esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### CNN Network for Activity Recognition Through Optical Flow
A Multi-Layer Perceptron (MLP) head, consisting of a fully connected layer, performs the final classification into five activity categories. The Adam optimizer is used to train the model, and optical flow computed via the Farneback method enriches temporal understanding by capturing pixel-wise motion dynamics between consecutive frames, improving robustness in real-world excavation scenarios. Each stream in the CNN architecture independently extracts spatial and motion features, ensuring that structural and dynamic information is preserved. The convolutional layers in each stream are responsible for hierarchical feature extraction, capturing intricate patterns associated with excavator movements. Before processing the feature map information for the LSTM layer, the spatial features of both convolutional branches are concatenated to preserve the spatial and channel-wise information of both branches and enhance the learning capability of the concatenation tensor of the LSTM. The LSTM layer analyzes the feature maps to evaluate the sequential pattern and temporal dependencies, followed by the MLP head (classification layer). This MLP head serves as the classification layer and is responsible for producing the final activity prediction based on the spatiotemporal features learned by the CNN and LSTM components. The final classification is performed using a fully connected (FC) layer, which receives the LSTM output and generates activity predictions for five categories: approach, digging, dumping, idle, and leveling. The classification layer is followed by an optimization step using the ADAM optimizer, which fine-tunes the model by minimizing classification loss and improving recognition accuracy. The proposed approach, integrating Farneback-based optical flow estimation with a two-streamCNN-LSTM architecture, substantially advances excavator activity recognition. Combining multi-stream feature extraction, traditional optical flow estimation, and sequential pattern recognition enables the model to outperform conventional single-stream deep learning techniques, making it a robust and reliable solution for analyzing real-world excavator activities. The proposed framework ensures robust activity recognition, even in challenging real-world excavation scenarios, using Farneback-based motion estimation, multi-stream feature extraction, and sequential pattern learning.
### Research on the Influence Law of the Effect of Fault Zone Thickness on Fault Activation  
The influence of the barrier effect of fault zone thickness on rock movement is analyzed, which is depicted in Figure 22a numerical geometric model, that is, the difference of horizontal thickness a. In this paper, the horizontal thickness of the fault fracture zone is taken as 10, 20, 30, 50, and 60 m respectively. Taking fault hanging wall mining as an example, Figure 22 depicts the length between the mining area and the fault.  
The calculated results indicate that the maximum principal compressive stress concentration degree between the mining area and the fault zone will increase, the barrier effect of the fault fracture zone on the stress field is stronger and the stress difference between the hanging wall and football of the fault zone is greater as the thickness of the fault fracture zone increases. When the fault zone is 30 m thick, the stress field distribution of the surrounding rock is shown in Figure 22, Figure 23 shows the displacement distribution of surrounding rock on the monitoring line with different fault zone thicknesses. The analysis shows that when the fault fracture zone thickness develops, the barrier degree of the fracture zone continually improves and the difference in displacement value of the surrounding rock on both sides of the fracture zone steadily increases. For example, when the thickness of the fault zone is 10 m, the horizontal displacement difference at the fault zone of the monitoring line is 0.414 m, and the vertical displacement difference is 1.193 m. When the fault zone thick increases to 60 m, the horizontal displacement difference of surrounding rock at the fault zone of the monitoring line increases to 0.57 m, while the vertical displacement difference increases to 1.57 m.  
Figure 22: The principal stress distribution of surrounding rock when the thickness of the fault zone is 30 m, they should be listed as: (**a**) Maximum principal stress distribution; (**b**) Minimum principal stress distribution.
###### Abstract  
In this paper, we propose a joint optimization scheme for a transmit power and power-splitting ratio in device-to-device (D2D)-enabled simultaneous wireless information and power transfer (SWIPT) cellular networks, considering energy signal transmission. This energy signal facilitates the energy cooperation between the D2D transmitter (DT) and the CU. Under the proposed scheme, the D2D rate is maximized while guaranteeing that the cellular user (CU) achieves the same performance as in scenarios without D2D communications. In order to solve the formulated nonconvex problem, we leverage the monotonically increasing property of logarithmic functions to transform it into an equivalent convex problem. As a result, we obtain the optimal solution in closed form. Also, the optimal D2D performance is analyzed, and useful insights into the performance improvements achievable through the proposed scheme are obtained. Numerical results demonstrate that the proposed scheme significantly outperforms the baseline scheme.  
device-to-device (D2D) communication; simultaneous wireless information and power transfer (SWIPT); transmit power optimization; power-splitting ratio optimization 2025 13 389 1  
[PERSON] and [PERSON]  
1 Department of Information & Communication Engineering, Changwon National University, Changwon 51140, Republic of Korea; [EMAIL_ADDRESS]
## 2 Background
### Influencing Factors of Placement Methods  
In an AR experience, it is essential to place virtual objects consistently in the real world while insuring stability. Jittering refers to the phenomenon where the augmented model is unstable in the scene and oscillates at high frequencies and small amplitudes [48]. This might be due to the availability of placement points [49]. Furthermore, factors such as weather variations, sunlight, and shadows, which can change lighting, are major problems in outdoor applications [50]. This weakens vision-based placement techniques that rely on image intensity information (pattern matching, points of interest, and pattern matching). The virtual content cannot be increased in the case of marker placement if the markers are blocked by other objects in the environment [51].  
Additionally, there are a various source of errors in sensor-based techniques that can lead to a low level of placement accuracy, particularly in sanitary and environmental applications. Although these sensors are calibrated before use, accuracy problems are inevitable. The unavoidable white noise in the gyroscope data causes some rotational angle drift. Over time, this noise builds up and leads to inaccurate placement outcomes [52].
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 4 Discussion
### Effect of Biochar on Soil Properties and Metal(oid)s Concentration
#### 4.1.3 Soluble and Bioavailable Forms
In the current study, the 8% biochar dosage even caused an increase in the bioavailable fraction of As. This result agrees with previous studies that show some concerns about the application of biochar to manage soil contaminated with As since it may not lead to a reduction in As mobility, but to an increase [6]. To overcome such limitations associated with As mobilization, the use of solid amendments can be tested. For example, combining biochar with iron oxides may be a good option, since as mentioned above, iron oxides can reduce the mobility of As in the soil by anion exchange [59]. [PERSON] et al. [46] confirmed the use of soils rich in free iron oxides as a suitable material in the remediation of the studied area. Thus, biochar in combination with iron-rich amendments can promote As retention capacity in soils and reduce the potential toxicity related to this element. The mechanisms involved in the immobilization of metal(loid)s caused by biochar addition are different, depending mainly on the characteristics of the soil, the biochar used and the type of metal(loid)s present [26]. Although soil pH may explain the behavior of some of the metal(loid)s studied in relation to their mobility it is worthy to mention that immobilization of metals is also related to the ability of organic matter to bind these elements [60]. Thus, not only may biochar reduce the metal(loid)s mobility by an increase of the soil pH but also by the addition of organic matter to soil. However, soluble organic matter can generate ligand binding effects that increases the solubility of heavy metals [61; 62]. Indeed, the toxicity of metals is greatly enhanced by their strong tendency to form organometallic complexes, which facilitates their availability and dispersion [63]. For example, in the case of Pb and As, bioavailability is controversial, and there could be a potential environmental risk in the long term if these areas are revegetated without adequate immobilization by additional amendments such as carbonate- and iron-rich materials. Consequently, the application of amendments to reduce the bioavailability of these elements requires monitoring over time. Regarding to the potential toxicity of the soluble concentrations of metals analysed, for Zn and Cu, the 8% treatment manages to lower the soluble fraction to concentrations below the toxic values in soil solution (Cu = 0.7 mg kg\({}^{-1}\); Zn = 0.5 mg kg\({}^{-1}\)) [64]; and in the case of Cu, also RA maintains a non-toxic concentration in soil solution. In all treatments, the soluble Pb and As content values are lower than the toxic values in soil solution (Pb = 1.0 mg kg\({}^{-1}\), As = 0.04 mg kg\({}^{-1}\)) [64; 65].
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 4 Model Architecture
### Loss Function  
The hybrid CTC/Attention architecture is an approach that has led to advances in speech processing [22; 25]. By combining both paradigms, the model is able to adopt both the [PERSON] assumptions of CTC (an aspect that is in harmony with the nature of speech) and the flexibility of the non-sequential alignments provided by the Attention-based decoder. Thus, the loss function is computed as follows:  
\[\mathcal{L}=\alpha\log p_{ctc}(\mathbf{y}|\mathbf{x})+(1-\alpha)\log p_{attn} (\mathbf{y}|\mathbf{x}) \tag{1}\]  
where \(p_{ctc}\) and \(p_{attn}\) denote the CTC and the Attention posteriors, respectively. In both terms, \(\mathbf{x}\) and \(\mathbf{y}\) refer to the input visual stream and its corresponding character-level target, respectively. The \(\alpha\) weight is introduced to balance the relative influence of each decoder. In our work, following the indications stated in [22], \(\alpha\) was set to 0.1 in all our experiments.
## 4 Experimental Results
### Results  
#### 4.3.1 Hyperparameter Study  
In our approach, we find the best probability of performing RSMDA and the best slice size using the ResNet20 model and the fashionMNIST dataset. In order to find the best probability, we performed experiments using different probabilities starting from 0.1 to 1.0 with 0.1 intervals, and it was found that 0.5 was the best probability. To find the optimal slice size, we investigate a slice size of two as the minimum, one-third of the image height or width as the maximum, and a random slice size between the minimum and maximum with each batch of images. The best parameters were found to be 0.5 for the best probability, and the random slice size was the best slice size, as shown in Figure 3, in which the x-axis, the three different color lines, and the y-axis show the probability of performing RSMDA, the slice size, and accuracy, respectively. For all of the remaining experiments, we used the best parameters.  
Note, here, accuracy is defined as the percentage of correctly predicted samples, which is mathematically defined as:  
\[A=100*C/T \tag{6}\]  
where \(A\), \(C\), and \(T\) are the accuracy percentage, the number of correctly predicted samples, and the total number of samples, respectively. Therefore, higher accuracy is preferred. The error rate is the percentage of incorrectly predicted samples and can be defined as:  
\[E=100-A \tag{7}\]  
where \(E\) is the error rate, and \(A\) is the accuracy percentage. Therefore, a lower error rate is preferred.  
We performed a number of experiments using different networks and datasets with three strategies. First, we compare our three strategies' results with random erasing data augmentation and the baseline of different models, as shown in Table 1, where RSMDA(R), RSMDA(C), and RSMDA(RC) show RSMDA row-wise (horizontally), RSMDA column-wise (vertically), and RSMDA row-column-wise, respectively. The error rate is reported, and a lower rate is better. In Table 1, RSMDA(R) has better performance than the baseline and random erasing in almost all experiments. In fashionMNIST, RSMDA(C) showed the best performance of all other methods. In the case of the CIFAR10 dataset, RSMDA(R) was more successful, especially using the VGG network type. Using the CIFAR100 dataset, again, RSMDA(R) showed better performance than random erasing, and it showed better performance using the VGG network type. In the case of the STL10 dataset, RSMDA(R) was a winner compared to the baseline and random erasing. Overall, RSMDA(R) has shown a huge performance improvement, and we consider it the best for the rest of our experiments using the optimal hyperparameters discussed in Section 4.3.1.  
Figure 3: Hyperparameters: probability and slice size effect on accuracy.
## 2 Results  
### Screening and Identification of EPS-Producing Strains  
Microorganisms capable of producing EPS are ubiquitously distributed in diverse environments due to the protective properties of EPS and various secreted enzymes for degrading organics for growth. In this study, a novel strain ZH27 sharing features reminiscent of _Aureobasidium_ strains was screened through colony characteristics. Strain ZH27 produced a remarkable EPS production of 40 \(\pm\) 1.2 g/L during the initial screening phase, surpassing the levels observed in two other isolates (30-33 g/L). _Aureobasidium_ strains exhibit varied morphological features under different conditions [9]. When cultivated on YPD (1.0% yeast extract, 2.0% peptone, 2.0% glucose, and 1.5% agar) plates for 2.5 days, the colonies of strain ZH27 developed gradually into light pink, sticky, smooth, and round structures with radial mycelia, which were surrounded or coated by EPS (Figure 1A(a)). By the fifth day, the colonies underwent a transition into non-tacky and thick textureswith a milk-white feature (Figure 1A(b)). In contrast, the colonies on the potato dextrose medium (PDA) plates displayed a predominantly white appearance with radial mycelia for 2.5 days (Figure 1A(c)). Subsequently, the color of the colonies turned into a yellowish-brown, culminating in complete black (Figure 1A(d,e). Furthermore, three distinct cell types (yeast-like cells, blastospores, and mycelia) of strain ZH27 were observed in the liquid YPD (Figure 1A(f)).  
To identify strain ZH27, the phylogenetic tree was constructed using neighbor-joining analysis based on the internal transcribed spacer (ITS) sequences. As depicted in Figure 1B, strain ZH27 (GenBank non. MN658859) shared a phylogenetic similarity of 99.82% with _A. melanogaster_ strains ATCC 9348 and CBS 123.37. Both morphological characteristics and molecular identification strongly support the classification of strain ZH27 as one member of the _A. melanogaster_ species.  
Figure 1: Morphological characteristics (**A**) and phylogenetic tree (**B**) of strain ZH27. (**A**(**a**,**b**)) represent the colonial morphologies of strain ZH27 growing on YPD plates for 2.5 and 5 days, respectively. (**A**(**c**-**e**)) are the colonial morphologies of strain ZH27 growing on PDA plates for 2.5, 5, and 8 days, respectively. (**A**(**f**)) shows the morphological features of strain ZH27 cells in liquid YPD after cultivating for 20 h (10 \(\times\) 100 magnification). Diagram (**B**) illustrates the phylogenetic tree constructed using neighbor-joining analysis, and the triangle symbol means the target strain ZH27 can be easily found among all the strains on the tree. The bootstrap values are labeled based on 1000 replicates.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations
### Capture Chamber Model  
The air volume in the capture chamber of the oscillating water column is defined from the chamber's volume and the water volume as [20; 21; 22]:  
\[V(t)=V_{c}+\frac{w_{c}H}{k}\sin\left(kl_{c}/2\right)\sin(\omega t) \tag{3}\]  
where \(V_{c},w_{c}\) and \(l_{c}\) are respectively the capture chamber's volume, inner width and length.  
The volume flow rate can be defined from Equation (3) by [20; 21; 22]:  
\[Q(t)=w_{c}cH\sin\left(\frac{kl_{c}}{2}\right)\cos(\omega t) \tag{4}\]  
with \(c=w_{c}/k\).  
From the expression of the volume flow rate (4) and considering the geometry of the chamber, the airflow speed can be expressed by [20; 21; 22]:  
\[v_{x}(t)=\frac{Q(t)}{S}=\frac{8 Acw_{c}}{\pi D^{2}}\sin\left(\frac{\pi l_{c}}{ cT_{w}}\right)\cos\left(\frac{2\pi}{T_{w}}t\right) \tag{5}\]  
where \(D\) is the duct diameter, \(T_{w}\) is the wave period.
## 4 Discussion
### Comparison of Different Imputation Methods  
To verify the efficiency of the proposed GRUD-WGAN missing data imputation model, various different methods were compared. In order to allow the imputation results to be evaluated, the complete measured water head data of a large hydropower station in the middle reaches of the Yangtze River were adopted. The data set included the daily mean value of the water head from 1 January 2014 to 1 January 2020. Various missing rates, \(r\), of samples, including 0.1, 0.3, 0.5 and 0.7, were randomly selected from the complete dataset to construct the incomplete data set. The baseline methods are as follows:  
1. **Low-rank autoregressive tensor completion (LATC):** This model selects the temporal variation as a new regularization term, which makes it better able to capture the global consistency of data [40].
2. **Bayesian-augmented tensor factorization model (BATF):** This model establishes a full Bayesian framework, and the variational Bayesian algorithm is used to adaptively optimize the parameters [41]. BATF combines explicit patterns and latent factors together, and it has good generalization performance.
3. **Mean filling:** As the most straightforward statistical approach, the missing values are filled by the mean value of the sequence data.  
The parameters of the GRUD-WGAN model were consistent with those in Table 3. The main configurations of the other models are listed in Table 6.  
The specific imputation results of each model are shown in Figure 18. For each missing rate, the imputation values of GRUD-WGAN are closest to the actual values. The RMSEs between the imputation results and the actual values under different rates of missing data are compared in Figure 19. Under each missing rate, the RMSE of the GRUD-WGAN model achieves the lowest values, while the mean filling achieves the highest. The imputation RMSEs of all the compared methods increase along with the rise in the missing rate \(r\), and the GRUD-WGAN model has the most minor error increase amplitude. This suggests that the proposed GRUD-WGAN has the highest accuracy and robustness among all the compared models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{LATC} & Weight for tensors’ nuclear norm & 5 \\  & Truncation coefficient for nuclear norm & 30 \\  & Stop tolerance & 0.001 \\ \hline \multirow{3}{*}{BATF} & Rank of the factorization matrix & 80 \\  & Number of iterations & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main parameters of the compared imputation methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **STD** & **MAD** \\ \hline PDI curve obtained after data imputation & 11.023 & 0.098 \\ PDI curve obtained without data imputation & 13.429 & 0.305 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Criteria of different procedures.  
Figure 18: The result of data imputation with various missing rates: (**a**) \(r=0.1\); (**b**) \(r=0.3\); (**c**) \(r=0.5\); and (**d**) \(r=0.7\).
## 4 Case Study
### Data Description  
As one of the important ports in China, Yingkou City in Liaoning Province collects and disperses a large number of containers every day. Therefore, container throughput forecasting for Yingkou port is an important logistics and transportation management task. Container transportation involves multiple countries and faces a complex operating environment as well as various uncertainties, which not only increases the complexity of forecasting sea-rail intermodal container transportation for Yingkou port, but also reduces the forecasting performance. Therefore, in order to more accurately predict the container throughput of sea-rail intermodal transportation at Yingkou port, it is necessary to construct a WACNN container throughput prediction model by taking into account the quality of the container throughput data, the correlation between the selected features, and the complexity of the model building to improve the prediction accuracy.
## 4 Experimental
### Chemistry
#### 4.1.2 Synthesis of Sulfonamides **8a-m**  
A mixture of chlorosulfonyl chloride **7** (100 mg, 0.29 mmol) and amine (1 mmol) in anhydrous acetonitrile (5 mL) was stirred overnight at room temperature, and then the reaction mixture was concentrated in vacuo to afford the crude sulfonamide as a yellow solid. The residue was purified on silica gel using a mixture of CH2 Cl2:EtOH (25:1) as an eluent to give the titled compounds as a yellow solid.  
3-(1,3-dimethyl-1\(H\)-pyrazolol[4,3-_e_][1,2,4]triazine-5-yl)-4-ethoxy-N-(1,3-dihydroxy-2-m ethyl-propan-2-yl)benzenesulfonamide (8k) yield 89%; m.p. 119-122 \({}^{\circ}\)C. \({}^{1}\)H NMR (CDCl\({}_{3}\)): 1.34 (t, 3H, J = 6.8 Hz), 1.39 (s, 3H), 2.69 (s, 3H), 3.79-3.81 (m, 2H), 3.78 (d, 2H, J = 11.6 Hz), 3.98 (d, 2H, J = 11.6 Hz), 4.18 (q, 2H, J = 6.4 Hz), 4.32 (s, 3H), 7.13 (d, 1H, J = 8.8 Hz), 7.98 (dd, 1H, J = 8.8 Hz), 2\({}_{2}\) = 2.4 Hz), 8.27 (d, 1H, J = 2.4 Hz). \({}^{13}\)C NMR (CDCl\({}_{3}\)): 11.04, 14.43, 20.26, 34.82, 58.52, 64.94, 67.61, 112.78, 126.97, 130.48, 131.42, 132.49, 134.56, 142.45, 147.13, 158.42, 160.46. HRMS (ESI, m/ z) Calcd for C\({}_{18}\)H\({}_{24}\)N\({}_{6}\)O\({}_{5}\)S [M\({}^{+}\)] 436.4921. Found [M\({}^{+}\)] 436.4926. Anal. Calcd for C\({}_{18}\)H\({}_{24}\)N\({}_{6}\)O\({}_{5}\)S: C, 49.53; H, 5.54; N, 19.25. Found: C, 49.69; H, 5.70; N, 19.08.  
3-(1,3-dimethyl-1\(H\)-pyrazolol[4,3-_e_][1,2,4]triazine-5-yl)-4-ethoxy-N-(1-hydroxy-2-methyl-propan-2-yl)benzenesulfonamide (8l) yield 92%; m.p. 130-132 \({}^{\circ}\)C. \({}^{1}\)H NMR (CDCl\({}_{3}\)): 1.35 (t, 3H, J = 6.8 Hz), 1.44 (s, 6H), 2.68 (s, 3H), 3.78 (s, 2H), 4.17 (q, 2H, J = 6.4 Hz), 4.30 (s,3H), 4.68 (t, 1H, J = 6.4 Hz, OH, exchanged with D\({}_{2}\)O), 7.13 (d, 1H, J = 8.8 Hz), 7.96 (dd, 1H, J\({}_{1}\) = 8.8 Hz, J\({}_{2}\) = 2.4 Hz), 8.20 (d, 1H, J = 2.4 Hz). \({}^{13}\)C NMR (CDCl\({}_{3}\)): 11.04, 14.43, 24.60, 34.82, 55.80, 64.94, 70.30, 112.78, 126.97, 130.48, 131.42, 132.49, 134.56, 142.45, 147.13, 158.42, 160.46. HRMS (ESI, m/z) Calcd for C\({}_{18}\)H\({}_{24}\)N\({}_{6}\)O\({}_{4}\)S [M\({}^{+}\)] 420.1423. Found [M\({}^{+}\)] 420.1426. Anal. Calcd for C\({}_{18}\)H\({}_{24}\)N\({}_{6}\)O\({}_{4}\)S: C, 51.42; H, 5.75; N, 19.99. Found: C, 51.38; H, 5.89; N, 19.85.