esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Ensemble Empirical Mode Decomposition (EEMD)  
Ensemble empirical mode decomposition (EEMD) was first presented in 2009 by [PERSON] and [PERSON] (2009) based on empirical mode decomposition (EMD), which was proposed in 1998 by [PERSON] et al. EEMD is effective in extracting signals of different scales from data and can solve mode mixing well. By adding white noise, it can adaptively decompose the signal into a series of intrinsic mode functions (IMF) ranging from high to low frequency. Moreover, it should be noted that due to the uniform distribution of the white noise spectrum, the average value of the white noise is zero. That is why the added noise will be offset after multiple averaging, and the integration mean value can be taken as the final result. The specific steps are as follows:  
1. Add a group of white noise \(\omega(t)\) to the original signal x(t) to get a new signal sequence X(t): \[\mathrm{X(t)}=\mathrm{x(t)}+\omega(t)\]2. Decompose \(X(t)\) to obtain a set of \(imf_{j}(t)\) components: \[X(t)\ =\ \sum_{j=1}^{n-1}imf_{j}(t)+r(t)\] where \(imf_{j}(t)\) represents the \(j_{th}\) IMF component (the same below), \(r(t)\) represents the trend term.
3. Repeat step (1) (2) (m) times, add different white noise each time, and get a new set of IMF component: \[X_{i}(t)\ =\ \sum_{j=1}^{n-1}imf_{j}(t)\ +\ r_{i}(t)\]
4. Take the mean value of each IMF components as the final result: \[imf_{n}(t)\ =\ \frac{1}{m}\sum_{i=1}^{m}imf_{ni}(t)\] \[r(t)\ =\ \frac{1}{m}\sum_{i=1}^{m}r_{i}(t)\]  
In this paper, the EEMD algorithm is used to decompose the original PM\({}_{2,5}\) time series to obtain eight IMFs and a trend term. The amplitude of the white noise added to the original sequence is 0.2 (i.e., 0.2 times the standard deviation of the original sequence), and the number of sets is 200 (i.e., 200 repetitions in the calculation process, taking the mean value). In addition, considering that the model training should not contain future information, EEMD is made for each training set and the whole data set.
## 5 Performance Analysis
### Decryption Time  
We considered a different number of attributes and authorities to measure the decryption time, as shown in Figure 4.  
* Figure 4 shows the decryption time according to the number of attributes while considering multiple concurrent access requests to the same authority. The results show that: **i.** The increase in the average decryption time is about 0.0044 seconds when increasing the number of attributes from 4 to 32 while considering 300 concurrent customers; and **ii.** The increase in the average decryption time is about 0.0023 seconds when increasing the number of concurrent customers from 100 to 300 while considering 32 authorities.
* Figure 4 indicates the decryption time according to the number of authorities while considering multiple concurrent access requests to the same authority. The results show that: **i.** The increase in the average decryption time is about 0.0043 seconds when increasing the number of authorities from 4 to 32 while considering 300 concurrent customers; and **ii.** The increase in the average decryption time is about 0.0029 seconds when increasing the number of concurrent customers from 100 to 300 while considering 32 authorities.  
Finally, it is clear that the decryption time is reasonable considering the huge stream of concurrent requests to the same authority. The average decryption time is 0.01 seconds when considering the very tough conditions and huge traffic overhead. Thus, it is accepted by the users.
## 4 Experiment and Results
### Angular Resolution  
The angular deflection of the signal laser beam in the experimental system was introduced by the FSM. Therefore, it was necessary to ensure that the angular deflection of the FSM matched the final angular deflection measured by the experimental system. Prior to the construction of the experimental system, it was imperative to calibrate the angular deflection of the FSM. The calibration process is delineated in Figure 6b.  
In this experiment, the distance between the collimator and the FSM was represented by \(L_{1}\), which was 50 mm, and the distance between the FSM and the screen was represented by \(L_{2}\), which was 1930 mm. At this time, the laser beam was incident vertically with \(L_{2}>>L_{1}\). The driving voltage of the FSM was adjusted with \(\Delta V\), and the amount of movement of the center of mass of the spot in the screen was recorded with \(\Delta x\). The angular offset \(\Delta\theta\) at this time can be calculated with \(L_{2}\). To obtain more accurate calibration results, a different \(L_{2}\) was chosen to perform multiple sets of tests.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline Wavelength & 1550 nm \\ \hline Local Oscillation laser beam power & \(-\)7 dBm \\ \hline Collimator diameter & 2.1 mm \\ \hline Collimator divergence angle & 500 \(\upmu\)rad \\ \hline QD diameter & 3 mm \\ \hline QD bandwidth & 20 MHz \\ \hline \end{tabular}
\end{table}
Table 1: Key parameters of the desktop experimental system for the heterodyne coherent angle measurements.  
Following the calibration of the FSM azimuth and pitch axis angular deflections, the FSM was connected to the experimental system for the angular resolution test. The test results are shown in Figure 8. The FSM within the experimental system was actuated by piezoelectric ceramics, and the driving voltage adjustment step was set to \(\Delta V=0.2~{}V\). Due to the configuration of the optical path, the angular deflection of the FSM in the azimuth axis will produce twice the angular deflection of the optical axis, while the angular deflection in the pitch axis will produce the square root of two times the angular deflection of the optical axis. Consequently, the angular deflection measurements of the azimuth and pitch axes were estimated to follow a square root relationship [19]. The experimental results demonstrate that the experimental system for heterodyne coherent angle measurement built in this paper can accurately measure the known deflection angle of FSM.
# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
## 3 Research Methods
### TCN-BiGRU-Attention  
The strategy adopted in this study is to combine the unique strengths of three algorithms to solve the problem: the attention mechanism for selecting key features, TCN for feature extraction, and BiGRU for capturing long-term dependencies and acquiring future information. Therefore, this model is named TCN-BiGRU-Attention.  
Within the context of Figure 5, the input to the model is the dataset \(X\), post data preprocessing, which is structured as \(N_{sample}\), \(N_{timsteps}\), \(N_{features}\). Employing the attention mechanism, the characteristic vector \(X_{i}\) for each category within \(X\) is allocated distinct weights (i = 1,2,..., m) during the initial phase. In the subsequent phase, feature extraction is executed on \(T_{j}=(\alpha_{1}x_{1,t},\alpha_{2}x_{2,t},\ldots,\alpha_{m}x_{m,t_{j}})(j=1,2,\ldots,n)\) via a Temporal Convolutional Network (TCN), which is comprised of a pair of residual blocks. The initial residual block is equipped with two dilated causal convolutional layers, distinguished by a kernel size of 4, a dilation rate of 1, and a filter count of 7. Upon advancing to the subsequent residual block, the dilation rate is incremented to 2, while the kernel dimensions and the number of filters remain consistent with the preceding block.  
Figure 4: The framework of BiGRU.  
During the third phase, the output vector \(T_{j}^{\prime}\) from the Temporal Convolutional Network (TCN) serves as the input to the Bidirectional Gated Recurrent Unit (BiGRU) network. This methodology enables the capture of both the long-term dependencies inherent to the time series data and the dynamic interplay between prospective and present information. Subsequently, the output \(h_{t64}\), which signifies the culminating hidden state emanating from the second tier of the Bidirectional Gated Recurrent Unit (BiGRU), is employed as the input to the densely connected layer. Ultimately, it is this fully connected layer that ascertains the intent category label with the maximal likelihood, employing the stipulated formula \(y=soft\text{max}(\omega h_{t64}+b)\). Within the aforementioned equation, \(y\) denotes the categorical label, \(\omega\) represents the matrix of weights that are subject to determination through learning, and \(b\) signifies the bias vector.  
In this model, the TCN component consists of two residual blocks. Each residual block contains two 1D convolutional layers, dropout, and residual connections, with each convolutional layer having 128 filters. The BiGRU section includes two GRU layers, named GRU1 and GRU, and uses a FlipLayer to reverse the input data. The self-attention mechanism employs a single-head attention mechanism, with a key dimension of 50. All the materials can be founded in Supplementary Materials.
## 2 Experimental Details
### The Experiment Processes and Measurements  
A modified variable-speed polishing and dressing machine was used for the grinding and lapping experiments, as shown in Figure 7. During grinding and lapping, a power head applied a specific load (10-80 N) to a SiC wafer with a diameter of 100 mm and a thickness of 0.5 mm, which rotated at 80 rpm. A constant flow of slurry at 150 mL/h was used; the load applied to the plate was about 30 N, and the table disk rotated at 80 rpm. During conditioning, the diamond pad conditioner was attached to a moving head. The oscillation speed of the conditioner and the applied load was fixed. After the SiC substrate polishing process was completed, its surface roughness value was measured using a Zygo NewView 8300 interferometer (AMETEK, Inc. in UK) and white light (WLI) by a noncontact 3D optical method with a root-mean-square (RMS) repeatability of 0.01 nm and a measuring range of more than 20 mm. The measurement area of each image was 86 \(\times\) 86 \(\upmu\)m. The surface roughness value for each workpiece was taken as the average of nine measurements. The thickness of the SiC samples was measured before and after the polishing process, and the accuracy was 0.1 \(\upmu\)m.  
Figure 6: (**a**) Self-made lapping plate; (**b**) traditional copper plate.  
Figure 7: (**a**) The polishing system; (**b**) the polishing pad; (**c**) SEM image of the pad surface (**top**) and diamond dresser (**bottom**).
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### High Frequency (HF) Section
#### 2.3.4 ASK Backscatter Modulator  
The implemented ASK backscatter modulator is depicted in Figure 9. It is made up by two NMOS transistors \(m_{3}\); both transistors act as an open-close switches. Based on Equation (34), during the open state the drain-source resistance must accomplish \(R_{DS}\geq 20R_{rec}\) in order to enable a maximum power transfer into the differential rectifier, and no power is reflected into the antenna. During the close state, the drain-source resistance must accomplish \(R_{DS}=0.5R_{rec}\) to unmatch the differential rectifier with the antenna by a 0.5x factor, allowing a reflected power into the antenna. The frequency of the reflected and non-reflected power into the antenna contains the sensed temperature. This frequency creates two lateral sidebands located at \(f_{ask}\pm f_{osc}\).  
Figure 10: Layout of the implemented dipole antenna.
# A New Approach to the Allocation of Multidimensional Resources in Production Processes
## 4 Model of the Allocation and Control of Multidimensional Resources
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \hline \multicolumn{2}{c}{Indices} \\ \hline W & Set of cells/workstations \\ \hline M & Set of multidimensional resources (tools, software, hardware, employees) \\ \hline P & Set of orders/projects \\ \hline O & Set of operations/tasks \\ \hline F & Set of multidimensional resource functionalities/properties \\ \hline U & Set of unavailability states of multidimensional resources and/or \\  & cells/workstations \\ \hline m & Multidimensional resource index (\(\mathrm{m}\in\mathrm{M}\)) \\ \hline P & Order/project index (\(\mathrm{p}\in\mathrm{P}\)) \\ \hline w & Cell/workstation index (\(\mathrm{w}\in\mathrm{W}\)) \\ \hline o & Operation/task index (\(\mathrm{o}\in\mathrm{O}\)) \\ \hline f & Index of multidimensional resource functionalities/properties (\(\mathrm{f}\in\mathrm{F}\)) \\ \hline u & Index of unavailability state (\(\mathrm{u}\in\mathrm{U}\)) \\ \hline \multicolumn{2}{c}{Parameters} \\ \hline AR\({}_{\mathrm{m}}\) & Number of available resources m \\ \hline RC\({}_{\mathrm{m}}\) & Cost of using the resource m per time unit \\ \hline CT\({}_{\mathrm{o}}\) & Time to complete the task o \\ \hline SA\({}_{\mathrm{w}}\) & How many tasks can be assigned to the cells/workstations w at the same time \\ \hline CR\({}_{\mathrm{w}}\) & cell/workstation w work cost per unit of time \\ \hline VT\({}_{\mathrm{P}}\) & Penalty for failure to complete the order t \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model indexes and parameters. The questions are modeled as subsets of the model constraints (Table 4). Depending on the question being considered, the model may take the form of BILP (Binary Integer Linear Programming) or CSP (Constrain Satisfaction Problem) [20].
## 5 FGI Challenges and NGN Solutions for Future Networks
### Converged Architecture for Future Networks  
There is not a standardized Future Network architecture, so we can just guess what it will look like and how much it follows concepts from NGN or FGI architectures. We can recognize several trends that result in a potential Future Network architecture as presented in Figure 4.  
First of all, the architecture will follow a vertical layered model, similar to NGN, and probably there will be some inter-layer interfaces and hooks to existing architectures (cross-layer supporting functions/interfaces). We can definitely expect integration of new types of next generation access  
Figure 4: Principle for layered architecture of Future Network [15].  
networks like 4G, LTE (Long Term Evolution), Next Generation Optical Access but also those which deployed already like FTTx (Fiber to the X), UMTS (Universal Mobile Telecommunications System), and WiMax. We can expect the role of home networking and interconnection of things (or as industry calls it Internet of Things) will grow, as well as Fixed Mobile Convergence aspects. On the top of a new IP layer (if the clean-slate approach redefines the IP protocol, which is still not decided), there will most probably be some kind of virtualization or federation layer which will enable service control that uses any network resources (network capacity, storage, computing power, _etc._) and that provides additional network/platform capabilities like mobility, security, QoS handling as abstraction layer to service control and application layers. Here we can see potential to reuse from NGN transport control (RACS--(Resource and Admission Control Subsystem, NASS--Network attachment Subsystem) but also new 3 GPP concepts for SAE (System Architecture Evolution) and EPC (Evolved Packet Core) may already be an example of how this will evolve. Separated service control may be handled by IMS for services that need to be controlled/managed, and SIP could be used as control protocol. But there will be a huge group of applications which could be leveraged from network capabilities without using IMS. For example instead of best-effort services (like web, internet streaming, voice over internet), Future Network can provide those applications' QoS and other features. We may easily predict that most expectations are for new innovative services and applications, most of which we cannot even imagine today, but most probably related to extensions future media and content delivery, entertainment, communities, e-services, others or absolutely new designed applications.
# On the Feasibility of Using Industrial Urea to Mitigate Thermal and Shrinkage Cracking in Concrete
## 4 Results and Discussion
### Simplify Hydration Heat Development  
As a simple method to assess the heat of concrete relatively, the hydration heat history of the center was recorded with the time after producing a concrete specimen in the insulated mold. This experiment provides a relative comparison of the temperature at initial placing, peak temperature, and the time required to reach the peak temperature, as shown in Figure 8.  
Figure 6: Dry shrinkage strain of the concrete mixture.  
Figure 7: Flow out of urea after hardening of concrete.  
The urea mixture proportion was confirmed to strongly influence three factors. Compared to OPC, with an increase in the urea mixture proportion, the time required to reach the peak temperature was delayed by \(\sim\)8 h, 16.5 h, and 28 h for U5, U10, and both U15V and U15W, respectively. Furthermore, the peak temperature caused by the hydration heat decreased by 1.3 \({}^{\circ}\)C (U5), 4.2 \({}^{\circ}\)C (U10), 7.5 \({}^{\circ}\)C (U15V), and 8.1 \({}^{\circ}\)C (U15W) compared with that of OPC. Concrete mixed with urea showed a lower peak temperature because the temperature immediately after mixing was very low even though the subsequent temperature increase was generally similar. This suggests that the added urea keeps the temperature low constantly even after the rapid heat absorption reaction occurs upon contact with water. In conclusion, urea addition reduced the temperature of concrete immediately after placing, thereby proportionally reducing the peak temperature. Furthermore, it did not impact the hydration development of cement. SCM and LHC showed reduced temperatures corresponding to moderate levels of U10 and U15 in terms of factors such as the peak temperature and the time required to reach the peak temperature.
## 6 Open Challenges, Future Prospects and Research Directions
### FSO Scalability and Reliability for 5G and Beyond Networks
#### 6.1.1 Network Architecture  
The architecture of a network determines its ability to scale and adapt to new demands. In the realm of FSO systems, several architectural considerations are crucial. FSO systems can complement existing RF networks by providing high-capacity backhaul links. Hybrid architectures that seamlessly integrate FSO with RF can offer improved redundancy, resilience, and bandwidth. Also, implementing mesh network topologies with multiple FSO nodes can enhance coverage and provide alternative pathways for data transmission, improving reliability and scalability.  
Distributed control mechanisms can enhance the scalability of FSO networks by enabling more efficient resource management and reducing the load on central controllers. Also, integrating edge computing capabilities with FSO nodes can reduce latency and improve the performance of latency-sensitive applications, which is critical for 5G and beyond [478, 493].
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level 2 SVMs (L-2): Features and Optimal Weight Calculation
#### 4.2.3 Weight Calculation  
The weight parameters for L-2 SVMs that utilise the objective function in (6) are obtained by following a similar approach as defined in Section 4.1.3. In this case, a range of weights varying from 1:5 (\(W_{s}\):\(W_{ms}\)) and 5:1 (\(W_{ns}\):\(W_{s}\)), with 0.5 step size difference at each iteration, are tested during run-time. Level 2 SVM models are used to classify CUs either as \"non-split\" or \"split\". As only one SVM is used at the second level, it is not possible to address only one case--either _split_ or _non-split_. Therefore, we calculate the F-score for the cross-validation set to determine the best weight parameters. Here, training and cross-validation sets are derived following the same method explained in the first level (Section 3.1). The F-score calculation is given by  
\[F_{\text{score}}=\frac{2\times(\chi*\omega)}{(\chi+\omega)}, \tag{11}\]  
where \(\chi\) and \(\omega\) are precision and recall, respectively. Here, recall \(\omega\) is calculated using  
\[\omega=\frac{T_{p}}{T_{p}+F_{n}}. \tag{12}\]  
The pair of weights that gives the highest possible F-score is chosen for the SVM at the corresponding depth level. The kernel function used in the SVMs is RBF and similar to the hyper-parameters in L-1, although the weights are fine-tuned to minimise the adverse impact on the time gains by the model training during the encoding process. The cost and gamma parameters are the same as those in L-1.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.5 SMO and Non-RT RIC  
The telecom industry widely considers the service-based architectural in network implementation to give flexibility and future-proof solutions. In addition, the choice of components that produce and/or consume certain services to the deployment allows multi-vendor interoperability through the definition of standardized services and service interfaces. It is important to have the perspective of two Non-RT RIC architecture views;  
Figure 6: Near-RT RIC functional architecture including the Near-RT RIC API component for the xApps.  
the first one is the \"Functional\" view in Figure 7, showing the internal SMO framework and the three categorical components: rApps, Non-RT RIC framework and the open APIs for the rApps, while the \"Service-based\" view allows wide range of flexibility for deployment and is future-proof, the main principles for this architecture illustrated in Figure 8 are modularity, extensibility, functional abstraction, discoverability, composability, reusability, and loose coupling.