esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Immobilized Lipase
### Coimmobilization  
Some works have proposed to use coimmobilization of enzymes. The advantage is that the first enzyme releases the product that is transferred to the next coimmobilized enzyme with a short diffusional distance. This is especially relevant for lipases due to their specificity for triacylglycerglycerols ester bonds. The mixture of 1,3-specific lipase and a non-specific lipase enhances the global activity because it removes the limiting acyl-migration step. Several coimmobilized systems have been studied for biodiesel production like _Candida rugosa_ lipase and _Rhyzopus oryzae_ lipase simultaneously on silica gel [74], _Candida antarctica_ lipase B and _Thermomyces lanuginos_ lipase, on the surface of the _Pichia pastoris_ cell [57], and _Rhizomucor miehei_ and _Candida antarctica_ lipases on epoxy-functionalized silica [67, 100].  
Lipases are coimmobilized on the same material surface in order to get better global activity and improved enzyme specificity and selectivity for hydrolysis of triacylglycerors as well as those generated diacylglycerols and monoacylglycerols that must be further hydrolyzed [96]. However, the most active enzyme may get some loss of activity through this procedure [125]. A different coimmobilization strategy was proposed by immobilizing several lipases layer-by-layer using abcoating with polyethylenimine [126]. Other authors have used coating with PEI/glutaraldehyde to form 5 enzyme layers of lipases A and B from _Candida antarctica_, lipases from _Rhizomucur miehei_, and _Thermomyces lanuginos_ and phospholipase Lecitase Ultra [125]. Although it gives an innovative way for fats hydrolysis some problems might arise either from inhibition by coating agents used, high costs of different lipases used or steric hindrance for accessibility of triacylglycercols to the active site of lipases immobilized in the inner layers.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 1 Introduction
As shown in Figure 2, the content encoders learn to extract content features from unpaired clean and raw images, while the blur and noise encoders capture blur and noise information from low-quality raw images. Figure 1: Workflow of proposed OSE image enhancement method: unpaired raw and clean OCT images are used as the input of an unsupervised learning strategy based on disentangled representation and GAN. This process allows the one-step enhancer (OSE) to learn to extract content from low-quality OCT images and generate clean images. The next sections of this paper are organized as follows: in Section 2, we explain related work, including GAN-based deblurring, GAN-based denoising, and disentangled representation. In Section 3, we describe our proposed method, including the problem formulation, definition of loss functions, method implementation and assessment method. In Section 4, we present experiments and results. Finally, conclusions are presented in Section 5.
# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 4 Experimental Results
### Experimental Setup  
The experimental setup included Ubuntu 20.04 OS, an Intel i9-10920X CPU, 32 GB of RAM, and an NVIDIA GeForce RTX 3070 graphics card. Python 3.8 was employed as the programming language, while Pytorch 1.8.0 was utilized as the deep learning framework.  
During the training phase, the hyperparameters were configured as follows: the initial learning rate was set to 0.0031, the decay factor was set to 0.12, the momentum was set at 0.826, the batch size was defined as 16, and the number of training epochs was established at 300.
# Evaluating Resilience and Thermal Comfort in Mediterranean Dwellings: A Level(s) Framework Approach
## 2 Materials and Methods
### Assessment of Time Outside of Thermal Comfort Range for Elderly People
#### 2.5.1 Determining Thermal Comfort for the Elderly  
Research indicates that the thermal comfort zone for seated or quiet adults, typically between 20 \({}^{\circ}\)C and 24 \({}^{\circ}\)C, is inadequate for older adults, who generally prefer a higher comfort temperature than the normotype [28]. Several studies have estimated that the optimal thermal comfort range for older individuals lies between 24 \({}^{\circ}\)C and 32 \({}^{\circ}\)C [29]. As people age, their metabolism slows down, physical activity becomes more sedentary, and muscle mass decreases, significantly reducing the energy metabolic rate, ranging from 37 W/m\({}^{2}\) to 40 W/m\({}^{2}\)[30]. Based on these data, an energy metabolic rate of approximately 0.7 met has been established for older adults (compared to 1.0 met for quiet adult men and 0.85 met for quiet adult women), where 1 met equals 58.2 W/m\({}^{2}\).
# Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
## 5 Result and Discussion
### Experimental Localization Results for the Double Vibration Source
#### 5.2.1 Same Frequency Multi-Source Vibration Localization Results  
Figure 10a-f shows the multi-source dipole two-dimensional localization results by different data segmentation and feature extraction methods, respectively. From the localization error statistics in Table 4, the localization error of the spheres closer to the artificial lateral line (ALL) is much smaller than the localization error of the spheres farther away from the ALL, regardless of the combination method. Comparing MLP-7, MLP-9, and MLP-11, the MED of the closer spheres decreases from 0.6153 to 0.3162 cm and then decreases to 0.1749 cm as the segmentation increases (i.e., the amount of data becomes more extensive). This result indicates that the neural network fits better as the data increase. Similar results are obtained when comparing MLP-8, MLP-10, and MLP-12.  
Although increasing the dimensionality of the feature by extracting the amplitude of the multiplier can reduce the error, for example, from the localization results of MLP-9 and MLP-10, the MED of the more distant sphere plummets from 2.4308 to 0.8741 cm. However, the localization error of the more distant globe from the ALL is generally significant, indicating that the network does not accurately localize the more distant sphere. This is mainly because both dipoles vibrate at 8 Hz, making the signal from the more distant blob quickly drowned out by the call from the closer chunk. The MLP network input is the amplitude feature, which makes the features of the more distant sphere not effectively represented.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **MAE-x\({}_{1}\)(cm)** & **MAE-y\({}_{1}\)(cm)** & **MED\({}_{1}\)(cm)** & **MAE-x\({}_{2}\) (cm)** & **MAE-y\({}_{2}\) (cm)** & **MAD\({}_{2}\)(cm)** \\ \hline MLP-7 & 0.3378 & 0.4813 & 0.6153 & 0.6119 & 2.1918 & 2.3547 \\ MLP-8 & 0.1727 & 0.2428 & 0.3147 & 0.3877 & 0.8551 & 1.0042 \\ MLP-9 & 0.1475 & 0.2565 & 0.3162 & 0.4415 & 2.3220 & 2.4308 \\ MLP-10 & 0.1303 & 0.1391 & 0.2188 & 0.3786 & 0.7005 & 0.8741 \\ MLP-11 & 0.1160 & 0.1156 & 0.1749 & 0.4525 & 2.7091 & 2.8520 \\ MLP-12 & 0.1155 & 0.1151 & 0.1784 & 0.2853 & 1.4828 & 1.5730 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean absolute error of the results in FIG-10.  
Figure 10: Experimental localization results of MLP in two dimensions. (**a**) Localization results of MLP-7. (**b**) Localization results of MLP-8. (**c**) Localization results of MLP-9. (**d**) Localization results of MLP-10. (**e**) Localization results of MLP-11. (**f**) Localization results of MLP-12.
## 6 Discussion
### Stiffness Coefficients Identification Using a GA  
The proportional coefficients of sidewall stiffness sensitive with the inflation pressure are identified utilizing a GA with the modal prediction error of different inflation pressure as the object value. The optimization procedure is initialized with the same parameters list in Section 5 and the identification results of the identification procedure is presented in Figure 16.  
Figure 16 presents the compared object value of flexible ring on an elastic continuous foundation tire model and flexible beam on an elastic continuous sidewall beam tire model. Additionally, Figure 16 shows the stiffness coefficients of the sidewall spring model of the identification procedure and the Figure 16c illustrates the radial and tangential stiffness of the sidewall spring model with a different inflation pressure. Table 3 lists the identified stiffness coefficients of the sidewall spring model, which is sensitive with the inflation pressure. Figure 17 presents the compared modal resonance frequency (inflation pressure: 0.7, 0.6, 0.5, 0.4, and 0.3 MPa) between the experimental modal resonance frequency, analytical modal resonance frequency, respectively utilizing the flexible ring on an elastic continuous foundation tire model and flexible beam on an elastic continuous sidewall beam tire model. Figure 16: Stiffness coefficients identification procedure; (**a**) compared object value of flexible ring on an elastic continuous foundation tire model and flexible beam on an elastic continuous sidewall beam tire model; (**b**) stiffness coefficients of the sidewall spring model; (**c**) radial and tangential stiffness of the sidewall spring model with a different inflation pressure. Figure 17: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Symbol** & **Unit** & **Value** & **Symbol** & **Unit** & **Value** \\ \hline \(AA\) & / & \(-6.371\) & \(CC\) & / & \(-1.537\) \\ \(BB\) & / & \(-1.451\) & \(DD\) & / & \(-1.244\) \\ \hline \end{tabular}
\end{table} 
Table 3: Identified stiffness coefficients sensitive to inflation pressure. The identification results (Figures 16 and 17 and Table 3) imply that:  
1. The object value of the identification procedure (Figure 16a) is convergent to the steady point with the optimization procedure proceeding over 65 generations;
2.
## 4 Experiments
### Ablation Study
#### 4.5.1 The Effect of GTM  
In order to evaluate the effectiveness of each module, we first introduce the GTM module for experiments, which can generate global information to help us obtain more accurate results. As shown in Table 5, without the GTM, DFE and edge detection are denoted as a baseline model B. The baseline model achieves 51.54% mIoU. Adding GTM to the baseline model can be named as B + T; the result shows a gain of mIoU by 2.01 points. It indicates that the self-attention mechanism in the GTM module can acquire global information via long-range dependencies to assist the human parsing task. Compared with baseline model B, the performances on some classes which are usually adjacent and have easily confused appearances (e.g., upper-clothes and j-suits) gain nearly 1% and 6% mIoU, respectively. The dress and skirt boost by 5.26% and 7.25% in terms of mIoU. As can be seen, the performance on some small targets of the scarf and glove reach 20.07% and 40.08%, respectively. Since the long-range semantic information can provide more discriminated features, the GTM model is beneficial for recognizing large-size objects.  
We set multiple layers of encoder in the GTM. To explore the optimal encoder layers, we design four variants with different encoder layers. The best performance when encoder layers are set to 4, we find that more encoder layers do not yield better performance.
## 2 Related Work
### Rule-Based Methods  
The majority of rule-based methods require linguists to choose language traits and manually create rule templates. Pattern and string matching can be used to identify entities. These techniques rely on knowledge bases and domain dictionaries, which are quick and easy to use. According to the literature [19], because complicated rules are written using domain expert knowledge, only a small amount of training data is required to provide high-performance entity recognition results. A novel Chinese Electronic Medical Record (EMR) entity recognition model with a domain lexicon and rules has been proposed in the literature [20]. In the task of Chinese EMR entity recognition, this method outperforms other methods. The method presented in [21] uses regular expressions to build grammatical lexical rules based on Urdu language features, and the results show that the rule-based entity recognition approach outperforms the statistical learning method. This approach, however, has a limited range of applications and poor generalization capabilities, necessitating the creation of features for many domains by experts, and it has constraints that make creating rules difficult. As a result, most rule-based methods are combined with other named entity methods to improve entity recognition performance.
# Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI
## 3 Energy-Efficient Multicast Precoding for Massive MIMO
In particular, without loss of optimality, the energy-efficient multicast transmit precoding optimization problem in (9) can be simplified to the following problem  
\[\underset{\boldsymbol{\Lambda}}{\arg\max} \frac{WR_{\text{mc}}\left(\boldsymbol{\Lambda}\right)}{P\left( \boldsymbol{\Lambda}\right)},\] \[\text{s.t.} \text{tr}\left\{\boldsymbol{\Lambda}\right\}\leq P_{\text{max}}, \ \boldsymbol{\Lambda}\succeq\boldsymbol{0},\ \boldsymbol{\Lambda}\ \text{diagonal}, \tag{11}\]  
in which  
\[R_{k} =\mathsf{E}\left\{\log\det\left\{\mathbf{I}_{N_{k}}+\frac{1}{ \sigma^{2}}\mathbf{H}_{k}\mathbf{Q}\mathbf{H}_{k}^{H}\right\}\right\}\]0 \[R_{k} =\mathsf{E}\left\{\log\det\left\{\mathbf{I}_{N_{k}}+\frac{1}{ \sigma^{2}}\mathbf{H}_{k}\mathbf{Q}\mathbf{H}_{k}^{H}\right\}\right\}\]1 (13) \[R_{k} =\mathsf{E}\left\{\log\det\left\{\mathbf{I}_{N_{k}}+\frac{1}{ \sigma^{2}}\mathbf{H}_{k}\mathbf{Q}\mathbf{H}_{k}^{H}\right\}\right\}\]2  
In the objective function of the beam domain power allocation problem (11), \(R_{\text{mc}}\left(\boldsymbol{\Lambda}\right)\) and \(P\left(\boldsymbol{\Lambda}\right)\) are concave and linear functions with respect to \(\boldsymbol{\Lambda}\), respectively. Thus, the optimization problem in (11) is a concave-linear fractional program. [PERSON]'s transform and Charnes-Cooper transform are two classic approaches to solve the concave-linear fractional program [11]. In this work, we adopt [PERSON]'s transform to solve this optimization problem. Compared to Charnes-Cooper transform, [PERSON]'s transform has an advantage that no extra constraints must be included. Via invoking [PERSON]'s transform, the energy-efficient precoding optimization problem in (11) can be solved via iteratively solving the following sequence of convex optimization problems  
\[R_{k} =\mathsf{E}\left\{\log\det\left\{\mathbf{I}_{N_{k}}+\frac{1}{ \sigma^{2}}\mathbf{H}_{k}\mathbf{Q}\mathbf{H}_{k}^{H}\right\}\right\}\]3 \[R_{k} =\mathsf{E}\left\{\log\det\left\{\mathbf{I}_{N_{k}}+\frac{1}{ \sigma^{2}}\mathbf{H}_{k}\mathbf{Q}\mathbf{H}_{k}^{H}\right\}\right\}\]4  
where \(\eta^{(\ell)}\) is an introduced auxiliary variable in [PERSON]'s transform, which is iteratively updated as follows  
\[R_{k} =\mathsf{E}\left\{\log\det\left\{\mathbf{I}_{N_{k}}+\frac{1}{ \sigma^{2}}\mathbf{H}_{k}\mathbf{Q}\mathbf{H}_{k}^{H}\right\}\right\}\]5  
with \(\ell\) denoting the iteration index. It can be shown that the above [PERSON]'s transform-based iteration approach is guaranteed to converge to the global optimum of the original optimization problem in (11) with a super-linear convergence rate [11]. Although each transformed sub-problem in (15) is a convex program, the optimization complexity might still be high in practice due to the sample average in calculating the expectation operation. To reduce the implementation complexity in Monte-Carlo averaging over channel realizations, we further employ the large-dimensional random matrix theory [27; 28] to calculate the DE of the ergodic rate.
## 1 Introduction
The payloads and key technologies are briefly described in Section 4. The on-orbit operations for radiometric calibration traceability and the balloon-based demonstration experiments are introduced in Sections 5 and 6, respectively. To date, Phase A has been completed. The prototypes, including an absolute radiance standard IR calibrator based on ITS-90 miniature phase-change temperature points, a RSB self-correction absolute calibrator based on Spontaneous Parametric Down-Conversion (SPDC) principles with eight spectral bands spanning 450-1000 nm, and a cavity-type absolute cryogenic radiometer (ACR) with 20 K operational temperature, were built together with the corresponding radiometric scale transfer chains. In this exploratory phase, an uncertainty better than 0.15 K and 0.3% was achieved for all of the three benchmark calibrators. Based on these promising results, Phase B of the CSRB project was started in 2018 and the engineering model is ongoing.
## 3 Results and Analysis
### Combined Pumping Test  
No. 1 and No. 3 wells combined pumping test. No. 1 Q = 100 m\({}^{3}\)/h. No. 2 well artesian flowed. The dynamic water level of No. 3 was 2.58 m, Q = 100 m\({}^{3}\)/h, and q = 12.4 m\({}^{3}\)/h\(\cdot\)m. The total water output was 132 m\({}^{3}\)/h, and the daily water output was 3168 m\({}^{3}\)/d.  
No. 1 and No. 2 wells combined pumping test. No. 1 Q = 100 m\({}^{3}\)/h. The dynamic water level No. 3 was 3.1 m. The dynamic water level No. 2 well was 4.85 m. Q = 70 m\({}^{3}\)/h, and q = 4.4 m\({}^{3}\)/h\(\cdot\)m. The total water output is 170 m\({}^{3}\)/h, and the daily water output is 4080 m\({}^{3}\)/d.  
No. 2 and No. 3 wells combined pumping test. No. 1 well artesian flowed. The dynamic water level of No.2 was 0.85 m, Q = 24 m\({}^{3}\)/h, and q = 28.2 m\({}^{3}\)/h\(\cdot\)m. The dynamic water level of No. 3 was 2.50 m, Q = 79 m\({}^{3}\)/h, and q = 31.6 m\({}^{3}\)/h\(\cdot\)m. The total water output was 103 m\({}^{3}\)/h, and the daily water output was 2472 m\({}^{3}\)/d.  
According to the pumping test results presented in Table 2, three wells were regarded as one well for analysis. The rock between 21.0-67.4 m was broken and fracture was developed. It was the main aquifer with a thickness of 46.4 m. The total pumping time was 96 h, the water temperature was 61 \({}^{\circ}\)C, the total water inflow was 6731 m\({}^{3}\)/d, the maximum drawdown was 15 m, the water flow per unit width was 18.7 m\({}^{3}\)/h\(\cdot\)m, the hydraulic conductivity was 7.38 m/h, and the coefficient of transmissivity was 14.28 m\({}^{2}\)/h. After stopping pumping, it returned to artesian state in 34 min. It showed that in the fault fracture zone, the rock was broken, the fracture was developed, and the water permeability and water yield were very strong.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 2 Theoretical Foundations
### Knowledge Graphs  
Knowledge Graphs are structured representations of information. They comprise the three main components: entities, relationships, and semantic descriptions. Entities are the primary objects within the graph. They represent different items, such as real-world objects, e.g., people. Relationships represent the connections between the different entities. They define the relations and, therefore, provide the structure of the Knowledge Graph. The semantic description contains the properties and characteristics of the different entities, e.g., the name and gender of a person [23]. Knowledge Graphs are generally used in scenarios, where very accurate and explicit knowledge is required and the relationship between the entities is crucial [24]. In an LLM context, they can enhance the understanding of the data, since they provide knowledge concerning inference and interpretability. The representation of relationships provides a reasonable and accessible understanding for humans and, therefore, provide a lot of value. Neo4j [25] is a NoSQL, open-source graph database that uses Knowledge Graphs for data representation. Graph databases are databases that utilize the concept of knowledge graphs in their data representation and storage [26]. The advantage of Neo4j is the ability to represent the relationships between the entities bidirectional.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 2 Methods
### Evacuation Simulation
#### 2.5.2 Evacuation Parameters  
Evacuation parameters include evacuee distribution, evacuation speed, response time, warning distance, and congestion conditions.  
The resident population within Dukezong is 525, but this increases to approximately 9800 during peak tourist periods. Residents are distributed across non-hotel and non-restaurant buildings, while tourists are placed in hotels, attractions, and restaurants basedon Points of Interest (POI) data. Outside the district, 4538 evacuees are in the emergency shelter catchment (Figure 1c). Population distribution is proportional to building area, with a minimum of one person per building.  
All evacuations are assumed to occur on foot due to traffic restrictions in Dukezong, with an average speed of 1.24 m/s [58]. Speed reduction due to crowding is automatically set in AnyLogic.  
Warning distance refers to the distance between the point at which fire danger is detected and the buildings when individuals decide to evacuate. If the distance is too short, evacuees inside a dead-end alley may not be able to safely evacuate if a fire occurs at one end. In Dukezong, a minimum distance of 75 m is established. Additional warning distances of 150 m, 300 m, and 600 m are also examined.  
Response time, defined as the delay after the decision to evacuate, is set at 3 min, based on the fire resistance of buildings [59].  
Congestion is defined as crowd density exceeding 4 people per square meter for a duration exceeding 3 min [60].
## 2 Physical Model  
The considered 1D model consists of a gadolinium slab with thickness \(L\), thermally insulated on all sides except one, allowing horizontal heat flow into an idealized isothermal external medium. The slab starts the process at \(T_{i}\) = 320 K, and cools down until it reaches a steady-state temperature of \(T_{\infty}\) = 260 K (external medium temperature), assuming a constant convection heat transfer coefficient of \(h\) = 1100 W m\({}^{-2}\) K\({}^{-1}\) (typical free convection coefficient value for water).  
We've explicitly derived a discretization of the spatial nodal temperature from the energy balance equation. The nodal temperatures of each designated surface point were obtained using Equations (1)-(3), for each time instant \(t+n\Delta t\), starting from \(t\), where \(n\) represents the time index and \(n+1\) the next instant. As the slab cools down, it releases heat to the external surroundings triggering fluctuations in both temperature dependent \(C_{p}\) and \(k\), along the material's control volume nodes.  
Node 1:  
\[\begin{split} T_{1}^{(n+1)}&=\left[\frac{2k_{12}\Delta t }{\rho_{1}C_{1}\Delta x^{2}}\right]T_{2}^{n}\\ &+\left[1-\frac{2k_{12}\Delta t}{\rho_{1}C_{1}\Delta x^{2}}-\frac {2h\Delta t}{\rho_{1}C_{1}\Delta x}\right]T_{1}^{n}\\ &+\left[\frac{2h\Delta t}{\rho_{1}C_{1}\Delta x}\right]T_{\infty }\end{split} \tag{1}\]  
Node 2:  
\[\begin{split} T_{2}^{(n+1)}&=\left[\frac{k_{12}\Delta t }{\rho_{2}C_{2}\Delta x^{2}}\right]T_{1}^{n}+\left[\frac{k_{23}\Delta t}{\rho_ {2}C_{2}\Delta x^{2}}\right]T_{3}^{n}\\ &+\left[1-\frac{k_{12}\Delta t}{\rho_{2}C_{2}\Delta x^{2}}-\frac {k_{23}\Delta t}{\rho_{2}C_{2}\Delta x^{2}}\right]T_{2}^{n}\end{split} \tag{2}\]  
Node 3:  
\[T_{3}^{(n+1)}=\left[\frac{k_{23}\Delta t}{\rho_{3}C_{3}\Delta x^{2}}\right]T_{ 2}^{n}+\left[1-\frac{k_{23}\Delta t}{\rho_{3}C_{3}\Delta x^{2}}\right]T_{3}^{n} \tag{3}\]  
where \(T\) is the temperature in each spatial node, \(h\) the heat transfer coefficient, \(\rho\) the material's density, \(C\) the specific heat, \(k\) the thermal conductivity, and \(\Delta x\) represents the control volume length. To determine thermal conditions more accurately near the surface, the system's outer node 1 (the interface point between the slab and the external medium shown in Figure 1) has been assigned to the control volume with thickness of \(\left(\frac{\Delta x}{2}\right)\). This control volume acts as a gateway for all the internal heat flowing outwards.