where \(A_{I_{1},I_{2}}\in\mathbb{R}^{(I_{\text{max}}+1)\times(I_{\text{max}}+1)}\) is a coefficient matrix for quadratic-form representation and its \((l,m)\) component can be calculated as  
\[\begin{split}(A_{I_{1},I_{2}})_{l,m}=&\sum_{x,y}( \delta_{I_{1}(x,y),l}\delta_{I_{1}(x,y),m}+\delta_{I_{2}(x,y),l}\delta_{I_{2}( x,y),m}\\ &-\delta_{I_{1}(x,y),l}\delta_{I_{2}(x,y),m}-\delta_{I_{2}(x,y), l}\delta_{I_{1}(x,y),m})\,\end{split} \tag{4}\]  
where \(\delta_{i,j}\) is the Kronecker delta defined as  
\[\delta_{i,j}=\begin{cases}1&(i=j)\\ 0&(i\
eq j).\end{cases} \tag{5}\]
## 4 Conclusions  
In conclusion, the study demonstrates the effectiveness of modifying the mashing profile to produce low- and no-alcohol peers with reduced fermentable sugars, particularly maltose. This is confirmed by the results obtained for, among other things, the content of individual sugars. The modification of the mashing profile (omission of the saccharification pause) contributed to the production of approximately 25% less of the most important sugar in brewing, i.e., maltose. Thanks to this, the commonly used yeast (_Saccharomyces cerevisiae_ US-05) produced beers with an average content of 2.4-2.7% \(v/v\) during fermentation, which was much lower compared to the Congress mashing (3.5-3.8% \(v/v\)). This approach not only addresses fermentation potential but also ensures sufficient free amino nitrogen compounds for yeast growth and fermentation. The beers obtained with this method were also characterised by a rich sensory profile with high consumer acceptance. In the case of the yeast strain for the production of alcohol-free beer (_Saccharomyces cerevisiae_ var. _chevalieri_), with a standard mashing profile (with a saccharification pause), it contributed to obtaining beers with an alcohol content of 0.5% \(v/v\). For this strain, it is, therefore, important to choose a mashing profile that minimises glucose production by producing a higher amount of maltose. In addition, the production of low-alcohol beers using _Saccharomyces cerevisiae_ var. _chevalieri_ contributed to the production of beers with the desired sensory profile. These beers were characterised by a very rich aromatic profile, including higher alcohols, esters and terpenes. Higher levels of compounds such as \(\beta\) myrcene and 4-terpineol were observed, which also had a significant impact on the sensory profile of the beers obtained. In addition, beers with hops after modification of the mashing profile obtained the highest score (almost 4.5/5 points) in the sensory analysis. The sensory analysis underlines the importance of hop addition and yeast selection and highlights the potential to produce flavourful non-alcoholic beverages while maintaining traditional beer characteristics. Overall, the results emphasise the importance of tailored brewing techniques and yeast strains in achieving desired alcohol levels and sensory experiences in low-alcohol and non-alcoholic beers.  
Conceptualization, A.P. methodology, A.P. and M.J.; software, P.S.; validation, A.P., M.J. and P.S.; formal analysis, A.P. and P.S.; investigation, A.P. resources, A.P.; data curation, A.P.; writing--original draft preparation, A.P. writing--review and editing, P.S.; visualization, A.P. and M.J.; supervision, P.S.; project administration, A.P.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript. This research was financed by the Ministry of Science and Higher Education of Poland as a part of the Science Subsidy No. 070013-D020.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
References