esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed System Methodology
### Activity Recognition and Localization  
This subsection describes the proposed activity recognition and localization approach with four distinct classes and three classes, respectively, using dual LSTM networks. We implemented LSTM because it can handle temporal dependencies and because of its effectiveness in sequence prediction tasks, making it particularly suitable for activity recognition. A Long Short-Term Memory (LSTM) network, a variant of Recurrent Neural Networks (RNNs), is specifically engineered to handle time-series data. The structure of the LSTM network utilized in this research is illustrated in Figure 3.  
Figure 2: An in-depth overview of the proposed ECI-TeleCaring system along with the ECI engine.  
The network consists of an input layer, several LSTM layers, and a fully connected dense layer, culminating in a softmax output layer. The LSTM layers are tasked with capturing temporal dependencies in the data, and the dense layer translates these learned features into four distinct activity classes. The LSTM cell can be described by the following equations:  
Forget gate:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
where \(f_{t}\) is the forget gate output, \(W_{f}\) is the weight matrix, \(h_{t}\) is the previous hidden state, \(x_{t}\) is the current input, and \(b_{f}\) is the bias term.  
Input gate:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[C_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
where \(i_{t}\) is the input gate output, \(C_{t}\) is the candidate cell state, \(W_{i}\) and \(W_{\text{C}}\) are the weight matrices, and \(b_{i}\) and \(b_{\text{C}}\) are the bias terms.  
Cell state:  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot C_{t} \tag{4}\]  
where \(C_{t}\) is the current cell state and \(C_{t-1}\) is the previous cell state.  
Output gate:  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\cdot\tanh(C_{t}) \tag{6}\]  
where \(o_{t}\) is the output gate output, \(h_{t}\) is the current hidden state, \(W_{o}\) is the weight matrix, and \(b_{o}\) is the bias term. The LSTM network is trained using the cross-entropy loss function, which is suitable for multi-class classification tasks. The loss function is defined as  
\[L=-\sum_{i=1}^{N}y_{i}\log(\hat{y}_{i}) \tag{7}\]  
where \(N\) is the number of classes, \(y_{i}\) is the true label, and \(\hat{y}_{i}\) is the predicted probability for class \(i\). The LSTM model was trained using two datasets split into training, validation, and test sets having 70%, 15%, and 15%, respectively. The model was trained using the cross-entropy loss function and optimized with the Adam optimizer. The batch size was set to 32 and the learning rate was set to 0.0001. After training, we tested the model on the test dataset to evaluate the performance of the model using standard evaluation metrics such as accuracy, F-1 score, and recall.  
Figure 3: Illustration of the architecture of the LSTM model employed in the proposed ECI framework for activity and localization prediction.
## 2 Materials and Methods
### Numerical Schemes and Boundary Conditions
#### 2.4.2 Constraints and Boundary Conditions  
To investigate the influence of both uniform and helical LVOT flow, we introduce an in-plane velocity profile to complement the through-plane waveform [38] at the inflow boundary. The amplitude of the in-plane waveform is set to half of the through-plane velocity during systole and zero during diastole. The through-plane velocity profile along the \(+y\)-direction and the in-plane velocity profile on the \(xz\)-plane, and the resulting Windkessel (WK) pressure profile at the outflow boundary, are given in Figure 4.  
The velocity profile serving as the inflow boundary condition is imposed on the circular domain at the bottom wall of the computational box. The outlet pressure profile is dynamically determined using the three-element Windkessel (WK3) model, which is the most well-known Windkessel model with a proximal resistor (\(R_{p}\)) in series and a parallel  
Figure 4: The through-plane and in-plane inflow velocity profiles for the healthy case and the corresponding aortic pressure [38].  
Figure 3: Detailed view of structural meshes for aortic valves with varying degrees of calcification and housing: (**a**) mesh for Grade 3; (**b**) mesh for Grade 4; (**c**) mesh for Grade 5; (**d**) cut-away view of the reference housing with no calcification; (**e**) mesh for Grade 6.  
combination of a distal resistor (\(R_{d}\)) and a capacitor (\(C\)). The three-element Windkessel model is governed by the following ordinary differential equation:  
\[\bigg{(}1+\frac{R_{p}}{R_{d}}\bigg{)}Q(t)+CR_{p}\frac{dQ(t)}{dt}=\frac{P(t)}{R_{ d}}+C\frac{dP(t)}{dt} \tag{9}\]  
where \(Q(t)\) is the flow rate, \(P(t)\) is the pressure in mmHg, \(R_{p}\) is the characteristic impedance, \(C\) is the compliance, and \(R_{d}\) is the peripheral resistance. The simulation initiates with an internal aortic pressure set at 70 mmHg. The model parameters were selected as \(R_{p}=0.033\,\mathrm{mmHg\cdot s/mL}\), \(R_{d}=0.79\,\mathrm{mmHg\cdot s/mL}\), and \(C=1.75\,\mathrm{mL/mmHg}\)[38].  
Equation (9) is discretized using a second-order backward finite difference scheme and divided into three parts as follows:  
\[LHS=\frac{Q^{n}}{C}\bigg{(}1+\frac{R_{p}}{R_{d}}\bigg{)}+\frac{R_{p}}{\Delta t }\bigg{(}1.5Q^{n}-2Q^{n-1}+0.5Q^{n-2}\bigg{)} \tag{10}\]  
\[RHS=\frac{1}{\Delta t}\bigg{(}-2P^{n-1}+0.5P^{n-2}\bigg{)} \tag{11}\]  
\[DNM=\frac{1.5}{\Delta t}+\frac{1}{R_{d}C} \tag{12}\]  
\[P^{n}=\frac{LHS-RHS}{DNM} \tag{13}\]  
where \(n\) denotes the solution step and \(P^{n}\) is the outflow pressure, which will be used as a Windkessel boundary condition in the following time step.  
In cases involving calcification, the inlet flow rate is individually adjusted for each case using a unique factor with the goal of maintaining consistent aortic pressure above the valve while simulating increased LVOT pressure due to stenosis. Then, the flow rate factors for each calcification grade are defined as follows: \(f_{\text{No-calcification}}=1\), \(f_{\text{G3}}=0.75\), \(f_{\text{G4}}=0.67\), \(f_{\text{G5}}=0.67\), \(f_{\text{G6}}=0.48\).
## 3 Methodology
### Implementation
#### 3.5.2 Ex-Post Measurement Based Correction  
The objective of this step is synthesising a measurement profile. In a real-life implementation, this step would therefore not be required. As the curtailment profile is determined day-ahead, based on the expected PV output, a correction needs to be made to determine the actual profile as it would be measured in a real-life situation. To this end, an ex-post measurement based correction is made by comparing the PV output based on the weather measurements with the curtailment profile. When the PV production is lower than the curtailment profile, the curtailment profile is corrected downwards. When the PV production exceeds the expectation, this surplus is added to the curtailment profile. This new, corrected profile represents load measurements that would have been acquired at the flexibility asset in a real-life situation.
# Research on the Impact Force of Rockfall Impacting Sand Cushions with Different Shapes
## 4 Numerical Model and Discussion
### Discussion
#### 4.4.1 Penetration Depth  
The maximum penetration depth of rockfall with different shapes impacting sand cushion at the same height is shown in Figure 11.  
It can be found from the penetration depth shown in Figure 11 that the maximum penetration depth corresponding to different shapes of falling stones and the area with obvious deformation on the top of the cushion are different. The maximum penetration depth increases with the increase in shape coefficient. The maximum penetration depth of the falling rock with \(S=4\) is 68.237 mm. The maximum penetration depths of the falling rock with \(S=2\), 1, 0.5, and 0.25 are 30.033 mm, 21.729 mm, 13.696 mm, and 4.240 mm, respectively. The maximum penetration depth of rockfall with different shapes is very different. The maximum penetration depth of rockfall with \(S=4\) is about 16 times of that with \(S=0.25\). Therefore, even if the rockfall impact force with large shape coefficient is small, the influence of rockfall on impact response cannot be ignored in the structural design of the shed tunnel. The area of deformation caused by \(S=0.25\) rockfall on the top surface of the cushion is significantly larger than that caused by \(S=4\). This is mainly related to the area at the bottom of the rockfall. The lower surface curvature radius of the rockfall with \(S=0.25\) is larger, and the contact surface with the cushion is larger. The lower surface curvature radius of the rockfall with \(S=4\) is smaller, and the contact surface with the cushion is smaller. When the penetration depth of cushion is the same, the larger the contact area (the smaller the shape coefficient) is and the more the kinetic energy of rockfall is consumed. Therefore, when the initial kinetic energy of rockfall is constant compared with larger shape coefficient and spherical rockfall, the rockfall with a smaller shape coefficient needs greater penetration depth to consume the kinetic energy of rockfall. The maximum penetration depth increases with the increase in the shape coefficient.  
Furthermore, the phenomenon of different impact forces corresponding to different shapes of rockfall can be explained by the different maximum penetration depths corresponding to different shapes of rockfall. When rockfalls with different shapes fall at the same height, the tip rockfall is easier to move downward in the cushion than the flat rockfall, and the contact time with the cushion is longer. The corresponding penetration depth is larger, and the impact process is slower, so the corresponding impact force is smaller.
## 4 Experimental Validation
### EQ2. Robustness to Heterogeneous Environments  
In a heterogeneous environment, there are workers with varying training speeds, which may adversely affect the model convergence of asynchronous distributed training. For example, when there are a few workers with slow training speeds, the gradients computed from the slow workers (i.e., staled gradients) are highly likely to cause the global model to be learned in a wrong direction, thereby delaying the model convergence. Thus, distributed training algorithms should be evaluated in this aspect. To evaluate SHAT in this aspect, we configure one homogeneous and three heterogeneous clusters: (1) **HO**: a homogeneous cluster with no slow workers, (2) **HE0**: a heterogeneous cluster with a \(\times 2\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**\# of Workers**} & \multicolumn{4}{c}{**Competing Methods**} \\ \cline{3-6}  & & **ENSEMBLE** & **ASP** & **SHAT\({}^{Root}\)** & **SHAT** \\  & & \((\mathbf{\alpha}=\mathbf{0})\) & \((\mathbf{\alpha}=\mathbf{1})\) & \((\mathbf{\alpha}^{i}=\mathbf{1}-\frac{\mathbf{h}^{i}}{\sqrt{n}})\) & \((\mathbf{\alpha}^{i}=\mathbf{1}-\frac{\mathbf{h}^{i}}{\log n})\) \\ \hline \multirow{3}{*}{ResNet-50 [1]} & 2 & 0.9319 & 0.9332 & 0.9384 & **0.9399** \\  & 4 & 0.9112 & 0.9315 & 0.9329 & **0.9382** \\  & 8 & 0.8910 & 0.9182 & 0.9293 & **0.9311** \\  & 16 & 0.8491 & 0.9019 & 0.9113 & **0.9311** \\ \hline \multirow{3}{*}{VGG-16 [2]} & 2 & 0.9012 & 0.9209 & **0.9242** & 0.9238 \\  & 4 & 0.8821 & 0.9171 & 0.9223 & **0.9228** \\ \cline{1-1}  & 8 & 0.8247 & 0.9071 & 0.9128 & **0.9226** \\ \cline{1-1}  & 16 & 0.7002 & 0.8687 & 0.9081 & **0.9209** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The model accuracy of asynchronous training with different update methods.  
slower worker, (3) **HE1**: a heterogeneous cluster with a \(\times 10\) slower worker, and (4) **HE2**: a heterogeneous cluster with a \(\times 100\) slower worker. We train the ResNet-50 model using SHAT on the four clusters, and measure their accuracies with respect to the training time. Figure 5 shows the results, where the \(x\)-axis represents the training time and the \(y\)-axis represents the top-1 accuracy. Clearly, _SHAT is quite robust to all heterogeneous clusters in terms of the model convergence._ In particular, regardless of the degree of heterogeneity, SHAT always shows a consistent convergence rate. Note that the small loss is inevitable because of the slow worker. This result indicates that SHAT effectively reduces the difference among the local models of workers, by considering the heterogeneity among workers (i.e., the staleness of each worker \(c^{i}\)).
## 3 Results
### Mitigation of Odor and Targeted Gases as a Function of UV-A Dose
#### 3.1.1 Measured Odor  
UV-A photocatalysis significantly mitigated odor emissions from swine barn. UV dose \(\geq\) 4.0 mJ\(\cdot\)cm\({}^{-2}\) showed a statistically significant percent reduction of odor (Table 1). There was no significant improvement between 4.0 \(\sim\) 5.3 mJ\(\cdot\)cm\({}^{-2}\) doses, suggesting that a low dose is economical. The likely reason for the lack of apparent improvement for the higher dose is the odor measurement method itself (by dilution only), which accounts for the odor 'concentration' without considering VOCs' actual photochemistry and changes to the odor offensiveness. UV is also known to generate VOCs that might be impactful odorants, and therefore, the overall odor concentration is not solely sufficient to evaluate the mitigation effect. Evaluation of targeted odorants and linking them to specific aromas is shown in the subsequent sections.
## 3 Results and Discussion
### Effect of pH  
Some authors have reported a considerable effect of the suspension pH on the photocatalytic hydrogen generation for different photocatalytic materials and scavengers [41; 46; 47; 48; 44]. To evaluate the effect of this variable on the photoactivity, several runs are carried out at varying the solution pH. Figure 4 reports the mean hydrogen production rate collected at different pH values. A higher reactivity may be recognized under alkaline conditions, whereas negligible activity is recorded at acidic conditions (data not shown). Despite the great number of studies in the literature survey focusing on the effect of pH, only a few authors have attempted to explain the correlation between solution pH and photocatalytic activity [44; 49; 50; 51]. The surface properties of the photocatalyst, its stability in the aqueous solution, the band gap shift, and the sacrificial agent's molecular properties should be considered to understand the phenomenon.  
Figure 3: Reciprocal of the hydrogen production vs. the inversus of the organic concentration. Experimental conditions: V = 0.30 L; T = 35 \({}^{\circ}\)C; P = 1 atm; pH \(\approx\) 8.5; C\({}_{\text{(I$\%$wt)Cu${}_{2}$O/TiO${}_{2}$}}\) = 700 ppm.  
The following remarks should be considered to underpin the present photocatalytic outcomes:  
* The stability of the copper species on the surface of TiO\({}_{2}\) particles is affected by the pH. In particular, lower stability is recorded by several authors under acidic conditions [49; 52; 53].
* About Methanol (i.e., the sacrificial agent), has a pK\({}_{\text{a}}\) value of 15.0 [54]. Thus, methanol is present in non-dissociated form for all the pH values tested (See Figure 5).
* The pH\({}_{\text{2 PC}}\) of TiO\({}_{2}\) is about 6.25 [55]. Thus, a high concentration of negative charges is recorded on the catalyst surface under alkaline conditions (See Figure 5), able to promote the interaction between substrate and catalyst surface.
* Negative charges on the catalyst surface (under alkaline conditions) reduce particle agglomeration [56].  
Based on the above considerations, the greater photocatalytic activity under alkaline conditions may be attributed to (i) the presence of stable copper species involved in visible light-induced hydrogen generation and (ii) the more efficient adsorption of the organic compounds.
## 3 Materials and Methods
### The Location Feature Extraction Strategy
#### 3.2.2 Instantiation  
In the convolution block ConvNHS of the LFE block, we chose the normalization method through careful consideration and experimental verification. In the specific implementation, we chose the most classic BN as the normalization method of the CPU environment to verify the performance of global normalization. At the same time, in the GPU environment, the latest popular LCN was used to verify the performance of local normalization. In addition to the above deployment, the activation function used _Hard Swish_, which can have advantages in deeper networks.  
The LFE block can be plug-and-play, which was deployed in the YOLOv4 [57]. Firstly, only one LFE was inserted into the network for training and testing. While inspired by [PERSON]'s team [58], inserting five location feature extraction modules has been tested for training and testing. The insertion positions are shown in Figure 3. After the images enter the backbone network CSPDarknet53 [57] for feature extraction, the SPP module is deployed to increase the receptive field so that any size feature map can be converted into a fixed-size feature vector. Then the PANet [59] is used for feature fusion. Five places were selected after the SPP [60] module and concatenation separately, both of which were rich in information interaction. Finally, the three Yolo heads made predictions, respectively. The later experiments (see Section 4.4 for specific experiment results) also proved that this structure does reduce the location errors and accomplish the location feature extraction process.  
Figure 3: Improved YOLOv4. Scheme I: Insert a position feature extraction module in positions 1–5, respectively. Scheme II: Positions 1–5 are all inserted into the LFE block. DBL: DarkNetConv2D + Batch Normalization + Mish; SPP: Spatial Pyramid Pooling; Conv: Convolutional Layer; Concat: Concatenation.
## 3 Results and Discussion
### Compositions of Volatile Compounds
GSO (41.38 mg/L) made under atmospheric distillation showed the highest contents of alcohols, and MBA (20.04 mg/L) made under reduced pressure distillation using millet, sorghum, and rice showed the lowest. Isoamyl alcohol, isobutyl alcohol having a fruity flavor [35], and phenylethyl alcohol having a rose odor [37] were detected as major alcohol components. Isoamyl alcohol is known to give off a harmonious flavor when there is an appropriate amount in the liquor, but when there is a large amount, it has an unpleasant, musty flavor [38]. These alcohols are commonly found in various alcoholic beverages such as _soju_, wine, whiskey, and brandy [30; 31; 32; 33; 34; 35; 36; 37; 38]. Volatile acids are known to be inappropriate in alcoholic beverages [37]. Octanoic acid was detected in six samples. Principal component analysis (PCA) was performed using 56 volatile compounds detected in 6 or more samples to determine the overall distribution of volatile substances according to sample separation. The first principal component (PC1) showed 29.40% explanatory power, while the second principal component (PC2) showed 13.49%, as shown in Figure 1. The volatile compounds found on the positive side of PC1 (Figure 1) were ethyl lactate (es16), ethyl undecanoate (es41), ethyl dodecanoate (es56), 1-nonanol (all5), octanoic acid (ac2), nonanal (ad2), and benzenaldehyde (ad5). These compounds are considered to be the major compounds of the atmospherically distilled samples rather than the vacuum distilled samples, as also determined in other studies [7; 15]. Moreover, across the second principal component (PC2), there was a distinction among samples based on the major ingredients used for brewing. The sample brewed using only sweet potatoes (R40) was detected to have higher concentrations of esters such as ethyl 2-methylbutanoate (es6), propyl octanoate (es24), 2-methylbutyl octanoate (es35), and 2-decanone (ke2) along the positive side of PC2. By examining the sample distribution in Figure 1, MSO, an atmospheric pressure distillation product, showed a significant difference along the positive side of PC1, compared to the other samples. PJU and R40 made under atmospheric distillation were also positioned on the positive side of PC1. Most of the other samples except MBA, HBI, and CTO were distributed on the negative side of PC1, which were vacuum distilled samples. Across PC2, there was a distinct separation between R40 and MBA, which were manufactured using sweet potatoes or various grains such as sorghum and millet, respectively. As a result, this plot demonstrates that the composition of volatile compounds in these samples differed according to the raw materials and the distillation method.
# Study on the Driver Visual Workload of Bridge-Tunnel Groups on Mountains Expressways
## 2 Materials and Methods
### Experimental Scenario
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Name** & **Definition** \\ \hline Freeway tunnel group & A freeway tunnel group refers to a collection of tunnels where the distance between adjacent tunnel portals is less than 100 m [39]. It is a collective term for adjacent or continuous tunnels [40]. The vehicles entering the downstream tunnel will affect those traveling between downstream and upstream tunnels [41]. \\ \hline Adjacent tunnels & Adjacent tunnels comprise two tunnels with a distance between their portals of less than 250 m [36]. When the design speed is 80 km/h, the maximum spacing should be controlled within 110 m [42]. If the length of the connecting section between the tunnel groups is less than the stopping sight distance, drivers exiting the upstream tunnel may fail to brake promptly when they observe obstacles at the entrance of the downstream tunnel. The vehicles entering the downstream tunnel may affect those traveling inside the upstream tunnel [41]. \\ \hline Continuous tunnels & Continuous tunnels are two tunnels with a distance between them, ranging from 250 to 1000 m [12; 36]. At the design speed of 80 km/h, the distance between continuous tunnels should not exceed 377 m [42]. This distance ensures that drivers exiting the upstream tunnel can brake promptly when seeing obstacles at the entrance of the downstream tunnel during light adaptation [41]. \\ \hline Freeway bridge group & A bridge group is a road section with two or more bridges spaced within 1 km, among which at least one bridge has a length of 500 m or more. It describes a group of bridges spaced at a certain distance, typically on expressways [37; 43]. \\ \hline Bridge-tunnel group & A bridge-tunnel group is a section of road where the distance between the bridges and tunnels, tunnels, or bridges is less than or equal to the travel distance in 5 s at the designed vehicle speed [43]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of highway bridge group and tunnel group. mountainous expressways in the central and western regions of China. For this experiment, two tunnel-bridge groups on the Yakang Expressway were selected: the Shawan tunnel-bridge Group (Section A) and the Lahabahe tunnel-bridge Group (Section B). Section A has a total length of 28.77 km, with 9 tunnels (five extra-long tunnels and four long tunnels), and the average longitudinal gradient is 1.88%. The length of bridges and tunnels is 99.53% of the total length. Section B has a total length of 10.9 km, with seven tunnels including an extra-long tunnel, two long tunnels, three medium tunnels, and a short tunnel. Detailed information on these tunnels in the experimental sections is listed in Table 2. Typical experimental scenarios are shown in Figures 1-3.