esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Frequency-Dependent Constitutive Equation for the Dissipative Structure
### Introduction of the Acoustic Impedance Boundary Operator and Radiation Impedance Operator  
Let \(\psi^{\mathrm{sol}}_{\Gamma_{E}}\) be the value of \(\psi^{\mathrm{sol}}\) on \(\Gamma_{E}\). For all \(\mathbf{x}\) in \(\Omega_{E}\), let \(\mathbf{R}(\mathbf{x},\omega/c_{\mathrm{E}})\) be the complex linear operator such that  
\[\psi^{\mathrm{sol}}(\mathbf{x},\omega)=\mathbf{R}(\mathbf{x},\omega/c_{ \mathrm{E}})\,v\,, \tag{124}\]  
and let \(\mathbf{B}_{\Gamma_{E}}(\omega/c_{\mathrm{E}})\) be the complex linear boundary operator such that  
\[\psi^{\mathrm{sol}}_{\Gamma_{E}}=\mathbf{B}_{\Gamma_{E}}(\omega/c_{\mathrm{E} })\,v\,. \tag{125}\]  
Using Equation (120), for all \(\mathbf{x}\) in \(\Omega_{E}\), the pressure field \(p(\mathbf{x},\omega)\) is written as  
\[p(\mathbf{x},\omega)=\mathbf{Z}_{\mathrm{rad}}(\mathbf{x},\omega)\,v\,, \tag{126}\]  
in which \(\mathbf{Z}_{\mathrm{rad}}(\mathbf{x},\omega)\) is called the radiation impedance operator that can then be written as  
\[\mathbf{Z}_{\mathrm{rad}}(\mathbf{x},\omega)=-i\,\omega\,\rho_{\mathrm{E}}\, \mathbf{R}(\mathbf{x},\omega/c_{\mathrm{E}})\,. \tag{127}\]  
Similarly, the pressure field \(p|_{\Gamma_{E}}(\omega)\) on \(\Gamma_{E}\) is written as  
\[p|_{\Gamma_{E}}(\omega)=\mathbf{Z}_{\Gamma_{E}}(\omega)\,v\,, \tag{128}\]in which \(\mathbf{Z}_{\Gamma_{E}}(\omega)\) is called the acoustic impedance boundary operator, which can then be written as  
\[\mathbf{Z}_{\Gamma_{E}}(\omega)=-i\,\omega\,\rho_{\mathrm{E}}\,\mathbf{B}_{ \Gamma_{E}}(\omega/c_{\mathrm{E}})\,. \tag{100}\]
## 2 Materials and Methods
### Complex Network Model  
Complex network makes it possible to undertake a quantitative study on complex systems from different scales and perspectives [23]. This analysis approach abstracts entities in a system into nodes and correlations between entities into edges. A complex network with N nodes can be expressed as \(G=(V,\,E)\). Here \(V=\{v_{1},v_{2},\dots,\,v_{n}\}\) represents the set of nodes; \(E=\{E_{1},E_{2},\dots,E_{m}\}\), the set of edges; \(n\), the number of nodes; and \(m\), the number of edges.  
At the macro scale, complex network offers various topological properties to describe the overall situation of the network. At the meso-scale, complex network draws upon community detection to mine the system's pattern. At the micro scale, complex network measures nodes' importance with centrality indicators. In this article, we focus on the nodes' properties and base our discussion at the micro scale. Classic centrality indicators include degree, closeness, betweenness and PageRank centrality [24; 25; 26; 27]. The indicators are defined as follows:  
(1) Degree centrality (DC) describes the number of other nodes linked to a specific node in the network. DC is expressed as in Formula (1) where \(k_{i}=\sum_{i}a_{ij}\). Here, \(a_{ij}\) represents the element in row \(i\) and column \(j\) of the adjacency matrix A; \(n\), the number of nodes in the network; and \(n-1\), nodes' maximum degree. In the complex network, a node's degree centrality shows its range of influence. The higher the degree is, the more important the node is. The average of all nodes' degree centrality is the average degree.  
\[DC_{i}=\frac{k_{i}}{N-1} \tag{1}\]  
(2) Closeness Centrality (CC) rules out the interference of special values by calculating the average distance from a specific node to all other nodes in the network. CC is expressed as in Formula (2) where \(d_{ij}\) is the average minimum distance from a random node \(i\) to other nodes in the network. The shorter the average minimum distance is, the higher the CC is.  
\[CC_{i}=\frac{N-1}{\sum\limits_{j=1}^{N}d_{ij}} \tag{2}\]  
(3) Betweenness centrality (BC) shows nodes' function as bridges and is expressed as in Formula (3). \(g_{st}\) means the number of the shortest paths between nodes \(s\) and \(t\), and \(n_{st}^{i}\) represents the number of the shortest paths which are between nodes \(s\) and \(t\) and pass through node \(i\). Node \(i\) acting as a bridge reflects i's control over the transmission of information between other nodes along the shortest paths.  
\[BC_{i}=\sum\limits_{s\
eq i\
eq t}\frac{n_{st}^{i}}{Sst} \tag{3}\]  
Figure 1: CWRU bearing test rig.  
(4) PageRank centrality (PC) shows the quality of a node's neighbors and is expressed as in Formula (4). \(k_{j}^{out}\) is the out-degree of node \(j\). The higher a node's PC, the more nodes pointing towards it. The specific node also has high-degree friends.  
\[PC_{i}(k)=\sum_{j=1}^{N}a_{ji}\frac{PC_{j}(k-1)}{k_{j}^{out}}i=1,2,\ldots,N. \tag{4}\]
# Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
## 4 Our Approach
### Attentive LSTM  
LSTM [34] is one of the most representative variations of recurrent neural network (RNN) architectures. It was widely used in time series modelling since it can overcome the problem of vanishing gradients and better capture long-term dependencies of time series [7; 35]. In particular, it is common used to the prediction of stock price, Bitcoin price [26; 27], and so on. As shown in Figure 3a, before applying LSTM, we first leverage a fully connected layer to learn features from the inputs \(x_{t}\). The learnt features are represented by a latent representation \(f_{t}\), which is \(f_{t}=\tanh(W_{m}x_{t}+b_{m})\), where \(W_{m}\) and \(b_{m}\) are parameters to be learnt. After this process, an LSTM layer is applied to map \([f_{1},f_{2},...,f_{T}]\) to a hidden representation \(h_{t}\in[h_{1},h_{2},...,h_{T}]\). More specifically, \(h_{t}\) is computed by combining the latent  
Figure 3: Overview of our ALEN model.  
representation \(f_{t}\) and previous hidden representation \(h_{t-1}\). It can be formulated as \(h_{t}=\text{LSTM}(f_{t},h_{t-1})\). Please note that each cell in the LSTM structure can be computed as follows:  
\[\begin{bmatrix}\tilde{c_{t}}\\ o_{t}\\ i_{t}\\ \tilde{f_{t}}\end{bmatrix} =\begin{bmatrix}\tanh\\ \sigma\\ \sigma\\ \sigma\end{bmatrix}T_{A,b}\begin{bmatrix}f_{t}\\ h_{t-1}\end{bmatrix} \tag{7}\] \[c_{t} =\tilde{c_{t}}\odot i_{t}+c_{t-1}\odot\tilde{f_{t}}\] (8) \[h_{t} =o_{t}\odot\tanh(c_{t}), \tag{9}\]  
where \(i_{t}\) represents an input gate, \(\tilde{f_{t}}\) represents a forget gate, \(o_{t}\) represents an output gate, \(c_{t}\) represents a memory cell and \(h_{t}\) is a hidden state. \(T_{A,b}\) is an affine transformation, which depends on parameters of the network \(A\) and \(b\), \(\sigma\) denotes the logistic sigmoid function and \(\odot\) denotes the element-wise multiplication.  
Although the LSTM layer can learn long-term dependencies, it cannot detect the important time instance for financial time series prediction. It is important to capture the significance of different time slots for stock movement prediction, as shown in [7, 8]. Attention mechanism is much more important, especially in our Bitcoin price fluctuation prediction problem, since our problem mainly focuses on the large fluctuation of Bitcoin price. Formally, the hidden representation \(h_{t}\) learnt from LSTM is transformed as follows:  
\[\mathbf{g} =v_{g}^{\top}\tanh(W_{g}h_{t}+b_{g}), \tag{10}\] \[\gamma_{i} =\frac{\exp(g_{i})}{\sum_{j=1}^{T}\exp(g_{i})},\] (11) \[e^{\text{btc}} =\sum_{i=1}^{T}\gamma_{i}h_{i}, \tag{12}\]  
where \(W_{g}\), \(b_{g}\), \(v_{g}\) are parameters to be learnt, \(\gamma_{1:T}\) are the temporal attention weights, and \(e^{\text{btc}}\) is the final representation obtained through the attentive LSTM.
## 3 LPWAN Technologies and Design Goals
### Link Budget  
LoRa and Sigfox work on a tight link budget of \(150\pm 10\) dB [36], which lets them attain a coverage of up to 15 km in a rural area and a few kilometres in an urban environment. In order to attain a high sensitivity of up to \(-130\) dBm [15], LoRa sacrifices the data rate by decreasing the modulation rate to place more energy in each symbol, enabling the reception of highly attenuated signals. LoRa uses the spread spectrum modulation technique for adding noise immunity; Sigfox, on the other hand, uses the ultra narrow band (UNB) technique [16]. LoRa receiver sensitivity will essentially depend on the spreading factor, which typically ranges between \(-130\) dBm and \(-105\) dBm; the same can be calculated for a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specifications** & **LoRa**[15; 32] & **Sigfox**[16; 33] \\ \hline Modulation & CSS & DBPSK/GFSK (UNB) \\ \hline Bandwidth & \(125/250/500\) KHz & \(100\) Hz \\ \hline Frequency Band & \(433\), \(868\), \(915\) MHz & \(868\), \(902\) MHz \\ \hline Localization & Available & Not Available \\ \hline Encryption & AES \(128\) & AES \(128\) \\ \hline Payload & Upto \(255\) byte & Upto \(12\) byte \\ \hline Adaptive Data Rate & Available & Not Available \\ \hline Data rate & Upto \(50\) kbps & \(600\) bps (DL), \(100\) bps (UL) \\ \hline Sensitivity & \(-137\) dBm & \(-129\) dBm \\ \hline Topology & Star of Stars & Star \\ \hline Devices/Gateway & \(1\) k & \(1\) M \\ \hline Duty Cycling & \(1\)\% & \(1\)\% \\ \hline MAC & ALOHA & ALOHA \\ \hline Coding Rate & Available (4) & N/A \\ \hline Proprietary Aspect & Physical layer & Physical and MAC \\ \hline Over the air updates & Available & Not Available \\ \hline Forward Error Correction & Available & Not Available \\ \hline Message per day & Unlimited & \(140\) (UL) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical- and Deployment-Based Specifications of LoRa and Sigfox.  
LoRa device with a transmitting power of 14 dBm, distance \(d\) as 1 km, a path loss exponent (PLE) of \(n=3\) for urban area setting, path loss reference distance \(d_{0}\) (1 m) of 32 dB, and a nominal antenna gain of 2 dBi. The path loss and link budget are calculated in dB for the above data and given in Equations (1)-(3).  
\[Pathloss=10*n*log_{10}(d)+L_{0} \tag{1}\]  
\[Pathloss=10*3*log_{10}(1000)+32=92 \tag{2}\]  
\[Linkbudget=14+2+2-92=-74 \tag{3}\]  
Thus this value of link budget is well below the receiver threshold of \(-130\) dBm, also use of higher spreading rate will increase receiver sensitivity.
## 2 Previous Works
### AI and ML Applications  
AI and ML techniques have been increasingly used in SPVS modeling to improve accuracy and efficiency. For instance, Artificial Neural Network (ANN) models have been used to extract parameters of single-diode PV modules [20], model I-V curves [21], and predict PV module behavior under various atmospheric conditions [20]. Genetic algorithms have also been coupled with radial basis functions to optimize the number of connections in the ANN hidden layer [9].  
Moreover, [PERSON] et al. [21] presented in its review the latest ML techniques applied to SPVSs, with a more decisive focus on deep learning. It explored research on the use of ML for the control, detection of islanding, management, fault detection and diagnosis, irradiance and power generation, forecasting, and sizing of SPVSs. To date, over 100 research articles that have appeared predominantly in the last 5 years have been reviewed, showing the state-of-the-art application of ML techniques in SPVSsLikewise, [PERSON], [PERSON], and [PERSON] [22] conducted a comparative analysis of ANNs and Multiple Linear Regression (MLR) to model the efficiency of SPVS power plants, taking into consideration various weather conditions. Their research highlights the strengths and limitations of these methods in predicting SPVS efficiency. While MLR is simpler and more interpretable, ANNs demonstrated superior predictive accuracy, especially in handling non-linear relationships between variables like temperature, humidity, and irradiance. This study underscores the growing importance of advanced AI techniques in enhancing predictive models for energy systems, offering insights into how machine learning can be leveraged for improving operational efficiency in renewable energy sectors.  
The National Renewable Energy Laboratory (NREL) has also played a part in this growing knowledge through its poster on AI and ML toward enhancing SPVS reliability and system performance. Thus, in this regard, it focused on the development of white-box machine learning, integrating statistical signal processing, convex optimization, and expert domain knowledge. In addition, this study focused on the use of deep neural networks for automated metadata extraction and quality assurance for promoting SPVS reliability and improved performance.  
[PERSON] and [PERSON] [23] also provided a detailed examination of the LASSO method as a tool for feature selection in ML. This technique is widely recognized for its ability to enhance model performance by eliminating irrelevant or redundant features from datasets, thereby simplifying models without significantly compromising predictive accuracy. The authors conducted a comparative analysis of LASSO against traditional feature selection methods and praised its computational efficiency and ability to handle large datasets with numerous variables. The paper concludes that LASSO is particularly effective in high-dimensional data scenarios, which makes it a valuable tool in areas such as predictive analytics and data-driven decision-making.
# Sound Effects on Standing Postural Strategies in the Elderly via Frequency Analysis Approach
###### Abstract  
Sound and sound frequency could improve postural sway in the elderly. The power spectrum intervals of the center of pressure (COP) displacement are associated with different postural regulations, which could be revealed by frequency analysis. The aim of the study was to investigate the effects of sound on dual-tasking postural control and conduct frequency analysis to distinguish postural regulations in the elderly. Fifteen young and 15 older healthy participants were instructed to stand on a force platform and performed the Purdue Pegboard test while hearing 50 dB sounds with sound frequencies of 250 Hz, 1000 Hz, 4000 Hz, or no sound. The total excursion, velocity, sway area, and power spectrum of low-, medium-, and high-frequency bands of the COP displacement were calculated in the anterior-posterior and medial-lateral directions. The percentages of low-frequency and medium-frequency bands in both directions were significantly different between with and without sound conditions, but not affected by sound frequency. Older adults showed a smaller percentage of low-frequency, larger percentage of medium-frequency, larger total COP excursion, and faster velocity in the medial-lateral direction. The outcome of the study supports the frequency analysis approach in evaluating sound effects on postural strategies in dual-tasking and reveals older adults utilize vestibular regulation as the primary postural strategy when the dual-task required visual attention.  
Keywords:postural control strategy; frequency analysis; dual-task; older adult +
Footnote †: journal: Applied Sciences
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 4 Discussion  
This section compares the calculated workload of CQWO-TD-CDE, TD-CDE, and FD-CDE. When calculating complexity in CQWO-TD-CDE, it is necessary to consider window function weight optimization. The comparison content for (I) each equalized output value needs real number multiplication \(\mathrm{n}_{\mathrm{R}_{\mathrm{mul}}}\) and real number addition \(\mathrm{n}_{\mathrm{R}_{\mathrm{mul}}}\); (II) total amount of calculation in the equalization process about real number multiplication \(\mathrm{n}_{\mathrm{R}_{\mathrm{mul}}}^{T}\) and real number addition \(\mathrm{n}_{\mathrm{R}_{\mathrm{mul}}}^{T}\). To facilitate quantitative comparison of the calculation operations between different equalizers, a constant factor \(\eta_{\mathrm{mul}}\) and \(\eta_{\mathrm{a}\mathrm{d}}\) is introduced, and the expression is  
Figure 7: The Q-penalty performance of the DP-QPSK and DP-16 QAM data. We compare the filters of the TD-CDE FIR filter and the CQWO-TD-CDE FIR filter under different quantization stages M. (**a**) Q-penalty curve under the DP-QPSK, and the fiber transmission distance is 1000 km. (**b**) Q-penalty curve under the DP-16 QAM and the fiber transmission distance is 700 km.  
\[\eta_{\text{mul}} =1-\mathbf{n}_{\text{R}_{\text{mul}}}^{T_{1}}/\mathbf{n}_{\text{R}_ {\text{mul}}}^{T_{2}} \tag{25}\] \[\eta_{\text{add}} =1-\mathbf{n}_{\text{R}_{\text{add}}}^{T_{1}}/\mathbf{n}_{\text{ R}_{\text{add}}}^{T_{2}}\]  
Here, \(\eta_{\text{mul}}\) and \(\eta_{\text{add}}\) are the optimizations indexed of the proposed algorithm to calculate the operands, \(\eta_{\text{mul}}\) is the real number multiplication calculation, and \(\eta_{\text{add}}\) is the real number addition calculation. The larger \(\eta_{\text{mul}}\) and \(\eta_{\text{add}}\), the lower the computational complexity of the proposed algorithm. \(\mathbf{n}_{\text{R}_{\text{mul}}}^{T_{1}}\) and \(\mathbf{n}_{\text{R}_{\text{add}}}^{T_{1}}\) are the total calculation times of real number multiplication and real number addition in the experimental group (CQWO-TD-CDE). \(\mathbf{n}_{\text{R}_{\text{mul}}}^{T_{2}}\) and \(\mathbf{n}_{\text{R}_{\text{add}}}^{T_{2}}\) are the full calculation times of real number multiplication and real number addition in the control group (TD-CDE or FD-CDE).
## 2 Description of the Algorithm
### BiLSTM Structure  
The inertial data collected by IMU devices follow a time-series pattern due to the highly repetitive nature of pedestrian pace. [PERSON] et al. [22] considered only CNN networks and ignored the time-series nature of the data. To fully learn the temporal correlation of inertial data features, enhance the long-range learning performance of deep neural networks and achieve more nonlinear transformations, a bidirectional long short-term memory (BiLSTM) network is added after the ResNet18-NAM in our algorithm. BiLSTM is a variant of LSTM [12] network, consisting of forward LSTM and backward LSTM stacking, which not only has the long-range sequence learning capability of LSTM model but also can better explore the backward and forward dependencies of inertial data. To obtain the BiLSTM output \(H_{t}\), the hidden state \(\overrightarrow{h_{t}}\) of the forward LSTM output and the hidden state \(\overleftarrow{h_{t}}\) of the backward LSTM output are connected. \(H_{t}\) is calculated as follows:  
\[H_{t}=W_{\overrightarrow{hf}}\overrightarrow{h_{t}}+W_{\overleftarrow{hf}} \overleftarrow{h_{t}}+b_{h} \tag{4}\]  
Figure 2: Basicblock-NAM structure.  
In Equation (4), \(W_{\overline{hf}}\) is the connection weight matrix from the forward LSTM to the output layer; \(W_{\overline{hf}}\) is the connection weight matrix from the backward LSTM to the output layer; \(b_{h}\) is the bias of the output layer.
## 2 Problem Formulation  
### Demand for Collaborative Information in Multilayer Rail Transit System Integration  
With the rapid development of the rail transit industry, there is a demand for solution and technology on the allocation of multilayer regional capacity resources, optimization of overall transportation organization, collaborative and comprehensive safety assurance, and integrated passenger information services. Therefore, research focusing on single-mode operation has become increasingly inadequate to adapt to the upcoming situation. Here are some specific manifestations. For passenger flow prediction, under multilayer network operations, the spatiotemporal characteristics of multiple level overlapping passenger transport will change, making the existing flow prediction models difficult to apply. For train operation organization, since different modes are interdependent and mutually constrained, existing methods are no longer suitable for the requirements of regional collaborative operations, such as hub transfers, capacity allocation, and dispatching. For resource utilization, the comprehensive utilization and resource sharing of network system facilities will be different from the previous method of single-mode operations; regional rail transit operates different modes independently but connects them through physical hubs, especially when it comes to the coordination and command of the safety assurance system in emergency situations. For passenger information services, it includes more accurate information services, convenient access, and intelligent query retrieval based on natural language interaction, in terms of intelligence, comprehensiveness, flexibility, interactivity, transparency, and cost-effectiveness.  
The realization of collaborative scenarios depends on data resource exchange in multi-layered rail transit systems. In general, information requested can be divided into three forms: basic information, business information, and comprehensive information. Basic information refers to the data, graphics, attributes, and coding resources that can reflect the basic situation of railway transportation. Business information refers to the data and graphics that support the operation of railway business information systems and participates in railway business operation related to business. Comprehensive information refers to the management, control, and comprehensive decision information formed during the operation of a business system due to the requirements of business applications through the information comprehensive analysis and calculation of relevant business models. According to previous research and literature reviews [10], the information requested by main businesses is shown in Figure 1.
## 5 Evaluation Measures
### Miscellaneous
#### 5.4.2 Persuasiveness, Plausibility, and Intention to Use  
This section presents another theme explored in various studies [15; 37; 46; 49; 65; 68; 76]. [PERSON] et al. [15] define persuasiveness as the ability to convince the user to take a specific action. Similarly, [PERSON] describes the persuasive power of an explanation as its capacity to convince the user to follow the provided advice. The authors further emphasize that this definition of persuasive power is independent of the explanation's correctness. For this reason, persuasiveness must be distinguished from transparency and understandability, as the persuasive power of an explanation may lead to over-trust. In such cases, users may believe the system is correct, even when it is not, without fully understanding how it works [49]. This highlights the dual nature of persuasiveness in explainable AI (XAI): it is both a goal and a potential risk. On one hand, if an explanation lacks persuasive power--meaning it does not influence user behavior--it suggests that the explanation is irrelevant and provides no added value. On the other hand, as [PERSON] et al. [46] warn, users should not be misled by persuasive yet inaccurate explanations.  
In our sample, persuasiveness was evaluated using both objective and subjective measures. [PERSON] [49], for instance, measured the construct by using behavioral metrics, assessing how often participants followed the advice given by the AI, regardless of its correctness. Similarly, [PERSON] et al. [68] measured the switch percentage across different conditions of model information. [PERSON] et al. [75] also measured this objectively, evaluating whether users followed the AI system's recommendations.  
Beyond behavioral changes, some studies interpret persuasiveness in terms of changes in belief or attitude. For instance, [PERSON] [37] outlined that \"if the user changes their mind after receiving an explanation, it is persuasive\". From this stance, some studies in our sample employed subjective measures based on self-report surveys. In these cases, persuasiveness was most often evaluated based on how convincing users found the explanation. For example, [PERSON] et al. [58] measured persuasiveness using the item _\"this explanation is convincing\"_. Similarly, [PERSON] questionnaire included items like _\"the explanation is convincing\"_ and _\"the recommendation or result is convincing\"_. [PERSON] et al. [76] adopted a similar operationalization, assessing _\"how convincing AI explanations are to humans\"_. However, in their case, they used this measure to evaluate the plausibility of XAI explanations.