esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Critical Views and Recommendations for Future Research  
This study is a comprehensive compilation of information on several types of steel columns encased in concrete. A thorough literature review on various steel configurations for use in bridge applications has shown a lack of real-world project experience and a variety of current systems and tests.  
In addition to emphasizing areas for development and improving construction techniques, this thorough review is essential for directing future research and promoting these technologies. CES technology opens the door for creative solutions in the industry and helps to navigate the difficulties of constructing bridges. A short list of the analytical parameters is provided, emphasizing how the analysis procedure uses them. The findings can be resumed as follows:  
1. Fiber element modeling is a reliable method in order to represent the seismic behavior of the CES columns.
2. Confined concrete could be presented as highly confined concrete or partially confined concrete depending on the position of the concrete in the CES columns.
3. The length of the plastic hinge should be calculated for the CES column considering the presence of the steel profile embedded in the section as well as the rebar ratio.
4. The configuration of the transversal reinforcement also has an impact on the resistance of the CES columns.  
Based on the model presented by [22], partially confined concrete could be modeled using the \(K_{p}\) coefficient factor and highly confined concrete could be modeled using the \(K_{h}\) coefficient factor in the CES column. However, based on the configuration and the shape of the steel section, these factors should be different.  
The durability and seismic performance of concrete-encased steel bridge piers should be the main topics of future research. Evaluating these construction techniques' sustainability from an economic and environmental standpoint is essential. Additionally, research ought to focus on developing more robust designs, especially for areas with strong seismic activity.  
It should be noted that the torsional behavior of the CES bridge piers has not been considered in this paper. There is very little research on the torsional behavior of the  
Figure 17: Damage to the column under cyclic loading, reprinted with permission from [26], 2010, Journal of Bridge Engineering.  
CES columns in the buildings only [48]. Primarily, the torsional mode is not dominant in the seismic behavior of the bridges. However, in irregular or skewed bridges, the torsion effect could be more important. For this reason, future research should focus on the torsional behavior of the CES bridge piers as well as their impact on the seismic behavior of the bridge.  
Future designs of concrete-encased steel columns will be greatly influenced by the analytical methodology discussed in this paper. Apart from the suggested configurations, the design parameters are also discussed. Bridges built with concrete-encased steel columns are more durable, leading to higher-quality structures.
# Seventy-Five Years since the Point-Contact Transistor: Germanium Revisited
## 4 Defect Processes in Ge
### Carbon and Oxygen Impurities  
During crystal growth and the processing of Ge wafers there is an introduction of defects in the Ge lattice. In particular, during the Czochralski crystal growth process there is a significant concentration of carbon and oxygen input in the Ge lattice that exceeds the equilibrium concentration of carbon and oxygen defects [33]. This is again analogous to the case for Czochralski-grown Si, with the important difference being that there is a higher content of carbon and oxygen defects that has implications on the defect processes [103; 104; 105; 106; 107]. Although there is an impact of these defects on the properties of donor and acceptor atoms in Si, there is only limited information in terms of Ge. [PERSON] et al. [44] determined that the diffusion of donor atoms is retarded in the presence of carbon atoms in Ge; however, there is no benefit for the deactivation of the donor atoms via the formation of D\({}_{\rm n}V_{\rm m}\) clusters. The related subsequent DFT results supported these findings by predicting that although carbon atoms result in higher migration energies for \(DV\) pairs via the ring mechanism of diffusion (and thus retard the diffusion of donor atoms), they do not impact the formation of extended clusters containing donor atoms and vacancies [45]. At any rate, the study of [PERSON] et al. [86] determined that there is efficient junction control using C co-implanted into P in pre-amorphised Ge.  
Considering the impact of oxygen interstitial atoms on the diffusion of \(p\)-type dopants such as B, it was recently determined by [PERSON] et al. [70] that a high oxygen concentration is linked with increased B diffusion. In particular, the diffusion of B was significantly enhanced in parts of the sample where the oxygen concentration exceeded \(10^{19}\) cm\({}^{-3}\) (i.e., it was orders of magnitude higher than the equilibrium dopant concentration) [70]. Nevertheless, such high oxygen concentrations could be possible from the quartz ampule used in the experiment and from oxidation near the surface [70]. This study prompted the DFT study by [PERSON] et al. [72], as it was anticipated that oxygen interstitials would interact with other acceptor dopants such as Ga. This study revealed that the binding energies required to form Ga substitutional-oxygen interstitial (O\({}_{\rm i}\)) defects are limited [72].
## 4 Proposed Method
### Fault Prediction Method
#### 4.3.3 Attention-MLGRU Algorithm Flow
Parameter update (Nadam optimizer):  
The Nadam optimizer uses the following formula to update parameters:  
\[m_{t}=\beta_{1}\cdot m_{t-1}+(1-\beta_{1})\cdot g_{t} \tag{26}\]  
\[\mathrm{z}_{\mathrm{t}}^{(1)}=\sigma\Big{(}W_{\mathrm{z}}^{(1)}x_{\mathrm{t}} +U_{\mathrm{z}}^{(1)}h_{\mathrm{t-1}}^{(1)}+V_{\mathrm{z}}^{(I)}c_{t}+b_{ \mathrm{z}}^{(1)}\Big{)} \tag{19}\]0  
\[\mathrm{z}_{\mathrm{t}}^{(1)}=\sigma\Big{(}W_{\mathrm{z}}^{(1)}x_{\mathrm{t}} +U_{\mathrm{z}}^{(1)}h_{\mathrm{t-1}}^{(1)}+V_{\mathrm{z}}^{(I)}c_{t}+b_{ \mathrm{z}}^{(1)}\Big{)} \tag{19}\]1  
\[\mathrm{z}_{\mathrm{t}}^{(1)}=\sigma\Big{(}W_{\mathrm{z}}^{(1)}x_{\mathrm{t}} +U_{\mathrm{z}}^{(1)}h_{\mathrm{t-1}}^{(1)}+V_{\mathrm{z}}^{(I)}c_{t}+b_{ \mathrm{z}}^{(1)}\Big{)} \tag{19}\]2  
\[\mathrm{z}_{\mathrm{t}}^{(1)}=\sigma\Big{(}W_{\mathrm{z}}^{(1)}x_{\mathrm{t}} +U_{\mathrm{z}}^{(1)}h_{\mathrm{t-1}}^{(1)}+V_{\mathrm{z}}^{(I)}c_{t}+b_{ \mathrm{z}}^{(1)}\Big{)} \tag{19}\]3  
where \(m_{t}\) and \(v_{t}\) represent the first moment estimate and second moment estimate of the gradient, respectively, \(g_{t}\) represents the gradient at the current moment, \(\beta_{1}\) and \(\beta_{2}\) are adjustable hyperparameters with a general value of 0 and 0.999, \(\alpha\) denotes the learning rate, \(\epsilon\) is a small number (such as \(1\times 10^{-8}\)) used for numerical stability. \(\hat{m}_{t}\) and \(\hat{v}_{t}\) are deviation corrections for the first and second moment estimates of gradients used to solve the deviation problem of the Adam optimizer. By introducing the correction term of Nesterov momentum, the first moment estimation of the gradient is more accurate. The weight parameters and bias terms in the above formula are updated according to the rules of gradient descent to minimize the loss function. The above steps are repeated for multiple rounds of iterative training until a predetermined stopping condition or convergence is reached. By using the attention mechanism and the Nadam optimizer, attention weights and learning rates can be adjusted adaptively in multi-layer GRU neural networks to optimize the updating process of network parameters.
###### Abstract  
The cable room, located at the base of the ring main unit, is prone to water vapor due to its proximity to damp cable holes and its relatively enclosed structure. This may penetrate internally and ultimately affect operational safety. Therefore, a dehumidifier was introduced to utilize dry air for internal circulation. To enhance the dehumidification in the cable room, the cable room device was designed for experimental research. Meanwhile, computational fluid dynamics (CFD)-electromagnetic (EMAG) coupling simulation is used to calculate the power loss of heat sources and their influence on multiple physical fields in numerical simulations. The feasibility of this study was confirmed by comparing the relative humidity, temperature, and velocity values between the experimental and numerical approaches. Furthermore, the layout of the ventilation pipes was modified to a vertical distribution, with upward supply and downward suction, to improve the airflow. The results indicate that the maximum relative errors in temperature, relative humidity, and velocity are only 3.61%, 7.14%, and 7.14%, respectively, which fall within an acceptable range. On this basis, additional simulation analysis was conducted on the humidity, dew point temperature, and airflow within the cable room, using an optimized model with a more comprehensive internal structure and cables. After implementing an optimized ventilation pipe layout, the relative humidity at the corresponding measuring points can decrease by up to 10.6%. The dew point temperature has decreased by 2.61 \({}^{\circ}\)C and the airflow has become more stable.  
dehumidification; cable room; CFD-EMAG coupling simulation; the layout of the ventilation pipes
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 4 Results  
To facilitate comparisons between the models, single-regime models were divided into two groups: group 1 includes the simplest models with two to three parameters, group 2 contains models that represent the relation v(k) with S-shaped curves. Finally, a group of two-regime models was distinguished. All models that meet criterion 1 unconditionally were cumulatively assessed for criteria 2, 3 and 4 (Table 5). **Group 1, single-regime models. ** The models do not give a correct representation of any of the traffic conditions. In free-flow traffic relative to the empirical fall, speed falls rapidly as density and flow increase. Where the flow is maximal, the estimated speed is significantly underrepresented (Figure 6). From the analyzed models, those not meeting the boundary conditions are [PERSON]'s (BC1, BC2), [PERSON]'s (BC2), and [PERSON]'s (BC2) models. The other models in the group show major errors in estimations and incorrectly estimate the values of nearly all boundary parameters (Table 5). **Group 2, single-regime models. ** Group 2 models offer a much better representation of the empirical relation with the S-shaped curve performing significantly better for characteristics of traffic flow both for free flow and congested traffic (Figure 7). The models by [PERSON], [PERSON], [PERSON] and [PERSON], and [PERSON] and [PERSON] do not meet BC2; hence, as set out in the assessment procedure (Figure 5), they are not included in further analysis. From those left, the two models by [PERSON] and [PERSON] in the chart are practically the same, and the estimated boundary parameters are almost identical (Table 5). The models that are the best at meeting the criteria and have the highest scores are [PERSON]'s and [PERSON] models. However, both do not seem to give a good enough representation of the area of maximal traffic (Figure 7). **Two-regime models. ** [PERSON]'s model is non-continuous, and the boundary parameters in free-flow and congested traffic are not consistent (Figure 8). As a result, BC3 is not satisfied. The triangular model does not represent the actual traffic patterns at all. In visual terms, [PERSON]'s and [PERSON]'s models perform the best with a relatively small error and an assessment of criterion 4 comparable with that of group 2 models. In the case where model simplicity is not a priority, both models can compete with the best single-regime models that have been analyzed. The results of the analyses show that group 2 single-regime models offer the best characteristics and give the best representation of real traffic conditions. However, none of them are able to correctly represent the area of maximal traffic, and the values they produce are too low for \(\text{q}_{\text{max}}\) and \(\text{v}_{\text{opt}}\) (Table 5). Given the situation, models that do not satisfy BC2 could be included in the analysis conditionally. They may be applied e.g., in methods for traffic condition assessment where our main focus is on free-flowing traffic before the road reaches its capacity.
## 4 UKF filtering processing  
In the section two, we attain the transfer function of the loop system by deduction. Since the system is non-linear, we cannot adopt linear system theory to analyze it. Furthermore, the DILL cannot eliminate the Gaussian noise effectively and has a relatively larger error to track with poor stability. In order to keep rapid, accurate tracking of mobile targets effectively, we adopt UKF filtering processing to filter the receipt signals, solving the defect of nonlinear distribution in the statistical property by using the UT transformation into Kalman filtering to attain the more accurate approximation of mean value and covariance of the signals.  
After the Kth target signal source passes the DILL, the signal sampling points become more intensive as the orientation angle \(\hat{\theta}_{k}\) fluctuates around the \(\theta_{k}\). Set \(x_{k}(\hat{\theta}_{i})\) is sample output of Kth signal, for simplicity, this paper defines the signal as \(X_{i}=x_{k}(\hat{\theta}_{i})\) and the specific process of UKF filtering as follows:  
Assume the state equation and measurement equation of the non-linear discrete system is respectively [14]:  
\[\begin{cases}X_{i+1}=F(X_{i},v_{i})\\ Y_{i}=H(X_{i},n_{i})\end{cases} \tag{17}\]  
Where \(X_{i+1},X_{i}\) is the state vector at the sampling instant of \(i+1,i\) while \(Y_{i}\) is observation vector, and \(v_{i},n_{i}\) is the noise vector. Then we estimate the \(X_{k}\) by UKF filtering.  
(a) initialize the output variable \(X_{i}\) of DILL:
## 3 Framework
### Offline Phase  
We train a bipartite GNN-based policy stable predictor \(\pi_{\theta}\) following previous work [27], where \(\pi\) denotes the policy as a function and \(\theta\) represents the set of trainable parameters. The policy is designed to learn patterns from specific operational scenarios, enabling the prediction of variable stability for the UCP under unseen but similarly distributed scenarios.  
\(\mathcal{D}=\{(\mathbb{G},stable)\}\) denotes the training set. The policy is a three-layer structure shown in Figure 4 and is described as follows:  
* Input layer: This layer, consisting of a regularization layer, fully connected layers, and activation functions, inputs the embedding \(\mathcal{V}^{0}=\{v_{1}^{0},\ldots,v_{n}^{0}\}\), \(\mathcal{C}^{0}=\{c_{1}^{0},\ldots,c_{m}^{0}\}\), where \(v_{i}^{0}=v_{i}\), \(c_{j}^{0}=c_{j}\) will be embedded into a higher-dimensional latent vector \(\mathcal{V}^{1},\mathcal{C}^{1}\).
* Convolutional layer: The information of neighboring nodes \(c_{j}^{l}\in\mathcal{C}^{l}\) and \(v_{i}^{l}\in\mathcal{V}^{l}\) is transitioned after a multilayer convolutional layer and takes the following form: \[c_{j}^{l}\gets f_{c}(c_{j}^{l-1},layernorm(\sum_{i}^{(i,j)\in E}f_{vc}(v _{i}^{l-1},c_{j}^{l-1},e_{ij})))\] (15) \[v_{i}^{l}\gets f_{v}(v_{i}^{l-1},layernorm(\sum_{j}^{(i,j)\in E}f_{cv}(v _{i}^{l-1},c_{j}^{l-1},e_{ij})))\] (16) where \(f_{c},f_{v},f_{vc},f_{cv}\) are Multilayer Perceptrons with ReLU activation functions, and _layernorm_ represents the layer normalization function.
* Output layer: This layer consists of fully connected layers and a sigmoid activation function, which maps the output to a scalar in the range \([0,1]\), representing the stability confidence of each variable \(\pi_{\theta}(\mathbb{G})=\{p_{1},\ldots,p_{k}\}\) and \(p_{i}\in[0,1],\mathrm{i}\in\{1,\ldots,k\}\). Depending on the task setting, we can selectively retain the stability predictions of specific types of binary variables--for example, only the commitment variables, or all binary variables. The remaining variables are discarded from further consideration.  
Similar to the selective retention in the output layer, the training labels are also selectively clipped. Specifically, we retain only the stability indicators corresponding to certain variables as training labels, represented as \(stable\in\left[0,1\right]^{k}\), while discarding the labels for the remaining variables. Given that the labels are continuous values bounded within \([0,1]\), the Binary Cross-Entropy (BCE) loss function is employed, which is ideally suited for such probabilistic labels. The loss function is defined as follows:  
\[L(\theta)=\frac{-1}{|\mathcal{D}|}\sum_{(\mathbb{G},stable)\in\mathcal{D}}\sum _{i=1}^{k}[stable_{i}\cdot\log(p_{i})+(1-stable_{i})\cdot log(1-p_{i})] \tag{17}\]  
Figure 4: Architecture of the bipartite GNN policy \(\pi_{\theta}\).
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies
### Hydrate-Storage Designs  
After formation of the hydrate, there is a need to store the hydrate in container which can provide the required pressure and temperature in the stability range of the hydrate. Although no actual working plant for hydrogen storage in hydrate exists, there are some conceptual designs suggested to store hydrogen in gas hydrate. [PERSON] et al. suggested the idea of storing hydrogen in a sII hydrate inside a large-capacity underground silos. The hydrogen can supplied from various factories in the industrial zone close to  
Figure 10: High-pressure optical cell (HPOC) for spectroscopy studies.  
the hydrate-production and storage plant. Figure 11 represents an underground storage tunnel where the formed hydrate can be stored. These tunnels are equipped with cooling systems, high-pressure storage tanks, and the facility to load/unload the formed hydrate granules [66].  
Figure 12 also represents another design for storing the formed hydrates in above-ground storage tanks suggested by [PERSON] et al [67]. In this design, to sustain the temperature of the tank under 0.11 MPa hydrogen pressure at 140 K, a gas-circulation system is employed to chill down the hydrogen by LNG vaporisation. This tank is equipped with a screw-conveyer for unloading the hydrate while the fresh hydrate can be loaded from the rotary stacker on the top of the tank. The hydrate can be supplied from a neighbouring hydrate-production plant, and, in response to demand, unloaded from the tank to a neighbouring regassification plant. It is worth mentioning that, due to the high diffusivity of hydrogen, and to avoid any leakage, the major portion of the facilities is a cylindrical triple-wall tank insulated by perlite concrete blocks [67].  
Table 1 summarizes the features of discussed reactors for hydrate formation and their potential in hydrogen storage. The designs with water dispersion was found to be more suitable for hydrogen storage due to the formation of small pieces of hydrates which will provide sufficient free surfaces for hydrogen uptake or release. Many designs are  
Figure 11: Layouts of storage tunnels using deep shafts.  
Figure 12: Schematic of hydrogen-hydrate storage facilities composed of a cylindrical, above-ground storage tank and a refrigeration system with hydrogen.  
dispersing gas in a large volume of water, which is useful for many other applications, such as water treatment or dewatering. Indeed, the formation of a big slabs of hydrate will make it unsuitable for hydrogen storage application, and so an extra step of hydrate crushing is being used for gas adsorption/desorption applications.
###### Abstract
[21] used augmented learning to predict dissolved oxygen (DO) content in wireless sensor networks and demonstrated the advantages of machine learning in optimizing sensor network deployment and data acquisition strategies. The authors of the above studies explored the significant development of the application of machine learning algorithms combined with remote sensing and sensor technology in water quality monitoring. However, both remote sensing technology and sensor technology have advantages and disadvantages: sensor technology provides high accuracy and real-time monitoring capabilities; however, it is constrained by limited coverage and high deployment and maintenance costs [22]. Remote sensing technology can realize large-scale water quality monitoring; however, its accuracy and real-time performance may be limited by weather conditions and the complexity of data processing [23]. The authors of future studies will need to continue to optimize models, improve data processing capabilities, and address current challenges in remote sensing monitoring to achieve comprehensive monitoring and effective management of water quality. Through the continuous optimization of technology and integration of different monitoring methods, water resources can be more effectively protected, improving environmental quality. The main objectives of this paper are as follows: (1) to establish a new hybrid model by combining the newly fused attention mechanism, custom residual blocks, and convolutional neural networks; discuss the applicability of this model structure in the prediction of different water quality parameters; and compare and analyze with other neural network models to verify its effectiveness. (2) The same model was used to invert turbidity, permanganate, ammonia nitrogen, and dissolved oxygen, and then R\({}^{2}\), RMSE, MAPE, and RPD were used as evaluation bases to evaluate the effect of water quality parameter inversion by different models. (3) The spatial variation maps of four water quality parameters were drawn to comprehensively evaluate the water quality of the Yellow River basin in Ningxia, China.
## 3 Results and Discussion
### Antioxidant Activity of Plant Extracts: DPPH and Reducing Power Assays  
In the present study, we selected five different fruits in addition to a native Australian fruit, the Kakadu plum (_T. ferdinandiana_), to examine their antioxidant potential. Various assays are available to quantify antioxidant capacity and, from these, we chose DPPH and the potassium ferricyanide reducing power assay to evaluate the antioxidant potential of our samples, expressed as gallic acid equivalents (GAE) and ascorbic acid equivalents (AAE). These assays are used widely to determine the antioxidant properties of compounds as free radical scavengers or hydrogen donors [50], and the results are shown in (Table 4)  
The results of the DPPH assay revealed that the antioxidant capacity of the fruits ranged from 1.16 \(\pm\) 0.02 to 239.7 \(\pm\) 8.17 mg/g, expressed as GAE, and varied from 2.47 \(\pm\) 0.05 to 124.4 \(\pm\) 4.34 mg/g, expressed as AAE. Similarly, the reducing power assay recorded that, in comparison with all fruit samples, KKD-NT and KKD-WA showed the highest antioxidant properties, ranging from 6.65 \(\pm\) 0.44 to 179.6 \(\pm\) 5.35 mg GAE/g DW and from 14.08 \(\pm\) 0.83 to 74.4 \(\pm\) 2.06 mg AAE/g DW. The IC50 value was calculated to determine the concentration of the sample required to inhibit 50% of the free radical. The lower the IC50 value, the higher the antioxidant activity of samples [51]. The half-maximal inhibitory concentration (IC50) value of the KKD-NT extract was 170.6 \(\upmu\)g/mL, whereas KKD-WA showed an IC50 value of 272.9 \(\upmu\)g/mL. We examined whether the scavenging capacities for KKD-NT and KKD-WA were extended dose-dependently (Figures 1 and 2) and the KKD-NT extract displayed potent antioxidant and reducing power activities compared with those measured for the KKD-WA extract and the other fruits (pomegranate, lemon, orange, blueberry, and raspberry). According to a previous study, it was also proven that the Kakadu plum contains a higher level of antioxidant potential in comparison to blueberries as strong fat-soluble antioxidants are present in the Kakadu plum [51].