esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### REE Concentrations in the Weathering Crust  
#### 4.1.1 REE Concentrations of Rhizosphere Soils  
The concentrations of REEs and several parameters on the REEs distribution characteristics of rhizosphere soils in various habitats are listed in Table 1. According to the Shapiro-Wilkes test, \(\Sigma\)REE, \(\Sigma\)LREE and \(\Sigma\)HREE in the rhizosphere soils were normally distributed in NGH and APHs (_p_ \(>\) 0.05) but not in BCHs (_p_ \(<\) 0.05), and therefore, the median represents the statistical characteristic of REE concentrations in various habitats. Rhizosphere soils in NGH contained REE concentrations with the median \(\Sigma\)REE values being 173 \(\mu\)g\(\cdot\)g\({}^{-1}\) and the median \(\Sigma\)LREE values being 157 \(\mu\)g\(\cdot\)g\({}^{-1}\), which were higher than that in APHs with the median \(\Sigma\)REE values being 149 \(\mu\)g\(\cdot\)g\({}^{-1}\) and the median \(\Sigma\)LREE values being 133 \(\mu\)g\(\cdot\)g\({}^{-1}\). However, \(\Sigma\)HREE exhibited the opposite feature that rhizosphere soils in APHs with higher HREE concentrations. The median LREE/HREE and La\({}_{N}\)/Yb\({}_{N}\) of rhizosphere soils in NGH were 9.76 and 7.86, respectively, which were higher than that in APHs with a median of 11.4 and 8.82, respectively, suggesting greater fractionations with LREEs and HREEs in NGH. After micro-fertilizers application, it exhibited remarkable enrichments of REEs in the rhizosphere soils of BCHs with the median \(\Sigma\)REE, \(\Sigma\)LREE and \(\Sigma\)HREE values being 187 \(\mu\)g\(\cdot\)g\({}^{-1}\), 166 \(\mu\)g\(\cdot\)g\({}^{-1}\) and 20.2 \(\mu\)g\(\cdot\)g\({}^{-1}\), respectively, which were also higher than that in NGHs. Additionally, LREE/HREE and La\({}_{N}\)/Yb\({}_{N}\) in the rhizosphere soils of BCHs increased, with the median LREE/HREE and La\({}_{N}\)/Yb\({}_{N}\) being 8.36 and 9.53, respectively. The chondrite-normalized REE patterns exhibited an obvious right-inclined style that all the rhizosphere soils were enriched in LREEs (Figure 3a,c,e) with moderately to slightly negative Eu anomalies, especially in APHs and BCHs. However, the Ce anomaly was not apparent. The coefficients of variation (CVs) of \(\Sigma\)REE, \(\Sigma\)LREE and \(\Sigma\)HREE of rhizosphere soils in APHs were 6.84%, 6.88% and 8.84%, respectively, which were much lower than those in NGHs and BCHs, indicating that the REEs concentrations of soils developed in loess without fertilization were relatively homogeneous.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 2 Related Work  
### Avsr  
The AVSR problem is highly related to lip reading. [PERSON] et al. [21] performed phoneme classification based on feed-forward deep neural networks (DNNs). In addition, several prior studies have conducted AVSR to recognize digits or isolated words by using various features such as deep bottleneck features [22], discrete-cosine-transform (DCT)-based features [23], and pre-trained CNN features with mel-frequency cepstral coefficients (MFCCs) [12]. [PERSON] et al. published a continuous speech recognition model that fused pre-trained CNN features and audio features with a dual attention mechanism [7]. [PERSON] et al. studied a model that fused raw-pixel images and waveforms by using pre-trained CNN and stacked bidirectional recurrent network [14]. [PERSON] et al. compared and analyzed AVSR models by applying either cross-entropy loss or CTC loss to a transformer-based AVSR model [20].  
Since the transformer model was presented in machine translation [24], there have been many researches to introduce the transformer model not only to ASR but also for many audio-visual tasks. Although the LSTM- and bidirectional-LSTM-based models compressed input data into a fixed-size vector, the transformer model calculated the global context over the entire input data through attention mechanism, which might result in improved performance and faster and more stable training (e.g., [18; 25]). In [26], the transformer model was also combined with the LSTM-based model. In a typical AVSR transformer model with two encoders for audio and video and one common decoder, [PERSON] et al. [20] analyzed the advantages and disadvantages of both models based on the CTC loss and sequence-to-sequence (seq2 seq) loss of the transformer model. Recently, an efficient fusion method of audio and visual in a transformer-based AVSR model has also been studied (e.g., [27; 28]). In addition, the transformer model is under study for various audio-visual tasks (e.g., [29; 30; 31; 32]).
## 1 Introduction
Although these methods previously showed good predictive accuracy, they have inherent flaws that are hard to overcome by conventional options, e.g., a grey predictive model requires stable data, so its effect drops for volatile data; SVM has difficulty in operating key parameters during modeling; conventional neural nets converge slowly and tend to become stuck in local optima due to gradual descent limits; wavelet analysis has a complex calculation process. How to select a prediction method suitable for the time series characteristics of AE is also one of the key issues that requires solving in the current rockburst research field. In order to effectively improve the actual performance of the above-mentioned various prediction models, researchers have innovatively introduced a target weighting strategy and combined it with the Particle Swarm Optimization algorithm [17; 18; 19]. After discretizing the original rockburst monitoring data, fine-grained dynamic adjustments are made to the weights of the various parameters input into the model. Facing dataset imbalance due to sample size differences in rockburst cases, researchers adopted advanced data-augmentation techniques like random oversampling [20] and SMOTE to optimize the original dataset, enhancing the model's generalization in complex conditions [21; 22]. Researchers used the Random Gradient Boosting model to classify rockburst damage in a 254-event database and received five predictive indicators [23; 24]. Ma [25] proved that, after dataset optimization, the model based on Borderline-SMOTE1 and Adaboost had significantly better prediction accuracy. Although deep learning for rockburst intensity prediction is quite advanced, research on predicting future trends of rockburst-related parameters based on monitoring data time series changes is still in its infancy. By combining enhanced CNN with dynamic sliding window technology, a model to capture rockburst evolution trends and predict microseismic parameters or combinationsis expected [26]. The BiLSTM, with its unique mechanisms, can identify and classify microseismic, AE, and EMR signals related to rockburst [27; 28; 29]. Addressing the aforementioned issues, the author proposes a fundamental approach to predicting rockbursts using AE time series. Firstly, factor analysis is utilized to reduce the dimensionality of AE characteristic parameters, eliminating redundant information, and the reorganized parameters are used as observation indicators for the AE time series prediction model. Subsequently, a Back Propagation (BP) neural network is employed to establish an AE data prediction model. To mitigate the poor prediction performance caused by initial weights and thresholds when BP networks process large-scale time series, a related fitness function can be established, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights and thresholds of the BP neural network, ultimately achieving better prediction results. Finally, a performance analysis of the FA-PSOBP AE time series model is conducted.
## 4 Numerical Results and Discussions
### Simulation Results
The performance of the UWA communication system in terms of bit error rate (BER) is shown in Figure 12, where the CIR of the generated channel depicted in Figure 6 is adopted, and the Doppler factor is set as 0.004. Each value of BER is averaged with 500 Monte Carlo simulations. From Figure 12, we observe that the BER of the UWA communication system with Doppler compensation and channel equalization is obviously reduced. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR/dB** & **OMP Algorithm** & **CPS-IOMP Algorithm** \\ \hline
0 & 0.505 s & 0.261 s \\
5 & 0.490 s & 0.282 s \\
10 & 0.497 s & 0.273 s \\
15 & 0.488 s & 0.291 s \\
20 & 0.491 s & 0.304 s \\
25 & 0.490 s & 0.285 s \\
30 & 0.489 s & 0.285 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of channel estimation method in terms of CPU running time. Figure 11: The impact of Doppler estimation error on the performance of proposed channel estimation method.
###### Abstract
As an emerging economic model, the DE has the characteristic of environmental friendliness [33] as it can reduce carbon emissions by increasing the energy efficiency, advancing energy structure transformation [34], promoting industrial upgrading [35], and strengthening environmental regulations and technological R&D [36]. Moreover, the DE enhances carbon emission efficiency through the quality of industrial structure adjustments [37] and the mediating effect of green technological innovation [38], ultimately achieving the goal of green sustainable development. Nonetheless, alternative viewpoints have been suggested in other research. In cases of high green energy efficiency, the DE may, paradoxically, increase carbon emissions [39]. Furthermore, in regions with lower technological levels, the DE is unlikely to fully realize its emissions reduction potential [40]. More complexly, while the DE drives industrial upgrading and population agglomeration, this shift may increase carbon emissions due to the \"masking effect\" [41]. A small body of literature has also examined the relationship between the DE and GD from a static perspective, demonstrating a coordinated coupling relationship between the two. Specifically, GD is argued to guide the digital economy's green transition through four dimensions: green growth, green welfare, green wealth, and green governance [42]. Additionally, scholars have suggested that ecological upgrades reduce the cost of building digital infrastructure, facilitating the expansion of the digital economy and the adoption of digital technologies [43]. Existing research on the DE and GD has predominantly centered on measuring the levels of DE and GD or examining the unidirectional impact of DE on GD. Only a few studies have explored their static relationship. In addition, there are few studies on the forecasting of the DE. Given the significance of the DE to GD and the limitations of existing research, this study aimed to: (1) observe the spatio-temporal dynamics characteristics of the DE and GD in China from 2013 to 2021; (2) investigate the dynamic interplay between the DE and GD and the impact of the DE on GD; and (3) provide recommendations for the effective integration of the DE and GD. To fulfill these objectives, a combination assignment method was employed for comprehensive evaluation. The spatio-temporal characteristics of provincial DE and GD were analyzed by integrating an unconditional spatial kernel density estimation method and the SDE model. The interplay between the DE and GD levels was explored using a PVAR model, and GD levels were forecasted using univariate time series and RBF kernel \(\varepsilon\)-SVR prediction models.
## 4 Results and Discussion
### Horizontal stresses  
Horizontal stresses are essential for defining the lateral earth pressure coefficient at the springline. In both the S and P tests, L5 and L6 cells were positioned at the location of the pipe springline. Figure 9 illustrates the variation in horizontal stresses measured in the tests and computed from the analyses.  
In the P test, a horizontal deflection of about 5 mm was recorded at the springline. This deflection causes the soil particles in the springline zone, initially in the Ko condition, to move towards the tank wall.  
Substantial variations in stresses were observed at the same locations in the S and P tests, considering arching, pipe deflection, and stress distribution. As the crown (L3-L4) and invert (L1-L2) stresses decreased, the stresses at L9-L10 and L7-L8 increased due to arching. The effect of vertical stresses transferred to the adjacent soil prism is clearly evident at points L9-L10, while this effect is somewhat reduced at points L7-L8. The most significant alteration, mainly attributed to the influence of horizontal deflection (Ph), was observed in the horizontal stresses at the springline.  
Figure 8: Vertical stresses for adjacent soil prism; (**a**) L9 and L10, (**b**) L7 and L8.  
Figure 9: Variation of horizontal stresses in the springline.
## 4 Materials and Methods
### Scheduling Procedure  
Table 5 shows the details of the scheduling. In this schedule, the longest activity duration (LAD) PR was used for the scheduling based on the project information in Table 2; Table 4. In Table 5, Column 1 (from the left) shows the starting time of the eligible activities while column 9 shows the corresponding finish times of the activities in each scheduling cycle. Column 2 shows the eligible activities while columns 3, 4, and 5 show the resources and the availability per day. The activity durations, the PR applied and the decision made in each cycle is shown in columns 6, 7, and 8, respectively. The last column shows the substitution rule used to resolve the resource shortage. In the first cycle, i.e., at the beginning of the project (time = 0) as shown in Table 5 only activities C, B, and A were eligible and enlisted for scheduling. The activities were sorted base on LAD. Activities C and B were scheduled to be completed on days 5.71 and 4.27, respectively. Activity A was delayed because of insufficient resources and the substitution rule could not resolve the resource shortages for R1 and R2. Then, in the second cycle (time = 4.27), activity B has been completed while activity C continued as scheduled in the previous cycle. The completion of activity B consequently made activity F eligible for scheduling in this cycle. Activity F and A were started accordingly and to be completed on days 8.6 and 7.58, respectively. Activity A was started by applying the resource substitution rule 2R3 = 1R2 in columns 4 and 5. The total R2 required by activities C, F, and A start is 9 of R2 which is 2 units above the daily availability (7 units). Therefore, 2 units of R2 were subtracted from column 4, and a corresponding 4 units of R3 were added in column 5. This way activity A was scheduled. Note that at the end of each cycle, the number of resources used by the activities either in starting or in progress should not exceed the availability per day of each resource. Additionally, notice that the start times for the next cycle is the earliest finished time of the previously scheduled activities. For example, in the 2 nd cycle, the finish times for activities C, F, and A are 5.71, 8.6, and 7.58, respectively. Thus, the next cycle starts on day 5.71. In the third cycle (time = 5.71), activity C was completed thereby making activities H and I eligible. Activities F and A continued as scheduled in the last cycle while activity H was delayed until the next cycle because resources were not enough to start them and the substitution rule could not resolve the resource conflict of R1 and R2. Thus, activity I started and to be finished on day 10.03 using the substitution rules 2R3 = 1R1 and 2R3 = 1R2. The shortage in R1 (2 units) and R2 (1 unit) in columns 3 and 4, respectively, were resolved in
## 3 Results and Discussion
### The Accuracy of the Inverse Calculations  
To determine the inverse calculation accuracy, the comparison between measured and calculated temperature was carried out. The analysis of the accuracy of the inverse calculation was performed based on the data obtained from the experimental cooling processes conducted at the water pressure \(p=0.1\) MPa. The comparison of the temperatures measured at points P1, P2, P3 and P4 (tm) with the temperature calculated at these same points (tc) for AAS, WS and JET cooling systems is presented in Figures 16-18.  
After leaving the electric furnace, the sensor was cooled in air during transport to the cooling chamber. The effective HTC resulting from heat losses by convection and radiation was identified by the inverse solution. The transport time from the furnace to the cooling chamber was 4 s. The initial temperature field of the sensor used in the numerical solution was generated based on the temperature measurements at time \(\tau=0\). The temperature changes calculated by using FEM were almost identical to those measured at the places where the thermocouples were located (Figures 16-18). Good agreement with the temperature changes at point P4 was also obtained. The location of the P4 point was intended to test the validity of the adopted boundary conditions on the rest of the surface. The accuracy of the performed numerical calculations is presented in Table 3. It can be concluded that in all analysed cases, a low value of the average error of temperature  
Figure 16: Comparison of the temperature changes between the measured and calculated temperatures, AAS, \(p=0.1\) MPa.  
Figure 17: Comparison of the temperature changes between the measured and calculated temperatures, WS, \(p=0.1\) MPa.  
calculations less than 1 K was obtained. The error of the inverse solution was derived in the form of the average square error calculated perpendicular to the measurement curve.  
The temperature variations indicate that for all cooling systems, the point located in the middle of the sensor radius (P2) was cooled the fastest, but the point P3 located at the edge of the sensor area cooled the slowest. The differences in these values are caused by an uneven supply of water to the cooled surface, which results with the occurrence of different cooling intensity zones.
## 3 System Model
### Energy Model  
The first-order radio model refers to the classic Leach protocol and the literature [16; 30] to calculate the dissipated energy in this paper. The energy consumption for transmitting between nodes \(i\) and \(j\) with bits of data over distance \(d\) is as follows:  
\[E_{TX(i,j)}=\begin{cases}k*E_{elc}+k*\varepsilon_{fs}*d^{2},d<d_{0}\\ k*\varepsilon_{elc}+k*\varepsilon_{mp}*d^{4},d>d_{0}\end{cases} \tag{1}\]  
where \(E_{elc}\) is the energy consumed in a transmitter or receiver circuit to send or receive one bit of packet data, and \(d_{0}\) is the threshold distance of the transmission information which is estimated by \(d_{0}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\). \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the amplifier coefficients used for free space and multi-path models. Similarly, the energy consumption for receiving between nodes \(i\) and \(j\) with \(k\) bits of data can be calculated according to Equation (2):  
\[E_{RX(i,j)}=k*E_{elc} \tag{2}\]  
Furthermore, the energy consumed for aggregating \(k\) bits of data can be estimated by  
\[E_{DA}=k*E_{pdb} \tag{3}\]  
Figure 1: WSN network model.  
where \(E_{pub}\) is the energy consumed for fusing 1-bit data.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 3 Results and Discussion
### Regional Intensive Integration Case Simulation Results  
Simulation results for the three regional intensive integration cases in base environment without any reinforcement are shown in Figure 6.  
Figure 5: Results of base case simulations: (**a**) No–synch condensers; (**b**) Synch condensers.  
Figure 6: Results of simulations of intensive input in each region: (**a**) Area 1 case; (**b**) Area 2 case; (**c**) Area 3 case.  
The above simulation results measured the hosting capacity by placing the DERs in each region, as the regions were divided in the test system illustrated in Figure 4. It can be seen from the above results that when new DERs are mainly placed in Area 1, more DERs can be accommodated, and the reinforcement of Areas 2 and 3 is more effective in increasing the overall capacity of the system. In addition, it is important to accommodate a large number of DERs using each area adequately because a larger amount can be integrated when the usable areas are properly arranged.
## 4 Results and Discussion
### Gypsum  
Gypsum is a calcium dihydrate sulfate (CaSO\({}_{4}\)-2H\({}_{2}\)O). One of the oldest known minerals, it has found very widespread use in cultural heritage, construction, medicine, and industry. Gypsum deposits are visible in numerous localities, especially in Germany, France, Austria, and Italy. The mineral is usually found in sedimentary evaporite deposits, but it can also form as a product of sulfide oxidation. The structure of gypsum is based upon a pair of adjacent layers parallel to (010) which contain Ca ions and tetrahedral SO\({}_{4}\) ions. Between successive pairs of layers, the water molecules are located in such a way that they are hydrogen-bonded to oxygens of SO\({}_{4}\) groups [45]. Each Ca ion is co-ordinated by six oxygens of sulphate groups and by two water molecules [30].  
In the Masticarro Barite Mine, it is a by-product of pyrite oxidation, which gives rise to single crystals and, more often, to aggregates of elongated monoclinic crystals (Figure 8a). Usually colourless, sometimes tending towards white, the crystals have a glassy sheet and longitudinal streaks (Figures 8b and 9). The largest crystal measures 3 mm in length, the smallest a few microns.  
The EDS semi-quantitative chemical analysis (Figure 9) revealed that gypsum crystals contain about 38 wt% CaO and about 59 wt% SO\({}_{3}\). The data highlight a shift from the stoichiometric composition of gypsum, with a SO\({}_{3}\)/CaO ratio of 1.55 instead of 1.60, which is the theoretical value. This shift is due to the inability of the EDS/SEM analysis to detect the water content (generally about 21 wt%). Gypsum exhibits lamellar morphologies characterized by parallel intergrowth in which the monoclinic symmetry is clearly observed (Figure 9).  
Figure 7: Raman spectrum of corussite from the Masticarro Barite Mine (Catanzaro, southern Italy). In the insert is the structure of the corussite [44].  
mode (\(v_{3}\)) while the bands at 619 and 668 cm\({}^{-1}\) correspond to the sulfate antisymmetric bending mode (\(v_{4}\)) [28; 30; 35; RRUFF ID R0400299].