esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis of the Experimental Results
### BERT-BiGRU-Attention  
In this approach, we begin by encoding the input text utilizing the BERT model, resulting in a vector sequence. Subsequently, this sequence serves as the input for the Bi-GRU model, generating the output of the Bi-GRU model. The output of the Bi-GRU model is then weighted using an attention layer to produce a weighted sum as the final output vector. Finally, the output vector is fed into a linear layer and mapped to generate the probabilities associated with each relationship.  
To verify the effectiveness of the model, experiments are conducted on three Chinese relationship extraction datasets in this paper. The paper is divided into training set, testing set, and validation set according to their original proportions, and the experiments are used to evaluate the results of the model in named entity recognition and compare it with other Chinese relationship extraction models using precision, recall, and F1 score (F1) as evaluation metrics.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 8 Multi-User Access in LiFi
OFDM provides a straightforward method for multiuser access (i.e., OFDMA), where users are served and separated by a number of orthogonal subcarriers. However, no fast fading exists in IM/DD-based systems like LiFi systems; this signifies that low-frequency subcarriers have high signal-to-noise (SNR) statistics [140]; unlike RF systems, there is fast fading in wireless RF channels [63]. The indoor optical wireless channel shows a characteristic similar to the frequency response of a low-pass filter. Thus, subcarriers with lower frequencies generally provide users with high SNR statistics. Therefore, using appropriate user-scheduling techniques is important in OFDMA to ensure that fairness in the allocation of resources (subcarriers) is maintained. OMA and NOMA schemes are summarized in Table 6. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline  & & \multicolumn{5}{c|}{**OMA**} & \multirow{2}{*}{**NOMA**} & \multirow{2}{*}{**NOMA**} \\ \hline
**METRICS** & **TDMA** & **I-TDMA** & **O-FDMA** & **SDMA** & **CDMA** & **CSMA** \\ \hline System & LiFi & LiFi & LiFi & RF/LiFi & LiFi & WiFi/LiFi & LiFi, RF \\ \hline Working principle & Time slot allocation & Time slot allocation & User scheduling; such carrier is modulated independently and simultaneously & Separates signals by using different polarizations of the antennas. & Spreading digitalized analog signal bandwidth at a lower level & Channel sensing & Users are allocated simultaneous frequency resources \\ \hline Interference & Free & Free & High & Low & High & Low & Interference Cancellation using (SIC) \\ \hline Spectral efficiency & Low & High & Medium & Medium & Limited & High & Highest \\ \hline \end{tabular}
\end{table} 
Table 6: NOMA and OMA types with their characteristics [14; 67; 19; 145; 152; 194; 195; 196; 197; 199; 200; 201; 202; 203; 204; 205; 206; 207; 208; 209].
## 2 Battery Model Settings
### The Batteries
#### 2.1.2 Lithium-Nickel-Manganese-Cobalt Battery, LiNixCoyMnxO\({}_{2}\) (NCM)  
This type of accumulator is widely used for electric cars thanks to its high acceleration currents. It consists of manganese oxide for the cathode with micro-crystals that form a three-dimensional architecture. This characteristic reduces the electrical resistance and allows a high control of the currents. The union of nickel with manganese provides a high specific energy that makes the battery stable and long lasting. The cell has high thermal stability, and the low internal resistance allows rapid charging and discharging cycles.  
The energy density of these accumulators is 140-200 Wh kg\({}^{-1}\), and their useful life lasts longer than 2000 cycles. The working temperature of these cells can vary between \(-20\) and +40 \({}^{\circ}\)C; therefore, they do not require a very powerful cooling system.  
Due to its properties, this technology is suitable also for stationary storage applications as stated in Reference [25].
## 4 Method
### Clipping-Based Post-Training Quantization  
The key to this method is to clip input and weights during training. The clipping process will inevitably bring some accuracy drop. Differently from the traditional method, we clip the input to a fixed range in the training process. During the training of the model, the input learning of each layer can make up for a certain accuracy loss caused by clip operation to the greatest extent. Moreover, after clipping to a fixed range, we will know the maximum and minimum values of each layer parameter in each calculation process. It is helpful to avoid the cyclic calculation process of searching the range of input data stream dynamically, and we can also ensure that the range of input values is fixed in the range of our clip in order to avoid the risk of accuracy drop of post-training static quantization.  
The calculation method is as follows. We first clip the input and weight to \([-2^{-j},2^{-j}]\) and \([-2^{-k},2^{-k}]\), where j and k are integers:  
\[x=clip\left[-2^{-j},2^{-j}\right] \tag{14}\]  
\[w=clip\left[-2^{-k},2^{-k}\right] \tag{15}\]  
Similarly, the input and weight are converted into int8 type by multiplying an amplification factor and round function:  
\[x^{\prime}=round\left(2^{7+j}x\right) \tag{16}\]  
\[w^{\prime}=round\left(2^{7+k}w\right) \tag{17}\]  
Next, the dequantization process is also very simple (as shown in Figure 4):  
\[w\cdot x=\frac{x^{\prime}\cdot w^{\prime}}{2^{j+k+14}} \tag{18}\]  
\[q(w\cdot x)=\frac{x^{\prime}\cdot w^{\prime}}{2^{j+k+14}}\times 2^{7+k}=\frac{x^{ \prime}\cdot w^{\prime}}{2^{7+j}} \tag{19}\]Figure 4 shows a simplified forward propagation process for neural networks. Combined with Equations (16)-(19), it shows that the computational effort in forward propagation can be saved by multiplying the quantization parameters. This ingenious combination avoids many redundant parameter calculations. By comparing formula (18) with formulas (11)-(13), we can find that we have saved a lot of computation in the process of inverse quantization. In short, our post-training quantization method combined with clip during training has obvious advantages in avoiding the huge amount of calculation in dynamic post-training and solving the problem of accuracy loss in static post-training.  
It is worth mentioning that the clip values of our training-clipping method are symmetrically centered on 0 and are the integer power of 2; this minimizes the amount of computation with the representation of int8, but the closer the value of the clip to the real data range, the smaller the accuracy loss. In the experiment in Section 4, we will comprehensively consider these effects to obtain the best quantitative results.
## 3 Results
### The Linear Tribological Contact
At the same time, the iron content was decreased in the PI/10 CCF/1 HNT sample, but silicon was observed instead, which agreed with the Raman spectra presented above. On the PEI-based nanocomposites with the lower CoF values, in particular PEI/10 CCF/1 CNT, the iron content was negligible in the tribological layer. However, high content of both carbon and oxygen indicated the active development of tribo-chemical oxidation processes. Finally, the greatest amount of the chemical elements was in the tribological layer on the PEI/10 CCF/1 HNT nanocomposite with the most active nanofiller (Figures 13d and 9). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & **Spectrum 1** & **Spectrum 2** & **Spectrum 3** & **Spectrum 4** & **Spectrum 5** & **Spectrum 6** & **Spectrum 7** \\  & **at. \%** & **at. \%** & **at. \%** & **at. \%** & **at. \%** & **at. \%** & **at. \%** \\ \hline \multicolumn{8}{c}{PI/10 CCF/1 CNT} \\ \hline C & 12.79 & 20.80 & 59.70 & 46.04 & 62.16 & 56.17 & 17.03 \\ \hline O & 15.21 & 25.98 & 39.26 & 36.77 & 35.67 & 43.28 & 37.44 \\ \hline Cr & 1.07 & 0.65 & 0.05 & 0.27 & 0.09 & 0.21 & 0.71 \\ \hline Fe & 70.93 & 52.57 & 0.99 & 16.92 & 2.08 & 0.34 & 44.82 \\ \hline \multicolumn{8}{c}{PI/10 CCF/1 HNT} \\ \hline C & 29.36 & 27.85 & 36.63 & 59.89 & 57.45 & 61.21 & 38.62 \\ \hline O & 46.42 & 44.05 & 41.65 & 35.80 & 34.56 & 31.84 & 31.51 \\ \hline Si & 0.66 & 0.57 & 0.54 & 0.29 & 0.29 & 0.22 & 0.55 \\ \hline Cr & 0.36 & 0.45 & 0.31 & 0.11 & 0.12 & 0.10 & 0.54 \\ \hline Fe & 23.20 & 27.08 & 20.87 & 3.91 & 7.58 & 6.63 & 28.78 \\ \hline \multicolumn{8}{c}{PEI/10 CCF/1 CNT} \\ \hline C & 85.72 & 28.65 & 39.76 & 80.33 & 39.37 & 46.78 & 59.00 \\ \hline O & 13.62 & 37.91 & 37.64 & 18.44 & 31.35 & 39.00 & 35.49 \\ \hline Cr & 0.02 & 0.56 & 0.35 & 0.02 & 0.45 & 0.23 & 0.08 \\ \hline Fe & 0.64 & 32.88 & 22.25 & 1.21 & 28.83 & 14.02 & 5.43 \\ \hline \multicolumn{8}{c}{PEI/10 CCF/1 HNT} \\ \hline C & 68.19 & 61.67 & 69.88 & 23.11 & 54.86 & 23.84 & 61.07 \\ \hline O & 25.53 & 32.88 & 29.14 & 53.26 & 41.20 & 43.65 & 34.76 \\ \hline Al & 0.17 & 0.25 & 0.03 & 0.45 & 0.46 & 0.34 & 0.19 \\ \hline Si & 0.29 & 0.37 & 0.23 & 0.77 & 0.75 & 0.86 & 0.19 \\ \hline Cr & 0.11 & 0.09 & 0.04 & 0.52 & 0.09 & 0.63 & 0.08 \\ \hline Fe & 5.71 & 4.74 & 0.68 & 21.89 & 2.64 & 30.68 & 3.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of EDS analysis on the wear track surfaces shown in Figure 13.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 3 Intelligence Networking for 6G
### The Evolution of 6G
While 6G research is still in its infancy, researchers from Europe, the United States, and China have started to study 6G networks. It has been recognized that each new generation of mobile global standards appears every 10 years, and therefore 6G is expected to emerge around 2030. Moreover, 6G will have features of on demand service, strong AI seamless embedding, flexibility, and simplicity. Table 1 summarizes the wireless technologies from 1G to 5G in terms of peak data rates and presents driver applications used in 6G. The data rate of 5G mobile systems increased from 1 Kbps to 10 Kbps, 10 Mbps, 100 Mbps, and up to 10 Gbps. It is expected that the upcoming 6G systems will achieve the goal of increasing data rates by 100 times to about 1 TBps or even higher. This expectation is based on the realization of edge intelligent components, which will allow edge-computing networks to realize self-adaptation and self-learning. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Communication Technology** & **Radio and Networking Technology** & **Peak Rate** & **Mobile-Data** & **Driving Application** \\ \hline
**1G** & Analog cell phone, AMPS, TDMA & 8 Kbps & Voice communication \\ \hline
**2G** & Digital telephone, GSM, CDMA & 9.6–344 Kbps & Voice/data communication \\ \hline
**3G** & CDMA2000, WCDMA, TD-SCDMA & 2 Mbps & Multimedia communication \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison between mobile cellular networks and communication technology.
## 2 Morphodynamics of the High Alpine Mountains
### Morphodynamic Adjustments of Proglacial Torrential Systems  
Melting glaciers and permafrost degradation raise many questions about the release of volumes of sediment that could abundantly supply torrential systems, increasing their activity and the resulting risks.  
A number of studies have raised fears of an increase in torrentiality linked to an increase in the supply of sediment from deglaciated areas [60; 61; 62; 63; 64; 65; 66]. They are supported by several literature reviews that report on crises in torrential activity following glacial retreats [67]. The glacial retreat phase, in particular, is described as a period when morphogenic activity is at its peak. [PERSON] and [PERSON] [68] proposed models for the export of materials from deglaciated areas, which show an increase in sediment supply just at the time of deglaciation. [PERSON] et al. [49] show a trend towards an increase in floods and debris flows in glaciated basins, with the most significant events associated with periods of sustained rainfall and the destabilisation of moraine structures. Other work introduces sudden events, such as glacial lake outburst floods [67; 69] and the possible and punctual recovery of detrital stocks by fluvial processes (sediment materials eroded) during the paraglacial sequence [70; 71].  
Connectivity with the slopes and the capacity for sediment transit (constitution and/or remobilisation of sediment stocks) are central to the question of the effects of climate change on torrential systems. Research has shown that these hydrosystems are, in fact, highly fragmented due to the efficiency of paraglacial storage processes. Detrital stocks are only very partially coupled to torrential channels. [PERSON] and [PERSON] [67] show that the majority of sediments released from ice are stored in situ in de-iced areas. Proglacial lakes are extremely effective sediment traps. Paraglacial models of material export clearly show the transition to a new state of dynamic equilibrium. The changes are transition phases from a significant state of abundant sediment supply with spatially extensive fluvial processes(braiding, wide active band, etc.) to a state of equilibrium with a definite sediment deficit and a channel encased in glacial and fluvio-glacial fill. Extreme events occasionally disrupt the transition, but the trend remains the same.  
On the proglacial torrents on the Chamonix side of the MBM, [PERSON] [72] describes the sharp decline in torrent activity over the last 150 years and the consequent reduction in the potential of the torrent system to remobilise sediment sources. This does not rule out extreme phenomena that may occur (the 1920 and 1996 floods from the Mer de Glace), but which are very infrequent and have very local consequences. Glacial retreat does not imply an increase in sediment supply but, on the contrary, a decrease in input due to the inefficiency of sedimentary coupling.
## 2 Conventional Digital Image Correlation (DIC) Applications
### Bending Behavior of Thick-Walled Composite Tubes
#### 2.3.2 Experimental Work  
Two thick-walled thermoplastic composite tubes made of Carbon/PEEK material were manufactured using an automated fiber placement (AFP) process and were tested using a homemade pure bending test setup.  
**Sample manufacturing and preparation:** The test articles were made of 0.25-inch wide unidirectional rows supplied by TenCate Advanced Composites Company, Nijverdal, Netherlands (commercial name \"Cetex TC1200 PEEK AS4\"). The tows were made of a semicrystalline poly-ether-ether-ketone thermoplastic resin with unidirectional carbon fibers. The tows were fed into a robotic-type AFP machine available at CONCOM for manufacturing two thick-walled composite tubes (Figure 10). The first tube was made with a laminate stacking sequence of \([(25)_{45},(-25)_{45}]\) (i.e., the inner layer and out layer are oriented at 25 and \(-25\) degrees with respect to the tube axis), while the second tube had a laminate stacking sequence of \([25,-25]_{45}\), which means it was made of \([25,-25]\) layers alternatively arranged. Both tubes had the same dimensions (outer diameter \(=61.1\) mm, inner diameter \(=38.1\) mm, thickness \(=11.5\) mm, and length \(=1016\) mm).  
Figure 9: Before axial strain failure (\(\varepsilon_{xx}\)) obtained by DIC (side view) [15].  
For preparation of the tubes for measuring deformations and strains using DIC, the surface of the tubes should have a random pattern with good contrast to be recognized by the cameras. The surface of the thermoplastic tubes were quite black, and, therefore, a white random pattern was applied on the specimens' surface by hand using permanent markers (Figure 11). According to the recommendation of the DIC system's manufacturer (Correlated Solutions, Irmo, SC, USA), the size of the speckles was kept between 3 to 5 pixels with about 50% density. Two 5 Megapixel CCD cameras (from Point Grey with 2/3-inch sensor) attached with Schneider 17 mm lenses were used in this experiment.  
**Test setup:** The bending test was carried out using a homemade pure bending test setup as shown in Figure 12. The DIC system used for the test consisted of two pairs of cameras; one pair was placed in front of the tube, and the other pair was mounted above the test setup. After calibrating the cameras and connecting all the gages to the data acquisition system, the test was started. End forces applied by two hydraulic cylinders were converted to the bending moment using two moment arms at two ends of the setup. The bending moments were transferred to the tube ends through a set of inner and outer rings filled with low melting temperature point alloy (LMPA) for smooth transition of the load to the tube. In order to prevent tube failure inside the rings, tabs were added at the tube ends.  
Figure 11: Random pattern on the surface of the thick composite tube [16].  
Figure 10: Manufacturing of the thermoplastic composite tube using AFP [16].
## 4 Results and Discussion
### The Load Disaggregation Results  
The comparison of load disaggregation results and measured power for one day using our proposed method are shown in Figure 3. We calculated the cumulative estimated power and cumulative measured power of each appliance in one day and got the result in Figure 4. In Figure 3a, the dish washer and heat pump are simultaneously switched on at the 174 th minute, and our method shows good performance in recognizing multiple devices when they are simultaneously turned on. We can see from Figure 3, there is a misidentification at the 345 th minute due to an abnormal operating cycle of the fridge. Figures 3 and 4 show that the proposed algorithm has achieved disaggregation of four appliances, and the estimated aggregation consumption of each appliance is close to the measured consumption.  
Figure 3: Load disaggregation result and the measured power of four appliances for a day. From left: (**a**) Load disaggregation result of four appliances in a day; (**b**) measured power values of four appliances in a day.
## 3 Results and Discussions
### Development of Calibration FTIR-ATR Model
Table 1 provides an overview of the results of the proposed calibration models according to the spectrum adjustments used and the regression method used. Table 1 shows that the FTIR calibration model using PLS regression compared to PCR gives better results. This can be attributed to the fact that in the case of PLS regression, the determination of the number of PCs and the regression and rotation of the transformed matrices are performed simultaneously. In contrast, in PCR, the spectral information is first used to calculate PCs, and in the next step a mathematical description is created as the dependence of PCs on KV\({}_{100}\)\({}_{\text{C}}\). It follows that PCs are also used to construct the model, which can describe the source of variability in data that are not related to viscosity. As can be seen from Table 1, the adjustment of the spectra by the second derivation, which leads to the enhancement of the spectral bands [56], had a positive impact on the construction of the FTIR-PLS calibration model in the spectral range 1750-650 cm\({}^{-1}\). A significant dependence of R = 0.95 was achieved between the viscosity values determined by the standardized laboratory method according to CSN EN ISO 3104 and the values predicted by FTIR-PLS calibration models and RMSEC = 0.148 mm\({}^{2}\)s\({}^{-1}\) (see Figure 2). Given that 190 calibration standards and 275 wavenumbers were used to construct the calibration model, it was not the case that the correlation arose from overfitting (precising spectral data by derivation until they lose real information value) of too many wavelengths with a small number of samples. The spectral information contained in the 275 data points was compressed into ten principal components (PCs), which are linear combinations of the original absorbances at given wavenumbers and are uncorrelated to each other. The optimal number of PCs was determined by PRESS diagnostics in the minimum of RMSECV (Root Mean Square Error of Cross Validation) [39]; adding another component after this point does not improve the performance of the method, but on the contrary reduces it. The proposed calibration model must be continuously supplemented by other standards--motor oils, the composition of which will correspond to the matrix of worn-out motor oils and the range of their values KV\({}_{100\,\mathrm{\SIUnitSymbolC}}\).
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 6 Experiments and Results  
### Implementation Details  
Firstly, we trained a semi-supervised network for depth estimation on a computation hardware platform with NVIDIA GeForce GTX 1080 Ti (NVIDIA, Santa Clara, CA, USA), with the Ubuntu 14.04 (Canonical, London, UK) operating system and the TensorFlow1.4.0 (Google, Mountain View, CA, USA) development tool. In the KITTI dataset, we selected 7322 groups as the training set, in which each group contained right and left views and two corresponding sparse depth maps [32]. KITTI is a popular dataset which can be used for vision algorithm testing of ADAS; it contains a large number of stereo image pairs captured from a car driving in an urban scenario and also provides sparse depth data matched with the stereo vision. These depth data were not only the sparse depth labels in the  
Figure 5: Flow chart of the proposed distance measurement method.  
training process, but also the ground truth for algorithm evaluation. During depth estimation network training, we used stochastic gradient descent with an initial learning rate of 0.0001 and 50 epochs. From the 30 th to 40 th epoch, the learning rate was reduced to 1/2 of the initial value, and the learning rate of the last 10 epochs was reduced to 1/4 of the initial value. The batch size was equal to 8. We used the Adam optimizer to optimize the model, and set \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The Mask R-CNN model used in this paper was downloaded from [[https://github.com/matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN)]([https://github.com/matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN)).  
In order to assess the performance of depth map estimation, which is the key of distance measurements, we used the following depth evaluation metrics [33]:  
(1) Absolute relative error (_AbsRel_)  
\[AbsRel=\frac{1}{N}{\sum_{i=1}^{N}\frac{\left|\overline{z_{i}}-z_{i}\right|}{z _{i}}} \tag{13}\]  
(2) Root-mean-square error (_RMSE_)  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}\left|\overline{z_{i}}-z_{i}\right|^{2}}} \tag{14}\]  
(3) Threshold accuracy  
\[\text{max}\Bigg{(}\frac{\overline{z_{i}}}{z_{i}},\frac{z_{i}}{\overline{z_{i} }}\Bigg{)}=\delta \tag{15}\]  
where the threshold usually takes three values: 1.25, 1.25\({}^{2}\), and 1.25\({}^{3}\); for different thresholds, there are different threshold accuracies: \(\delta<\) 1.25, \(\delta<\) 1.25\({}^{2}\), and \(\delta<\) 1.25\({}^{3}\); \(N\) is the number of pixels with ground truth in the test set; \(\overline{z_{i}}\) and \(z_{i}\) are the predicted depth value and true depth value, respectively. Regarding the above evaluation indexes, the smaller the first two parameters (_AbsRel_ and _RMSE_), the higher the accuracy of the depth estimation result. Conversely, the larger the threshold accuracy, the better the depth estimation result.