esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Main Findings and Comparison with Previous Studies  
This is the first study to investigate the relationship between SR and SA while controlling the running speed. A limited amount of research has investigated swing-phase kinematics [5; 6; 7; 8]. This research, therefore, intended to improve the current understanding of SA in relation to SR, which has not previously been explored. This study found that there is not a significant correlation between SA and SR, and SR are correlated with SH. Implementing gait re-training strategies with recreational runners \begin{table}
\begin{tabular}{|c c c c c c c|} \hline \hline  & & **Step Rate** & **Speed** & \begin{tabular}{c} **Right Step** \\ **Length** \\ \end{tabular} & \
\begin{tabular}{c} **Right Step** \\ **Height** \\ \end{tabular} &
\begin{tabular}{c} **Right Step** \\ **Angle** \\ \end{tabular} \\ \hline \hline \multirow{3}{*}{Step rate (step/min)} & Pearson & \multirow{3}{*}{1} & \multirow{3}{*}{0.257 *} & \multirow{3}{*}{\(-\)0.102} & \multirow{3}{*}{0.040} & \multirow{3}{*}{0.160} \\  & Correlation & & & & & \\ \cline{2-5}  & Sig. (2-tailed) & & & & & \\ \hline \multirow{3}{*}{Speed (m)} & Pearson & \multirow{3}{*}{0.257 *} & \multirow{3}{*}{1} & \multirow{3}{*}{0.883 **} & \multirow{3}{*}{0.565 **} & \multirow{3}{*}{\(-\)0.103} \\  & Correlation & & & & & \\ \cline{2-5}  & Sig. (2-tailed) & & & & & \\ \hline \multirow{3}{*}{Right step length (m)} & Pearson & \multirow{3}{*}{0.160} & \multirow{3}{*}{\(-\)0.103} & \multirow{3}{*}{\(-\)0.105} & \multirow{3}{*}{0.662 **} & \multirow{3}{*}{1} \\  & Correlation & & & & & \\ \cline{2-5}  & Sig. (2-tailed) & & & & & \\ \hline \hline \multirow{3}{*}{Right step height (m)} & Pearson & \multirow{3}{*}{0.430} & \multirow{3}{*}{0.000} & \multirow{3}{*}{0.000} & \multirow{3}{*}{0.416} \\  & Correlation & & & & & \\ \cline{2-5}  & Sig. (2-tailed) & & & & & \\ \cline{2-5}  & Sig. (2-tailed) & & & & & \\ \hline \hline \multirow{3}{*}{Right step angle (deg)} & Pearson & \multirow{3}{*}{0.160} & \multirow{3}{*}{\(-\)0.103} & \multirow{3}{*}{\(-\)0.105} & \multirow{3}{*}{0.662 **} & \multirow{3}{*}{1} \\  & Correlation & & & & & \\ \cline{2-5}  & Correlation & & & & & \\ \cline{2-5}  & Sig. (2-tailed) & & & & & \\ \hline \hline \end{tabular}  
* * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). The number of trials: n = 62.  
\end{table} 
Table 6: Correlation coefficients between the parameters on the right side.
## 2 System and Channel Models
### Channel Model for Mobile Wireless Sensor Networks
As a result, the channel matrix of the MN MIMO system can be represented, like the SN MIMO system, as:  
\[\mathbf{H}=\mathbf{R}_{r}^{1/2}\mathbf{H}_{w}\mathbf{R}_{t}^{T/2} \tag{5}\]  
where \(\mathbf{R}_{r}=\mathbf{R}_{r}^{1/2}\mathbf{R}_{r}^{1/2}\) and \(\mathbf{R}_{t}=\mathbf{R}_{t}^{1/2}\mathbf{R}_{t}^{1/2}\) are the receive and transmit correlation matrices, and \(\mathbf{H}_{w}\) is an \((N\times M)\) matrix with zero mean, i.i.d. complex Gaussian elements with unit variance. The SN and MN MIMO channel models have different structures of their transmit correlation matrices, \(\mathbf{R}_{t}\). For example, when first \(M_{1}\) and last \(M_{2}\) antennas are, respectively, selected from SEN1 and SEN2, the transmit channel correlation matrix is:  
\[\mathbf{R}_{t}=\left| \begin{matrix}p_{1}^{2}\left|\begin{array}{cccc}1& \eta_{\text{SEN1},1,2}&\cdots&\eta_{\text{SEN1},1,M_{1}}\\ \left(\eta_{\text{SEN1},1,2}\right)^{*}&1&\cdots&\eta_{\text{SEN1},2,M_{1}} \\ \vdots&\vdots&\ddots&\vdots\\ \left(\eta_{\text{SEN1},1,M_{1}}\right)^{*}&\left(\eta_{\text{SEN1},2,M_{1}} \right)^{*}&\cdots&1\\ \end{array}\right|&0_{M_{1},M_{2}}\\ \left|\begin{array}{cccc}1&\eta_{\text{SEN2},1,2}&\cdots&\eta_{\text{SEN2},1, M_{2}}\\ 1&\cdots&\eta_{\text{SEN2},1,M_{2}}\\ \vdots&\vdots&\ddots&\vdots\\ \left(\eta_{\text{SEN2},1,M_{2}}\right)^{*}&\cdots&1\\ \end{array}\right|\\ =\left|\begin{matrix}p_{1}^{2}\mathbf{R}_{t,\text{SEN1}}&0_{M_{1},M_{2}}\\ 0_{M_{2},M_{1}}&p_{2}^{2}\mathbf{R}_{t,\text{SEN2}}\\ \end{matrix}\right|&p_{2}^{2}\left|\begin{array}{cccc}1&\eta_{\text{SEN2},1, 2}&\cdots&\eta_{\text{SEN2},1,M_{2}}\\ 1&\cdots&\eta_{\text{SEN2},1,M_{2}}\\ \vdots&\vdots&\ddots&\vdots\\ \left(\eta_{\text{SEN2},1,M_{2}}\right)^{*}&\cdots&1\\ \end{array}\right|\\ \end{matrix} \right| \tag{6}\]  
where \(\mathbf{R}_{t,\text{SEN}m}\) is the transmit antenna correlation matrix of SEN\(m\) and \(\mu(=p_{2}^{2}/p_{1}^{2})\) is the path gain ratio. Assuming \(p_{1}^{2}=1\) and \(p_{1}^{2}\geq p_{2}^{2}\) without loss of generality, \(0\leq\mu\leq 1\). The conventional SN MIMO model with SEN\(m\) can be represented as a subset of the generalized MN MIMO model as:  
\[\mathbf{R}_{t}=p_{m}^{2}\left| \begin{matrix}1&\eta_{\text{SEN}m,1,2}&\cdots& \eta_{\text{SEN}m,1,M}\\ \left(\eta_{\text{SEN}m,1,2}\right)^{*}&1&\cdots&\eta_{\text{SEN}m,2,M}\\ \vdots&\vdots&\ddots&\vdots\\ \left(\eta_{\text{SEN}m,1,M}\right)^{*}&\left(\eta_{\text{SEN}m,2,M}\right)^{* }&\cdots&1\\ \end{matrix} \right|. \tag{7}\]
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Fourier Transform Infrared Spectroscopy (FTIR) Method  
FTIR technology can obtain the infrared absorption spectrum of the measured gas by measuring the interferogram of infrared light and conducting Fourier integral transformation on the interferogram. It can realize the simultaneous monitoring of multiple components and is suitable for the background, profile, space-time change measurement, and isotope detection of greenhouse gases. The instrumentation system is relatively complex and expensive. It also has the advantages of high resolution, fast response, large number of detectable elements, high detection accuracy, strong anti-interference ability, etc., which can achieve qualitative and quantitative detection effects of various gas decomposition components. However, due to the characteristic peaks of SF\({}_{6}\) and the proximity of partial decomposition products, the existence of cross-interference is challenging for  
Figure 3: Schematic diagram of the carbon monoxide detection laser and the limit of detection. (**a**) Microphone photoacoustic spectroscopy [16]. (**b**) Quartz-enhanced photoacoustic spectroscopy (QEPAS) [9]. (**c**) Cantilever-enhanced photoacoustic spectroscopy (CEPAS). Adapted with permission from [42].  
engineering applications. FTIR uses the spectral properties of gas molecules to detect gases [43]. Different gases have different infrared absorption spectra. The quantitative detection of infrared absorption spectroscopy of gases is mainly carried out according to the Beer-Lambert law. [PERSON] et al. [44] used SF\({}_{6}\) as a background gas and used FTIR to analyze the characteristics of SOF\({}_{2}\), SO\({}_{2}\)F\({}_{2}\), CO, and SO\({}_{2}\) decomposition components. The wavenumbers corresponding to the optimal absorption peaks for the four gases were 530, 544, 2169, and 1167 cm\({}^{-1}\), respectively (Figure 4a). Figure 4b shows that as the PD time gradually increases, the gas absorption intensity of each component increases. There is a quantitative relationship between gas yield, PD time, and PD amount under five different PD conditions.  
In order to solve the problem of poor adaptability of traditional quantitative models to nonlinearity, [PERSON] et al. [45] combined Fourier transform infrared spectroscopy (FTIR) with SCARS-DNN (stability competitive adaptive reweighted sampling-deep neural network). The results showed that the lowest detection limits of SO\({}_{2}\), SO\({}_{2}\)F\({}_{2}\), and CS\({}_{2}\) were 58.03 ppbv, 55.95 ppbv, and 60.72 ppbv, respectively, under the interference of high SF\({}_{6}\) concentration.  
Figure 4: Fourier transform infrared spectroscopy quantitative analysis of SF\({}_{6}\) partial discharge decomposition components. (**a**) Standard IR spectra of SO\({}_{2}\)F\({}_{2}\), SO\({}_{2}\), SO\({}_{2}\), and CO. (**b**) Absorption intensity of decomposition components with PD time. Adapted with permission from [44].  
The relative error of the three decomposition products is no more than 1.36% and the maximum absolute error is only 0.48 ppm.
## 2 Methodology
### Case Study in Two Cities with Different Solar Radiation
#### 2.2.3 Objective Functions
The calculation method is shown in Formula (10):  
\[\text{LCCO}_{2}=\sum_{i=1}^{n}M_{i}\cdot F_{i}/A+n\cdot\left[Q_{H}\cdot EF_{ standard\ coal}/H\cdot\eta_{H}+\left(Q_{C}/\eta_{C}-E_{PV}\right)\cdot EF_{ electricity}\right] \tag{10}\]  
where \(M_{i}\) is the consumption of material of type \(i\), \(F_{i}\) is the CEF of material of type \(i\), (kgCO\({}_{2}\)/unit material quantity; see Appendix D of \"GB/T 51366-2019 standard for building carbon emission calculation\" [47]), \(A\) is the building area (m\({}^{2}\)), \(EF_{standard\ coal}\) is the CEF of standard coal (according to Appendix A of GB/T 51366-2019, the value is 0.094 KgCO\({}_{2}\)/MJ = 2.77 KgCO\({}_{2}\)/Kg), and \(EF_{electricity}\) is the CEF of electricity (in line with the \"2019 Annual Emission Reduction Project China Regional Power Grid Baseline Carbon Emission Factors\", the value is 0.8922 kgCO\({}_{2}\)/kWh). The meanings of the other parameters are the same as those in the Section Life-Cycle Cost. Photovoltaic Power Generation Estimation  
In order to make full use of solar radiation resources, it is assumed that solar photovoltaic panels are installed on the roof and south exterior wall of high-rise residential buildings. Considering the full use of solar radiation, the photovoltaic panels on the roof are laid at an angle of 30\({}^{\circ}\) and installed vertically on the south wall, except for the window areas. According to the provisions of \"GB/T 51366-2019 standard for building carbon emission calculation\" [47], the annual power generation of the photovoltaic system can be estimated according to Formula (11):  
\[\text{E}_{\text{PV}}=I\cdot K_{\text{E}}\left(1-K_{\text{S}}\right)\cdot A_{ \text{P}} \tag{11}\]  
where E\({}_{\text{PV}}\) is the annual power generation of the photovoltaic system (kW\(\cdot\)h), \(I\) is the annual solar irradiance of the photovoltaic panel surface obtained via Ladybug dynamic simulation (kW\(\cdot\)h/m\({}^{2}\)), \(K_{\text{E}}\) is the conversion efficiency of the photovoltaic cells (set at 15%for monocrystalline silicon photovoltaic cells), \(K_{\text{S}}\) is the loss efficiency of the photovoltaic system (set at 25% in line with the above criteria), and \(A_{\text{P}}\) is the photovoltaic panels' net area of the photovoltaic system (m\({}^{2}\)).
# Neural-Network-Assisted Polar Code Decoding Schemes
## 3 Neural Network-Assisted Polar Code Decoding Scheme
### Key-Bit-Based Subcode Neural Network-Assisted Decoding (KSNNAD) Scheme  
Although the LSNNAD minimizes the loss of decoding errors caused by model mismatches, the performance improvement is limited. So, we must find another scheme to improve the decoding performance.  
Ref. [37] analyzed the SC decoding error of polar codes and found that the first error is usually caused by channel noise, and the frequency of decoding failure caused by this error increases with the increase of code length. Ref. [38] analyzed and proved that when the polar code is divided into several R1 nodes, the first error in SC decoding of the polar code is likely to occur at the first information bit of the R1 nodes. Therefore, the decoding performance of the SC algorithm can be improved as long as the first error is correct. It can be seen from the above analysis that the first error is likely to occur at the first information bit position. This bit is defined as a key bit, and the node containing this bit is recognized as an NNN. In this scheme, all the NNNs are recorded and trained, and the models are saved to be called in the decoding part so that the model mismatched error is solved. Note that the label \(\dot{U}\) is the decoding result whose key bit is flipped in the decoding process. So, the training part of the KSNNAD does not need a polar encoder.  
The scheme is called a KSNNAD scheme, and the NNN is called the key bit based neural network node (KSNNN). We solve another problem: how to recognize a KSNNN.  
The simplest idea of recognizing a KSNNN is to select the key bit as the first bit of the KSNNN. Assuming that the position is \(Pos\) and the subcode length is \(N_{S}\), the position of the last bit of the KSNNN is \(Pos+N_{S}\). However, the dichotomy does not ensure that the key bit is the first position of the KSNNN; it may be the middle position. Next, we need to recognize a polar code with a KSNNN and the other four traditional subcodes mentioned above. Considering the complexity, the code length of KSNNN is limited to \(N_{S}\leq 16\). The identification flowchart is shown in Figure 11.  
Taking the polar code with \(N=32\), which is mentioned above as an example, the recognized result and decoding process are shown in Figure 12.  
Figure 10: Schematic diagram of the decoding process based on traditional subcodes combined with LSNNN.  
As shown in Figure 13, the time step required for decoding at this time is 12. The structure of the KSNNN is R0(8) + Rep(4) + SPC(4), compared with Figure 6. The structure of the original polar code is KSNNN(16) + Rep(4) + SPC(4) + R1(8). In the actual execution process, the subcode classification procedure is first executed. All possible forms of KSNNN are recorded. The key bit flip results corresponding to all possible situations of each specific form are trained as labels. Finally, the models are saved and called in the decoding part separately.  
Figure 11: KSNNN identification flowchart.  
Figure 12: Schematic diagram of the decoding process based on traditional subcodes combined with KSNNN.
## 2 Materials and Methods
### Methods
#### 2.2.2 Adsorption Test and Instrumental Analysis Using ICP-EOS  
For the adsorption tests, 20 batch experiments were conducted, pairing each synthetic monometallic solution with each ion-exchange resin. In each experiment, 0.4 g of resin was used, placed in contact with 20 mL of a solution at a known concentration. The PS-DVB and PA-DVB resins did not require any pretreatment, as they were already loaded with Na\({}^{+}\) ions, while the TFSA and OGL resins were pretreated overnight in a 15% (\(w/v\)  
Figure 1: Photographic images of the ion exchange materials utilized: (**A**) polystyrene-divinylbenzene resin (PS–DVB), (**B**) polyacrylic-divinylbenzene resin (PA–DVB), (**D**) regenerated Nafion granules (TFSA), and (**D**) lignin-based powder material (OGL).  
NaCl solution. To assess metal adsorption by the materials/ resins, solution aliquots were taken at intervals of 5, 10, 20, and 40 min. The aliquots were diluted and filtered using syringe filters with a pore size of 0.22 \(\upmu\)m, and the resulting samples were analyzed by inductively coupled plasma optical emission spectroscopy (ICP-OES Agilent 5800 ICP-EOS, Santa Clara, CA, USA). Calibration curves were previously prepared for each metal, starting from a multi-element standard solution assessing adsorption based on specific wavelengths (\(\lambda\)).
## 4 Experiments
### Experimental Setup
#### 4.1.4 Model Comparison  
We compared our model with the state-of-the-art extractive, abstractive, and topic-based models. The extractive baselines are as follows.  
Lead is a simple text summarization method based on fixed rules. It directly extracts the first few sentences of the original document as a summary without any semantic understanding or generation process.  
LexRank [11] is a summarization method based on graph algorithms. It treats each sentence as a node in the graph, builds a weighted graph by calculating the similarity between sentences, and then uses the PageRank algorithm to sort sentences to generate a summary.  
GraphSum [43] is a text summarization model based on graph neural networks, which uses graph convolutional networks to encode sentences in the input document and performs an attention mechanism on the graph to select the most representative sentences to generate summaries.  
We compared with the following abstractive baselines:  
T-DMCA [4] is an MDS model based on the transformer architecture. This model introduces a decoder-only architecture and uses the memory-compressed attention mechanism to handle the attention distribution more effectively.  
Flat transformer (FT) is a transformer model that directly processes flattened text sequences after merging multiple documents. It splices all documents into a single input sequence and uses standard self-attention mechanisms to capture and integrate information to produce a summary.  
Hierarchical transformer (HT) [23] is a variant of the transformer model adapted to handle hierarchical input structures. It encodes the input hierarchically and uses a multi-head attention mechanism to extract semantic features. The model can handle long texts by encoding the texts in layers, thereby avoiding traditional fixed-length input limitations and improving the classification performance.  
Most importantly, we also compared with the following topic-based baselines:  
TG-MultiSum [30] is a topic-based MDS model. It employs neural topic models to discover common latent topics, and then bridges different documents through them and provides global information to guide summary generation.  
S-sLDA [27] is an extended form of the LDA topic model, especially suitable for situations with limited annotated data. It first learns the topic distribution of documents and then optimizes the model parameters with the help of supervised signals.  
HierSum [18] is an MDS method based on hierarchical topic modeling. It uses the hierarchical LDA model to represent content specificity as a hierarchical structure of topic vocabulary distribution.
# Applications of Smart Glasses in Applied Sciences: A Systematic Review
## 3 Results
### Data Visualization, Processing, and Transfer Methods  
Smart glasses visualize information using augmented reality (AR) or mixed reality (MR) methods. AR enhances reality and provides additional information based on the real world, showing a virtual image against the background of the real world. Moreover, MR is a technology that arranges virtual objects in a real space with AR (displaying information against the background of reality with a technology that mixes augmented reality) in virtual reality, and constructs a virtual space based on reality. In other words, it portrays an environment with added computer graphics that can interact with reality in an actual environment. Although it is similar to AR because it is based on reality, it can be differentiated in terms of interaction with reality [74; 75]. Therefore, most smart glasses use AR or MR technology to obtain or use information. As the classification results indicate, a total 52 of the 57 studies considered (91.2%) used AR technology, and only 2 used MR technology. There were also two studies that did not use AR or MR and applied smart glasses only for data acquisition (Table 8).  
The data used for smart glasses are varied, such as visual data, numerical data, and positional data. The methods used to process the data were separated by cloud, local, and unused studies. In 23 of the studies, the data in the device were saved and sent to the cloud without being processed directly, which was 40% of the total number of studies. By contrast, in 47% of the studies, the acquired data were used in the local format, which can be processed in the device. Therefore, there were more studies on local storage than on cloud storage. When using smart glasses to display only visually without data acquisition, there were no data processes. There were five studies on devices in which data were not processed (9%), and for two studies (4%) the exact method was unknown (Figure 17).  
Wireless technology is required to transfer data. Therefore, wireless technology was used in studies that require a data transfer. The communication technology used differed based on the product used in the research. Of the 57 studies, 35 used devices that can apply Wi-Fi, and 26 used Bluetooth to transfer data. Regarding the use of an independently produced device, two studies were conducted for processing data using a cable. There were five cases in which the data were processed in the device without sharing the data, and communication technology was not used.  
Figure 17: Number and ratio of studies by data-processing method of smart glasses.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Number of Studies** & **References** \\ \hline AR & 52 (91.2\%) & [7; 8; 9; 1
## 4 Case Study
### Charging Stations  
For charging the EV fleet, there is a charger per EV, with different power capabilities. The vast majority of them are slow chargers of Type 2, which work at 22 kW AC power and are equipped with two outputs. Additionally, the parking load is also equipped with four fast chargers, with two outputs each, that work on CC mode and with power capabilities in the range of 50-100 kW.  
\begin{table}
\begin{tabular}{c c} \hline Industry consumption & \\ \hline Electric trucks, gas-powered trucks, & \(\sim\)300 kW averaged \\ and office assets power (\(P_{cons_{mean}}\)) & \\ Annual energy consumption (\(E_{cons_{annual}}\)) & 2570 MWh \\ Contracted grid power (\(Pg_{max}\)) & 380 kW \\ \hline PV generation & \\ \hline Peak PV power capacity (\(Ppv_{max}\)) & 333 kWp \\ Individual inverter nominal power (\(P_{inv}\)) & 100 kW \\ Number of PV inverters in parallel (\(m_{inv}\)) & 3 units \\ Annual PV energy generated (\(E_{PV_{annual}}\)) & 310 MWh \\ \hline Second-life energy storage system & \\ \hline Capacity (\(E_{SESS}\)) & 166 kWh \\ Power (\(P_{SESS}\)) & 191 kW \\ Cells in series (\(n_{b}\)) & 76 \\ Cells in parallel (\(m_{b}\)) & 8 \\ Maximum battery voltage (\(V_{bmax}\)) & 304 V \\ Minimum battery voltage (\(V_{b_{min}}\)) & 212.8 V \\ Rated battery voltage (\(V_{b_{rad}}\)) & 250.2 V \\ Maximum battery current (\(I_{d_{max}}\)) & 720 A \\ Battery price per kWh (\(J_{bu}\)) & 90 EUR/kWh \\ Battery price (\(J_{b}\)) & 14,955 EUR \\ Remaining battery cycles (\(n_{rem}\)) & 1000 cycles \\ \hline Electric vehicles and chargers & \\ \hline Vehicle model & _D WIDE ZE_ \\ EV battery capacity (\(E_{ev}\)) [42] & 265 kWh \\ Number of modules per EV [42] & 4 \\ EV unitary module capacity [42] & 66 kWh \\ EV cell specifications [42] & NMC \\ Number of EV considered (\(m\)) & 10 \\ Number of chargers available (\(c\)) & 10 \\ Maximum charging power (\(Pev_{max}\)) & 22 kW \\ \hline \end{tabular}
\end{table}
Table 1: Use case infrastructure and main considerations.  
It is also worth mentioning that they are mostly fed from the external grid through an MV/LV transformer rated at 1 MVA. The maximum active power consumption from the grid is 380 kW, and this value has been calculated considering that not all equipment can be operated at the same time (chargers, lighting, etc.). For this reason, the company supervises the chargers' operation through a SCADA and commands the majority of them with a control system that adjusts the power consumed to avoid exceeding the grid operator's limits.