esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 3 Multiview Temporal Alignment
### Private Latent Variables  
A key assumption of our temporal alignment algorithm TRANSIENCE is that the feature vectors of the sequence pair to be aligned share some common information. Although this is indeed our case, because the speech and bio-signals encode the same phone sequence, it is also true that each view may have its own unique characteristics, thus making the reconstruction loss in (8) difficult to optimize when only considering the shared latent variables. Therefore, it may be beneficial to model the unique characteristics of each view as well as the common characteristics shared among all the views. Inspired by the work in [25], we propose the introduction of private latent variables for each view \(\tilde{\mathbf{z}}^{x}\) and \(\tilde{\mathbf{z}}^{y}\) that aim at modeling the uniqueness of each view, as represented in Figure 2. The private variables are predicted from the inputs by a set of independent DNNs \(\tilde{\mathbf{z}}^{x}=\tilde{\mathbf{f}}(\mathbf{x})\) and \(\tilde{\mathbf{z}}^{y}=\tilde{\mathbf{g}}(\mathbf{y})\). These private latent variables are used in (8), in addition to the common, shared variables, for reconstructing the input data, i.e., \(\tilde{\mathbf{z}}=f^{-1}(f(\mathbf{x}),\tilde{\mathbf{f}}(\mathbf{x}))\) and \(\tilde{\mathbf{y}}=g^{-1}(g(\mathbf{y}),\tilde{g}(\mathbf{y}))\).  
When optimizing the weights of DNNs \(\tilde{\mathbf{f}}\) and \(\tilde{\mathbf{g}}\), a standard Gaussian distribution \(\tilde{\mathbf{z}}\sim\mathcal{N}(\mathbf{0},\mathbf{I})\) is chosen as the prior distribution for the private variables of each view. To enforce this distribution, we minimize the Kullback-Leibler (KL) divergence between the priors and the empirical distribution (modeled as a multivariate Gaussian distribution with diagonal covariance) estimated from the private variables as follows,  
\[\mathcal{L}_{KL}=\frac{1}{2}\sum_{i=1}^{d_{z}}\left(\sigma_{i}^{2}+\mu_{i}^{2} -1-\log\sigma_{i}^{2}\right), \tag{10}\]  
where the means and variances in (10) (\(\mu_{i},\sigma_{i}\), respectively) are estimated for each private variable in each minibatch.
## 3 Experimental Results
### Ablation Experiment  
The effect of the proposed model with the improved attention mechanism on the experimental accuracy is shown in Figure 6. It can be seen from the figure that the improved attention mechanism greatly improves the accuracy of the model in the medium and long-term PM\({}_{2.5}\) concentration prediction, the reasons are as follows.  
Firstly, according to the exogenous series correlation method, the data of the most relevant sites are dynamically combined, and the influence of the relevant sites exogenous series on the central site collection is considered. Secondly, the attention mechanism is added to the fully connected layer, the accuracy of PM\({}_{2.5}\) prediction at the central site can be improved by combining the correlation coefficients between neighboring sites and the central site. However, in the short-term prediction of 16 h, the experimental accuracy is improved to a limited extent by the improved attention mechanism. This is because the time step is relatively small and the dynamic combination of data from the most relevant sites increases the redundant data. The comprehensive results verify the validity of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time Methods**} & \multicolumn{2}{c}{**1–6 h**} & \multicolumn{2}{c}{**7–12 h**} & \multicolumn{2}{c}{**13–24 h**} \\ \cline{2-7}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline LSTM & 37.38 & 23.45 & 57.93 & 42.63 & 66.39 & 48.15 \\ CNN-LSTM & 39.37 & 23.87 & 51.37 & 41.93 & 64.03 & 44.82 \\ LSTM-FC & 36.97 & 22.97 & 56.70 & 40.23 & 63.79 & 42.57 \\ DA-RNN & 36.29 & 21.40 & 48.07 & 35.57 & 60.81 & 43.53 \\ DSTP-FC (ours) & **32.51** & **19.50** & **45.22** & **32.22** & **51.45** & **37.04** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the performance of different models.  
Figure 4: The prediction results of PM\({}_{2,5}\) concentration at the central site from 1 November 2014 to 30 November 2014 based on the proposed DSTP-FC model.
# Hybrid Ring- and Tree-Topology RoF Transmission System with Disconnection Protection
## 4 Conclusions  
A hybrid ring- and tree-topology RoF transmission system with self-disconnection protection was proposed, to integrate TDM and WDM techniques and increase the number of supportable BS in a signal-network architecture. The quality of service still can be ensured when the main ring-fiber link of the transmission system is interrupted for any reason by utilizing new-generation SBOADMs properly to bridge signal transmissions in the proposed architecture. The system operator can recover the broken connections quickly by transforming the operation state of an SW in the CO to allow the downlink signals to be transmitted in the CW and CCW directions of the main ring-fiber link at the same time. The employed SBOADMs with two-way transmission characteristics will drop down the desired RoF signals automatically into the connected BS-Group. The uplink optical signal emitted from each BS can also be transmitted back to the CO end along the opposite direction of its downlink signal-routing path. In this way, the interference of the fiber breakpoint can be avoided immediately. Accordingly, the entire transmission system can be reconnected and the quality of network services will be further ensured. Our experimental results showed that the proposed hybrid ring- and tree-topology RoF transmission system can provide similar transmission performances in fiber breakpoint scenarios and in normal circumstances.  
Figure 11: The optical spectra of uplink signals are measured at the CO end when a breakpoint occurs.  
Figure 12: (**a**) Downlink RoF signal error rate performance measured in BS-Group1, BS-Group2, BS-Group3, BS-Group4, and Back-to-Back when a breakpoint occurs. (**b**) Uplink RoF signal error rate of BS-Group1, BS-Group2, BS-Group3, BS-Group4, and Back-to-Back measured at the CO end when a breakpoint occurs.  
**Author Contributions:** [PERSON]-[PERSON] analyzed the data and started the manuscript. [PERSON]-[PERSON] conceptualized and supervised the research work and improved the manuscript for submission and publication. [PERSON]-[PERSON] performed the experimental work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology of Taiwan (Grant No.: MOST 107-2221-E-415-009-, MOST 108-2221-E-415-023-, MOST 109-2636-E-305-003-, and MOST 110-2221-E-415-009-) and the University System of Taipei Joint Research Program under USTP-NTUT-NTPU-110-01.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this paper.
## 3 Theory Background
### Causal Graphs  
Causal graphs \(G=(V,E)\), also known as directed acyclic graphs (DAGs), represent direct and non-cyclical relationships among system variables. The graph is composed of a node set \(V\), and an edge set (\(E\)). Each node represents a variable inherent to the underlying phenomenon and a direct edge \(X\to Y\) denotes a causal effect of \(X\) on \(Y\). The absence of a link between two variables may be interpreted as the absence of a direct causal effect with respect to the other variables in the graph. Any variable is a cause of its descendants. The causal Markov assumption supports this interpretation, and a variable, when conditioned on its direct causes (parents), is independent of any other variable for which it is not a cause (non-descendants).  
The concept of conditional independence is related to dependency-separation (d-separation), which involves blocking all paths (back-door criterion) between two variables (e.g., from \(X\) to \(Y\)) through a set of nodes \(S\), written \(X\bot\bot\bot\ Y\ |\ S\). This conditional independence provides ample information to determine if \(S\) is a sufficient set of variables to adjust in order to infer the causal relationship between \(X\) and \(Y\) by applying a particular causal inference method.  
**Definition 1**.: **Back-door criterion:** _given a cause-effect pair (\(X\), \(Y\)), a set of variables \(S\) satisfies the back-door criterion of (\(X\), \(Y\)) if and only if conditioning on \(S\) blocks all paths, except for the direct path, which ends with an arrow pointing to \(X\). In Figure 1a, in order to infer the causal effect of \(X\to Y\), it is necessary to block the back-door path \(X\to Z\to Y\) by adjusting for \(Z\), or the results will be biased. Therefore, \(Z\) can be referred to as a confounder._  
**Definition 2**.: **Confounder:** _given a cause-effect pair (\(X\), \(Y\)), a variable \(Z\
otin X\), \(Y\) is a confounder if it plays a central role in a fork and is on a back-door path of (\(X\), \(Y\))._  
According to Figure 1b, it is not possible to block the back-door path \(X\to U\to Y\) since the confounder \(U\) is unobserved. Specifically, the computed effect of \(X\to Y\) will be biased due to the unobserved effect of \(U\), requiring a specific causal identification to address the confounder effects of \(U\). In Figure 1c, both \(X\) and \(Y\) cause \(C\), and there is no causal effect or association between \(X\) and \(Y\). However, adjusting for \(Z\) incorrectly will introduce bias since a non-existent association between \(X\) will be inferred. Therefore, \(C\) is a collider and cannot be on back-door paths even when it is a confounder.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 3 Results
### Density Calculation Results  
Table 4 shows the descriptive density statistics of the four ship types and of all ships. As mentioned above, the temporal span of the analysis was one month. The density value in each cell is the total occupancy time (in hours) of the ships in that cell during one month. As four months of data were analyzed for each ship type, the density can be interpreted as the total occupancy time of the ships in a given cell over a four-month span. The results of this analysis method are easily interpreted because the density value has a temporal meaning. Moreover, the average instantaneous number of ships per square kilometer can be obtained by dividing the cell density value by the total timespan of the dataset (in this case, four months, equal to 2928 h). However, as this study targeted only trajectories with speeds of >3 kts, an instantaneous value can be defined as the average number of ships sailing at a certain speed rather than all ships in the actual environment.  
Among the total number of cells constructed within the area of interest (323,691 cells), \(N\) cells were used for the density calculation of a given ship type. The grid number \(N\) was smallest for passenger ships, which are known to closely adhere to specified repeated routes. Towing ships, with the second-smallest \(N\), typically sail close to the coast rather than on the open sea. The largest numbers of cells were occupied by cargo ships and tankers whose voyages vary widely. The average ship-occupancy times per cell during the four-month span, obtained as the means of the density calculation results, were 2.4 h for cargo ships, 1.1 h for tankers, 0.3 h for passenger ships, and 0.1 h for towing ships.
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 3 Outline of New CCF Life Prediction Model  
### Nonlinear Fatigue Damage Considering Load Interactions  
The evolution curve of fatigue damage under CCF loading, considering load interactions, was modified based on Miner's rule and nonlinear fatigue damage. The load blocks \(N_{\text{B}}\) were initially predicted using Miner's rule and Equation (2). The HCF damage \(D_{H}\) and LCF damage \(D_{L}\) are superimposed to derive cumulative damage, calculated using Equation (3) [36].  
LCF damage \(D_{L}\) is expressed as  
\[D_{L}=\left(\frac{N_{\text{B}}}{N_{L}}\right) \tag{11}\]  
HCF damage \(D_{H}\) is represented as  
\[D_{H}=\left(\frac{nN_{\text{B}}}{N_{H}}\right) \tag{12}\]  
The fatigue damage evolution curve was modified to account for the nonlinear damage buildup of load interaction, as illustrated in Figure 5. The fatigue damage evolution initially follows the exponential curve from point \(0\) to P, transitions to the straight line PP\({}_{1}\)P\({}_{2}\), and ends with the curve P\({}_{2}\)Q to \(Q\).  
Figure 5: Accumulated damage under CCF load.  
The cumulative fatigue damage under LCF load at point P is identical to the equivalent converted cumulative fatigue damage under HCF load at point P1, as per the damage curve approach and equivalent damage theory [17]. In the meantime, the equivalent converted cumulative fatigue damage can also be used to define the cumulative fatigue damage under HCF loading at point P1, as demonstrated by the following formulations in Equations (13) and (14).  
\[D\bigg{(}\frac{1}{N_{L}}\bigg{)}=D\bigg{(}\frac{n\prime}{N_{H}}\bigg{)} \tag{13}\]  
\[D\bigg{(}\frac{n}{N_{H}}\bigg{)}=D\bigg{(}1-\frac{n^{\prime}}{N_{H}}\bigg{)} \tag{14}\]  
Based on the idea outlined in Section 2.3 above, Equation (14) is changed to Equation (15) to account for the impact of the interaction between loads:  
\[D\bigg{(}\frac{n}{N_{H}}\bigg{)}=\bigg{[}D\bigg{(}1-\frac{n^{\prime\prime}}{N _{H}}\bigg{)}\bigg{]}^{\frac{\sigma_{L}}{\sigma_{L}}} \tag{15}\]  
By combining Equations (13)-(15), Equation (16) displays the equivalent cyclic ratio of the variable cyclic load at the secondary load:  
\[\frac{n^{\prime\prime}}{N_{H}}=1-\left(1-\left(\left(1-\left(1-\left(\frac{1 }{N_{L}}\right)^{B_{L}}\right)^{A_{L}}\right)^{\frac{\sigma_{L}}{\sigma_{H}}} \right)^{\frac{1}{\Lambda_{H}}}\right)^{\frac{1}{\Lambda_{H}}} \tag{16}\]  
Therefore, the following is an expression for the cumulative damage \(D_{c}\) under CCF loading:  
\[D_{c}=\frac{n^{\prime\prime}+n}{N_{H}} \tag{17}\]  
where \(D_{c}\) represents the cumulative damage under CCF loading.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 4 Simulation Processes
The strength of coupling between SRR unit cells is dependent on the separation between them and the orientation of each unit cell [59]. In this proposed work, there is high potential existence of mutual coupling since the unit cells are closely spaced by 5.18 mm. In addition, superdirectivity could be achieved due to the proximity of the unit cells from each other [51; 61]. However, the focus of this paper is to design a frequency reconfigurable metasurface antenna without mitigating the mutual coupling effect between the unit cells. Mutual coupling effects and superdirectivity will be investigated in future works.
## Materials and Methods  
### The Model of PolSAR Data  
Assuming that a \(p\)-dimensional random complex vector \(K=\left[k_{1},k_{2},\ldots,k_{p}\right]^{T}\) follows a complex multivariate normal distribution with mean 0 and dispersion matrix \(\Sigma_{K}=E\big{[}\big{\langle}KK^{*T}\big{\rangle}\big{]}\), the \(p\times p\) matrix \(\Sigma\) is a Hermitian positive definite random matrix and follows a Wishart distribution. A PolSAR system measures the amplitude and phase of the backscattered signals in four combinations of linear receive and transmit polarizations: horizontal-horizontal (_hh_), horizontal-vertical (_lv_), vertical-horizontal (_vh_), and vertical-vertical (_vv_) [5; 26]. Assuming that the target reciprocity condition is satisfied [22], the polarimetric information can be expressed by a complex vector:  
\[\Omega=\left[S_{hh},\sqrt{2}S_{hv},S_{vv}\right]^{T} \tag{1}\]  
where \(h\) and \(v\) denote the horizontal and vertical wave polarization states, \(T\) indicates the vector transposition, and \(S_{hv}\) is the scattering element of the horizontal transmitting and vertical receiving polarizations.  
For multi-look processed PolSAR data, the backscattered signal can be expressed as a covariance matrix:  
\[C=\Big{\langle}\Omega\cdot\Omega^{*T}\Big{\rangle}=\Bigg{\langle}\left[\begin{array} []{cc}\left|S_{hh}\right|^{2}&S_{hh}S_{hv}^{*}&S_{hh}S_{vv}^{*}\\ S_{hv}S_{hh}^{*}&\left|S_{hv}\right|^{2}&S_{hv}S_{vv}^{*}\\ S_{vv}S_{hh}^{*}&S_{vv}S_{hv}^{*}&\left|S_{vv}\right|^{2}\end{array}\right]\Bigg{\rangle} \tag{2}\]  
The covariance matrix \(C\) can be modeled by a complex Wishart distribution. The frequency function of covariance matrix \(C\) can be shown as follows:  
\[\begin{split} f(C)=\frac{1}{\Gamma_{p}(n)}\frac{1}{|\Sigma_{ \Omega}|^{n}}|C|^{n-p}\exp\Big{\{}-tr[\Sigma_{\Omega}^{-1}C]\Big{\}}\\ \Gamma_{p}(n)=\pi^{p(p-1)/2}\prod_{j=1}^{p}\Gamma(n-j+1)\end{split} \tag{3}\]  
where \(tr(\cdot)\) is the trace operator, \(n\) is the number of looks, \(\Gamma_{p}(n)\) is a normalization factor, and \(\Sigma_{\Omega}=E\Big{[}\Big{\langle}\Omega\cdot\Omega^{*T}\Big{\rangle} \Big{]}\).
## 2 Problem Statement
### QoS Criterion of SCOS  
QoS is a critical non-functional attribute for evaluating the capability of software services and is widely applied in SCOS to assess whether a service meets the user's performance expectations. QoS not only enables users to select software according to their individual preferences but also provides a theoretical foundation for service evaluation. At present, a relatively systematic evaluation framework for QoS in SCOS has been established. In this study, four of the most commonly used QoS attributes are selected for modeling and optimization: Time (\(T\)), Cost (\(C\)), Reputation (\(Re\)), and Quality (\(Q\)) [18]. The aggregation of QoS attributes is influenced by the structure of the service composition. Common service structures include sequential, parallel, conditional, and loop structures, and existing research has extensively discussed the transformation methods among these structures [19]. To simplify the model and achieve accurate representation, this study focuses exclusively on sequential structures for modeling and QoS computation. The comprehensive evaluation function of QoS in SCOS is shown in Equation (1).  
\[QoS(CMS)=\omega_{T}\times Q_{T}+\omega_{C}\times Q_{C}+\omega_{Re}\times Q_{Re }+\omega_{Q}\times Q_{Q} \tag{1}\]  
where \(\omega_{T}\), \(\omega_{C}\), \(\omega_{Re}\), \(\omega_{Q}\) represent the weight coefficients of time, cost, reputation, and quality, respectively, \(\omega\in[0,1]\) and \(\omega_{T}+\omega_{C}+\omega_{Re}+\omega_{Q}=1\). Table 1 presents the aggregation functions for the four QoS attributes.  
Figure 1: Schematic diagram of SCOS.  
Due to the significant differences in dimensions, value ranges, and evaluation criteria among various QoS attributes, normalization of the original QoS data is required to facilitate unified modeling and comparison in subsequent stages. In this context, \"Positive\" refers to attributes where higher values indicate better performance, while \"Negative\" refers to attributes where lower values are preferred. Therefore, QoS attributes can be classified into beneficial and non-beneficial types [20]. These attributes are normalized using Equation (2).  
\[q_{k}=\begin{cases}\frac{x_{\max}-x_{\min}}{x_{\max}-x_{\min}},&x_{k}\text{ is a negative attribute}\\ \frac{x_{\text{l}}-x_{\min}}{x_{\max}-x_{\min}},&x_{k}\text{ is a positive attribute}\\ \end{cases} \tag{2}\]  
where \(x_{\max}\) and \(x_{\min}\) denote the maximum and minimum values of the corresponding QoS attribute, respectively. In addition, when \(x_{\max}=x_{\min}\), \(q_{k}=1\). After normalization, all QoS attribute values are scaled to the [0, 1] interval, which facilitates the trade-off and integration of multiple attributes within multi-objective optimization algorithms.
## 6 Simulation Results
### Performance Analysis
Next, as shown in Figure 6, average episode rewards, average observation rate \(\alpha\) and average exploration rate \(\beta\) versus sensing range \(R_{s}\) under different communication conditions are tested. The results with communication are depicted by blue lines, denoted as \"Com\", while the results without communication are represented by red lines, denoted as \"NoCom\". The testing environment contains 5 UAVs and 7 targets in an area of 20 \(\times\) 20 cells. Each data point is an average of 10 tests. It can be found that with the increase in sensing range \(R_{s}\), average episode rewards, average observation rate \(\alpha\) and average exploration rate \(\beta\) all increase. Larger sensing range enables multi-UAV to detect larger area and acquire more information, leading to better performance. Compared with the result without communication, we can see that the performance of multi-UAV system is better when UAVs communicate with each other. On one hand, UAVs can achieve better understanding of the environment through information sharing and fusion. On the other hand, UAVs can achieve better cooperation via communication.
## 7 Evaluation
### Methodology  
To conduct a comprehensive evaluation of SKATEBOARD's effectiveness in managing, retrieving, and visualising the WLKG, we established a structured setup:  
* Environment configuration: We set up a dedicated evaluation environment with access to the WLKG endpoint powered by SKATEBOARD.
* User profiles: We recruited a diverse group of users, including literature enthusiasts, researchers, and academics, to represent various perspectives and use cases.
* Evaluation metrics: We defined Key Performance Indicators (KPIs) to measure the system's efficiency, accuracy, and user satisfaction during information retrieval and visualisation tasks.
* Test scenarios: We designed test scenarios that reflect real-world usage, including querying for authors, exploring literary works, and examining historical literary trends.
* Data collection: We collected quantitative data, such as response times and query success rates, and qualitative data through user feedback and observations.  
Informed by the existing body of literature on the evaluation of search tools [40], we identified multiple measures falling into two distinct categories:  
* **Subjective self-reported measures by users:** These encompass both quantitative responses on Likert scales and qualitative responses to open-ended questions provided by the users.
* **Objective measures:** These include data such as the user interface event logs, task completion times, and the specific search terms employed.  
In structuring the questionnaire and activities for user participation during the evaluation, we adopted a revised framework proposed by [PERSON] [41]. We identify four key segments:  
* **Demographic information:** Gathering insights into user demographics, including gender, age, and educational background.
* **Pre-task assessment:** Exploring users' existing knowledge or familiarity with the subject matter or the system under examination.
* **Post-task evaluation:** Focusing on the user's experience during specific tasks, emphasising efficiency, effectiveness, and overall satisfaction in gauging system usability.
* **Post-system assessment:** Encompassing the user's overarching experience and impressions when interacting with the information system.