esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result  
### Results on MFPT Dataset  
We evaluated our proposed SpikeCAN model on the 15-class classification task using the MFPT dataset. A five-fold cross-validation method was adopted to ensure statistical reliability. As shown in Table 1, SpikeCAN achieves fold-wise accuracies of 96.50%, 96.31%, 95.15%, 97.48%, and 96.11%, resulting in an average accuracy of 96.31%. Notably, this level of performance is achieved with only 1.36 million parameters and an estimated energy consumption of 0.78 mJ per inference. This result demonstrates significant improvements in energy efficiency. Specifically, the proposed SNN-based SpikeCAN achieved approximately 76.7 times greater energy efficiency compared to its ANN counterpart, which consumed 59.85 mJ per inference. Furthermore, SpikeCAN was also around 66.5 times more energy-efficient compared to the widely used ResNet-18 architecture, which required 51.84 mJ per inference. To the best of our knowledge, this represents state-of-the-art (SOTA) performance compared to existing SNN models on this task.  
We further compared SpikeCAN to two widely used ANN-based baselines: Cluster KD [37] and ResNet-18 [38]. Although ANN models generally have a performance advantage, SpikeCAN demonstrates highly competitive results. Specifically, the accuracy gap between SpikeCAN and Cluster KD, the best-performing ANN model with this task, is only 1.28%, despite the fundamental architectural differences. Moreover, SpikeCAN out performs ResNet-18 in both accuracy and efficiency (as detailed by the energy consumption comparison above), highlighting its suitability for edge and low-power applications.  
Compared to an SNN-based model, SEW-ResNet [26], SpikeCAN not only achieves higher accuracy but also requires fewer parameters and less energy. The confusion matrix presented in Figure 3 reveals that SpikeCAN maintains consistent classification accuracy across all classes, including Class 11 and Class 12, where other models tend to degrade sharply. These results demonstrate that SpikeCAN achieves an excellent balance between accuracy and efficiency. Its ability to deliver near-ANN-level performance while maintaining low computational and energy demands underscores its potential for practical deployment in real-world industrial monitoring systems.
## 1 Introduction
### Hypothesis  
Even though there are numerous investigations on the automatic detection and classification of a CAP and its sub-types (as discussed in Section 2), this research focuses on the implementation of natural language processing (NLP) and pattern recognition techniques on the same problem.  
The main reason for exploring these techniques is to (i) reduce the complexity of the problem by representing the time-series data as shorter sequences of symbols and (ii) try to obtain interpretable results by applying NLP techniques such as _word embeddings_.  
Word embeddings are a vectorial representation of words for text analysis, as proposed in [15]. This representation is based on mapping words (included in a collection of phrases or documents) to real-number vectors that rely on the _distributional hypothesis_. This hypothesis was popularized by [PERSON] in 1957, who stated that \"words that occur in similar contexts tend to have similar meanings\" [16].  
By assuming that signals can be translated into symbols and then organized as \"words\", they can also be, theoretically, represented as word embeddings. Then, the embeddings can be analyzed with machine learning models, as carried out in NLP.  
This being the case, the hypothesis for this research is that using a distributional approach (based on the distributional hypothesis) for EEG signal representation will allow for the implementation of NLP techniques to solve the CAP detection and classification problem. This will be achieved using a context-based vectorial representation of signal segments as if they were words or phrases in a text.
## Appendix A Information on Geometry Dimensions  
This appendix provides the resonator dimensions for the geometries depicted in Figure 2. For geometries using WPA, the variables are marked with a prime symbol\({}^{\prime}\), while no prime symbol is used for geometries with LPA. For performing the numerical simulations with CST software, the parameters of each geometry varied in the interval given by \([L_{\text{bound}},U_{\text{bound}}]\) when the values of the parameters are taken using a resolution equal to \(\Delta\). Table A1 lists for each geometry, the values of \(L_{\text{bound}}\), \(U_{\text{bound}}\), and \(\Delta\). Moreover, Tables A2-A4 list the values of the dimensions obtained in the numerical simulations for geometries combined to DW, glycerin, and graphite materials when the highest values of MLM is considered. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Geometry** & **Parameter** & \(L_{\text{bound}}\) & \(U_{\text{bound}}\) & \(\Delta\) \\ \hline \multirow{2}{*}{Retangular prism} & \(h_{\text{rp}}\) & 1 & 20 & 0.1 \\ \cline{2-5}  & \(L_{\text{rp}}\) & 15 & 35 & 1 \\ \hline Hollow rectangular prism & \(h_{\text{hrp}}\) & 1 & 20 & 0.1 \\ \hline Cylindrical hemisphere & \(r_{\text{hy}}\) & 1 & 30 & 0.1 \\ \hline Concave hemisphere & \(r_{\text{sp}}\) & 16 & 30 & 0.1 \\ \hline Hollow concave hemisphere & \(r_{\text{sph}}\) & 16 & 30 & 0.1 \\ \hline Hemisphere & \(r_{\text{spc}}\) & 16 & 30 & 0.01 \\ \hline Inverted cone & \(h_{\text{conc}}\) & 1 & 30 & 0.05 \\ \cline{2-5}  & \(r_{\text{t}}\) & 7.1 & 40 & 0.05 \\ \hline Hollow inverted cone & \(h_{\text{ch}}\) & 1 & 30 & 0.05 \\ \cline{2-5}  & \(r_{\text{th}}\) & 7.1 & 30 & 0.1 \\ \hline Cone & \(h_{\text{conv}}\) & 1 & 30 & 0.05 \\ \cline{2-5}  & \(r_{\text{bc}}\) & 7.1 & 30 & 0.1 \\ \hline LPA inverted half-cylinder & \(r_{\text{cy}}\) & 1 & 20 & 0.1 \\ \hline LPA hollow inverted half-cylinder & \(r_{\text{cyh}}\) & 1 & 20 & 0.1 \\ \hline LPA half-cylinder & \(r_{\text{cyc}}\) & 1 & 20 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table A1: Dimensions (in mm) and corresponding ranges for various geometric shapes.
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 2 Method
### Balanced Loss Function
#### 2.3.1 Dynamical Class Weighting  
We address the label imbalance problem by a dynamical weighting scheme inspired by class-balanced loss proposed by [PERSON] et al. [21]. Class-balanced loss aims to weigh class-specific loss by the number of effective examples. The class-balanced loss for classification is formulated as:  
\[\mathcal{L}_{CB}=-\frac{1-\beta}{1-\beta^{N_{k}}}\log(1-\epsilon) \tag{1}\]  
where \(\beta\in(0,1)\) is a hyper-parameter, \(k\) is the label of the example, and \(N_{k}\) is the occurrence of class \(k\) in the dataset. This class-balanced loss will assign higher weight to the loss of less-represented classes while training. Experiments in [21] show accuracy improvement on the class-imbalanced data of image classification problems.  
In surface defect segmentation, the label imbalance problem exists at the pixel level. We adapt the class-balanced loss to a dynamical one by scaling loss values by pixel label distribution, which means \(N_{i}\) in Equation (1) should represent the pixel number of a class. Assume that for the annotation \(A\) of an image, the label distribution is \(\{N_{1},N_{2},...,N_{C}\}\) that satisfies \(N_{1}+N_{2}+...+N_{C}=W*H\). We define a pixel weighting mask \(M_{class}\), whose value at point \((i,j)\)(pixel label is \(k\)) is:  
\[M_{class_{i,j}}=\frac{1-\beta}{1-\beta^{N_{k}}} \tag{2}\]  
Our approach is different from the original class-balanced loss in several ways. First, it is no longer associated with cross-entropy loss or other loss functions; it is a soft mask over pixel-level loss values that performs weighting. Second, \(N_{k}\) counts pixels rather than the images of a class. Last, unlike [21] where the hyper-parameter \(\beta\) is selected based on dataset volume, we select \(\beta\) by the image size \(W\times H\). The experiments for the optimal selection of \(\beta\) is provided in Section 3.
## 5 Experiment Results
### Score Metric  
The conventional metrics of precision, recall, and F1-Score are used to assess the performance of various methods. The preferred outcome for end-users is to obtain prompt and precise alarms with few false positives (FP), which may consume time and resources. The following window-based rules are implemented to discourage excessive FPs and encourage prompt and precise alarms: A true positive (TP) is recorded if a predicted window overlaps with a labeled anomalous window. A false negative (FN) is recorded if a forecasted window does not overlap with a labeled anomalous window. An FP is recorded if a labeled anomalous window does not overlap with a forecasted window. This method is also adopted in [PERSON]'s method [29] and [PERSON]'s method [23].
## 2 Materials and Methods
### LSTM Networks  
Recurrent Neural Networks (RNNs) are specialized networks designed for handling sequential data [44]. As illustrated in Figure 2, an RNN contains an internal loop structure, enabling the propagation of previous time-step state information to the current state to establish temporal relationships and exhibit dynamic behavior [45]. The output at the time step \(t\) is determined by the input at the time step \(t\) and the output at the time step \(t-1\). However, as time progresses, information loss occurs during the propagation process, leading to issues such as gradient vanishing in RNNs, thereby diminishing their ability to handle long-term dependencies in time series data [46]. The computational process of RNNs can be represented by Equations (10) and (11):  
\[h_{t}=tanh(Ux_{t}+Wh_{t-1}+b_{h}) \tag{10}\]  
\[y_{t}=softmax(Wh_{t}+b_{y}) \tag{11}\]  
where \(x_{t}\), \(h_{t}\) and \(y_{t}\) denote the input vectors, hidden unit states, and outputs at moment \(t\), respectively, and \(W\), \(U\) and \(V\) denote the weight matrices of the corresponding RNN layers. The parameters \(b_{h}\) and \(b_{y}\) are bias vectors.  
In response to the problem of gradient vanishing and other issues commonly encountered in RNNs, [PERSON] et al. proposed LSTM to address this challenge. LSTM, based on the original network architecture, replaces the RNN units in the hidden layers with LSTM units and resets the computational nodes. A basic LSTM neural unit is controlled by three gates: the input gate, the output gate, and the forget gate. Its structure is illustrated in Figure 3.  
Figure 2: Neurons of Recurrent Neural Network.  
In Figure 3, \(x_{t}\) is the input at the current moment, \(h_{t}\) and \(h_{t-1}\) represent the state of the hidden layer at the current moment and the previous moment, respectively; \(C_{t}\) and \(C_{t-1}\) represent the state of the cell at the current moment and the previous moment, respectively, \(x_{t}\), \(h_{t}\), \(C_{t}\) are fed into the LSTM through these three gate structures. each gate computes the input information and decides whether to activate it or not according to its logical function. In this case, the forgetting gate \(f_{t}\) discards the information that is considered unimportant by the memory cell, the input gate \(i\) recognizes the new information that needs to be retained in the cell state and updates it with the activation function, and the output gate \(o_{t}\) decides the output based on the cell state. The above process can be expressed by Equations (12)-(16):  
\[i_{t}=\sigma(T_{i}x_{t}+V_{i}h_{t-1}+q_{i}) \tag{12}\]  
\[f_{t}=\sigma\Big{(}T_{f}x_{t}+V_{f}h_{t-1}+q_{f}\Big{)} \tag{13}\]  
\[C_{t}=f_{t}\otimes C_{t-1}+i_{t}\otimes tanh(T_{C}x_{t}+V_{C}h_{t-1}+q_{C}) \tag{14}\]  
\[o_{t}=\sigma(T_{o}x_{t}+V_{o}h_{t-1}+q_{o}) \tag{15}\]  
\[h_{t}=o_{t}\otimes tanh(C_{t}) \tag{16}\]  
where \(\sigma\) and \(tanh\) respectively denote the Sigmoid activation function and the hyperbolic tangent activation function, \(T\) and \(V\) denote the parameter matrices, \(q\) denotes the bias vector, and \(\otimes\) denotes element-wise multiplication.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions
### UAV-to-UAV and Satellite-to-UAV Communication  
When using a UAV as a communication terminal, the Doppler effect, pointing error effect, and atmospheric turbulence effect should all be carefully considered. To receive the frequency-shifted optical signals caused by the Doppler effect, the bandwidth of the optical filter at the receiver should be increased. When analyzing UAV-satellite channels, attention should be paid to the optimization effect in terms of cost efficiency. The receiver diameter design is related to the payload of a UAV in a DL and the restrictions on a satellite in a UL, whereas the transmission power design is related to the payload of a UAV in a UL. Because of practical effects such as the Doppler effect, atmospheric turbulence, and pointing error are all considered. It is also important to note that a swarm of UAVs forms a multi-hop network that assists ground wireless devices in transmitting and receiving packets, each of which contains a direction, in order to provide communication services over a relatively large area. Due to the high-speed flexibility and the need to maintain close communication links with ground users, the interface connection with nearby UAVs is disengaged as much as possible. All standard steering protocols will not work with FANETs in this scenario. As a result, mastering UAV flight control may be difficult. When multiple UAVs collaborate, avoiding collisions becomes a critical issue for UAV security. As a result, point-by-point proliferation sequences are required in modern satellite-to-UAV channel models [121].
# Analysis of an Elasto-Hydrodynamic Seal by Using the Reynolds Equation
###### Abstract  
This paper reports numerical studies of an Elasto-Hydrodynamic (EHD) seal, which is being developed for supercritical CO\({}_{2}\) (sCO\({}_{2}\)) turbomachinery applications. Current sCO\({}_{2}\) turbomachinery suffers from high leakage rates, which is creating a major roadblock to the full realization of sCO\({}_{2}\) power technology. The high leakage rates not only penalize the efficiencies but also create environmental concerns due to greenhouse effects caused by the increased CO\({}_{2}\) discharge to the atmosphere. The proposed EHD seal needs to work at elevated pressures (10-35 MPa) and temperatures (350-700 \({}^{\circ}\)C) with low leakage and minimal wear. The unique mechanism of the EHD seal provides a self-regulated constriction effect to restrict the flow without substantial material contact, thereby minimizing leakage and wear. This work utilizes a physics-based modeling approach. The flow through the gradually narrowing seal clearance is modeled by the well-known Reynolds equation in EHD lubrication theory, while the deformation of the seal is modeled by using the governing equations of three-dimensional solid mechanics. As for the solution methodology, COMSOL's Thin-Film Flow and Solid Mechanics modules were employed with their powerful capabilities. The numerical results were presented and discussed. It was observed that the Reynolds equation fully coupled with the surface deformation was able to successfully capture the constriction effect. The maximum and minimum leakages were calculated to be 2.25 g/s and 0.1 g/s at P = 5.5 MPa and P = 11 MPa for the design seal, respectively. It was interesting to observe that the seal leakage followed a quadratic trend with increasing pressure differential, which can become advantageous for high-pressure applications such as sCO\({}_{2}\) power generation technology.  
e 9 August 2022  
Accepted: 17 September 2022  
Published: 22 September 2022  
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# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 5 Research Gaps and Future Directions  
### Design Phase  
Currently, the trend in applying digital technologies in the design phase of industrialized construction emphasizes the integration of innovative design methods, such as parametric design and discrete design, with digital tools. The objective is to support the efficient design and optimal fabrication and assembly of complex geometrical forms, thereby enhancing building performance. These design methods, combined with techniques such as performance-driven optimization and differential geometry, enable designers to efficiently explore optimal solutions under manufacturing and assembly constraints. Moreover, performance simulations and assessments, such as energy analysis and life cycle assessment, have been incorporated into the design process, enabling designers to optimize the energy efficiency and environmental impact of buildings from an early stage. Future research directions include the following:  
* While parametric and discrete design have enabled the realization of complex geometries and non-standard forms, further research into materials and the refinement of production processes for precast elements is needed for commercial production. Currently, wood and composite materials are considered ideal for discrete design [21]. Future research should systematically compare the suitability of different materials and investigate methods for designing large-scale, complex prefabricated concrete components.
* It is reported that the current IFC standard has limited support for information related to industrialized construction. Therefore, further research on open data standards and tools, such as an information delivery manual, is necessary to improve interoperability between digital design and the subsequential production tasks.
* One of the key barriers to applying LCA in the design process is the lack of accurate and comprehensive data, particularly regarding the environmental impacts of building systems. Future research can enable users to create their own online databases of construction systems. This will offer more flexibility in accessing and exchanging environmental impact information.
## 2 PICO-Inspired Impairment Analysis for Hand Exoskeletons
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**P-Problem** & & \multicolumn{3}{c}{**Skeletal**} \\
**(Impairment)** & **CNS** & \multicolumn{3}{c}{**PNS**} \\
**(Dissorders)** & **Disorders** & \multicolumn{3}{c}{**Muscle**} \\  & & **Disorders** & \multicolumn{3}{c}{**Disorders**} \\ \hline ROM limitation & Unidirectional & \multicolumn{3}{c}{x} \\  & Active & \multicolumn{3}{c}{(x)} \\  & Passive & \multicolumn{3}{c}{(x)} \\ \hline Strength reduction & Fine motor control impairment (mainly fingers) & \multicolumn{3}{c}{Ergometers (load cells), EMG 1, cameras, clinical scales} \\  & Gross motor control impairment (including the wrist) & \multicolumn{3}{c}{Ergometers (load cells), EMG, cameras, clinical scales} \\  & muscle atrophy from disuse/reduced excitation & \multicolumn{3}{c}{ergometers (load cells), EMG, cameras, echography, MRI 2, CT 3, meter, clinical scales} \\ \hline Coordination disorders & Dexterity & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\  & Coccontraction & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\  & Ataxia & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\  & Dysmetria & \multicolumn{3}{c}{EMG, cameras, clinical tests} \\ \hline Sensory disorders & Tactile–hypoesthesia/paresthesia & \multicolumn{3}{c}{Clinical assessment} \\  & Proprioceptive impairment & \multicolumn{3}{c}{Clinical assessment} \\  & Nociceptive—paresthesia/hyperalgesia/allodynia & \multicolumn{3}{c}{Clinical assessment} \\ \hline Tone alterations & Hypertonía/spasticity & \multicolumn{3}{c}{Clinical assessment/scales, basic research devices} \\  & & \multicolumn{3}{c}{(poorly used in clinical settings)} \\  & Hypotonia & \multicolumn{3}{c}{Clinical assessment/scales} \\  & Dysotonia & \multicolumn{3}{c}{Clinical assessment/scales} \\ \hline Other motor disorders & Mycolonia & \multicolumn{3}{c}{Clinical assessment, EMG} \\  & Hyper-reflexia & \multicolumn{3}{c}{Clinical assessment, EMG} \\  & Spasms & \multicolumn{3}{c}{Clinical assessment, EMG} \\  & Tremor & \multicolumn{3}{c}{Clinical assessment, EMG} \\ \hline Pain & \multicolumn{3}{c}{VAS 4, pressure test} \\ oedema & \multicolumn{3}{c}{Clinical assessment} \\ Fatigue & \multicolumn{3}{c}{Clinical assessment/scales, ergometers, EMG} \\ \hline \hline \end{tabular}  
* \({}^{1}\) Electromyography ; 2 Magnetic Resonance Imaging; 3 Computed Tomography, also known as Computed Axial Tomography (CAT); 4 Visual Analog Scale.  
\end{table} 
Table 1: The P (problem) and O (outcome) elements of the PICO model for the current study: the first two columns present the lists of the identified impairments and typologies, respectively. The third column describes the measures typically used to assess the outcomes.
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 4 Theoretical Analysis Results  
### Strain Transfer Rate of a Clamped FBG Sensor  
Table 1 lists the physical parameters and mechanical properties of materials, which will be used in the following analysis. Strain transfer rate distribution of the \(m\)-segment of the optical fiber is from Equation (23), while the other part is calculated from Equation (27). The 0 point in Figure 4 is located in the middle of the fiber, which is different from the previous point. The strain transfer rate distribution curve of a clamped FBG sensor with different gauge ratios is demonstrated in Figure 4. Those parameters are consistent with Table 1. The curve is composed of two parts: a symmetric curve and a straight line. The region from A to B of the curve shows an increasing trend for the maximum strain transfer rate. The strain transfer rate keeps constant in the region of B-C. The strain measurement of a clamped FBG sensor is equal to the average strain of the bonded range, which reveals the actual stress state of the matrix.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter/Unit** & **Symbol** & **Value** \\ \hline Elastic modulus of optical fiber/MPa & \(E_{S}\) & 72,000 \\ Elastic modulus of interlayer/MPa & \(E_{j}\) & 30 \\ Poisson ratio & \(\mu\) & 0.48 \\ Radius of optical fiber/\(\upmu\)m & \(r_{g}\) & 62.5 \\ Interlayer thickness/\(\upmu\)m & h & 440 \\ Gauge ratio parameter & A & 1.25 \\ Interlayer length/mm & L\({}_{1}\) & 25 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters.  
Figure 4: The axial distribution of FBG strain transfer rate.  
When gauge ratio parameters are equal to 1.25, 1.5, 2 and 3, the maximum strain transfer rates are respectively 79.63%, 61.87%, 35.63% and 14.54%, and the average strain transfer rates are 66.82%, 46.66%, 25.04% and 9.91%, respectively. Considering the influence from the gauge length of the optical fiber, when the gauge ratio parameter is larger, the strain rate is lower. As a result, it is predicted that the strain transfer rate of a clamped FBG sensor could be improved if the gauge ratio parameter is reduced. Therefore, increasing the interlayer length and decreasing the gauge range are proposed as measures to improve the strain transfer rate.