esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 2 Related Work  
In this section, we review a series of related works for predicting Km by using machine learning and deep learning approaches, which are summarized in Table 1.  
[PERSON] et al. [31] used EC numbers and biological types as inputs for a linear regression model to predict Km values and tested the model prediction performances through cross-validation. [PERSON] et al. [32] predicted Km values using a multilayer perceptron and optimized cellobiose production. [PERSON] et al. [33] utilized a multilayer perceptron and the physicochemical properties of substrate molecules to improve the accuracy of Km prediction in CYP450-mediated reactions. [PERSON] et al. [34] proposed the Machine Learning-Aided Global Optimization (MLAGO) algorithm, which predicts Km values by incorporating EC number, compound ID, and organism ID as inputs. The above methods based on traditional machine learning manually designed input features, which limits the prediction performances and practical applications.  
In recent years, some deep learning-based methods have been proposed for predicting Km values, due to their powerful nonlinear fitting capabilities and feature extraction abilities. [PERSON] et al. [35] proposed MPEK, which utilizes a protein pre-trained language model called ProtT5 [36] and a molecule pre-trained model called Mole-BERT [37] to extract features from enzyme and molecular sequences, respectively. [PERSON] et al. [38] developed EITLEM, which employs a protein pre-trained language model called ESM-1v to extract the features of enzyme sequences and incorporates substrate molecule information and attention mechanisms. The UniKP model developed by [PERSON] et al. [39] uses a protein pre-trained model called ProtT5-XL-UniRef50 and the SMILES Transformer model as upstream feature extractors and adopts an ensemble model generated by random forest and extreme trees algorithms as the downstream prediction module.  
However, as shown in Table 1, existing Km prediction approaches based on deep learning only use enzyme-substrate pairs as inputs, which limits the feature extractions of entire enzymatic reactions for Km predictions. To address this issue, this study constructed a new enzymatic reaction dataset including product information and developed a DLERKm model utilizing the information of substrates, products, and enzymes in enzymatic reactions as inputs to predict Km values.
# Assessment of DPPC Liposome Disruption by Embedded
###### Abstract  
In this study, the effect of \(\alpha\)-tocopherol malonate (TM) on physical and structural properties of DPPC liposomes was investigated using ANS fluorescence, DPH, and TMA\(-\)DPH anisotropy fluorescence and differential scanning calorimetry (DSC) methods. The presence of embedded TM in DPPC liposomes caused alteration in its phase transition temperatures, structural order, dynamics, and hydration of head groups increasingly with growing TM concentration. The ANS fluorescence results demonstrated that increasing TM presence in the DPPC gel phase due to interrupted membrane structure caused the formation of new binding sites. Temperature investigations in the range of 20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C showed that increasing temperature rises ANS fluorescence which reaches local and global maxima at 36 \({}^{\circ}\)C and 42 \({}^{\circ}\)C, respectively. The rising TM concentration at the phase transition temperature of DPPC led to the lowering of ANS fluorescence, indicating a decreased binding of ANS. Simultaneously, during heating, a roughly 10-nm shift of ANS emission maximum was observed. The results indicated that in the fluid phase, the observed quenching appears as a result of increasing accessibility of water molecules into ANS in this region. The DPH results indicated that in the gel phase presence of TM introduced disorder in the hydrophobic acyl chain region led to its fluidization. The TMA\(-\)DPH results indicated an increasing disorder in the interface region and an increasing hydration of head group atoms at the surface of the membrane. The increasing concentration of TM results in the formation of multicomponent DSC traces, suggesting the formation of another structural phase. The applied methods proved that the incorporation of TM into DPPC membrane results in the interaction of malonate moiety with DPPC head group atoms in the interphase layer and induces the interruption in the membrane packing order, leading to its structural changes. The presented results show that TM presence could regulate the membrane properties, thus it may indicate one of the possible mechanisms responsible for the effective disruption of cell membranes by TM. The knowledge of molecular mechanism how TM interacts with the membrane will help to elucidate its possible pharmacological activity.  
DPPC liposomes; main phase transition; DSC; ANS fluorescence; DPH and TMA\(-\)DPH fluorescence anisotropy;tocopherol derivatives
## 5 Performance Analysis
### Organization's Encryption Time  
We considered a different number of attributes and authorities to measure the organization's encryption time, as shown in Figure 3.  
* Figure 3 shows the encryption time according to the number of attributes while considering multiple concurrent access requests to the same authority. The results show that: **i.** The increase in the average encryption time is about 0.0127 seconds when increasing the number of attributes from 4 to 32 while considering 300 concurrent customers; and **ii.** The increase in the average encryption time is about 0.0092 seconds when increasing the number of concurrent customers from 100 to 300 while considering 32 attributes.
* Figure 3 indicates the encryption time according to the number of authorities while considering multiple concurrent access requests to the same authority. The results show that: **i.** The increase in the average encryption time is about 0.0826 seconds when increasing the number of authorities from 4 to 32 while considering 300 concurrent customers; and **ii.** The increase in the average encryption time is about 0.0045  
Figure 3: The encryption time in milli-seconds while considering 100, 200, and 300 access requests. Where (**a**) is the owner’s encryption time against the number of considered attributes and (**b**) is the owner’s encryption time against the number of considered authorities.  
seconds when increasing the number of concurrent customers from 100 to 300 while considering 32 authorities.  
Finally, it is clear that the encryption time is reasonable considering the huge stream of concurrent requests on the same authority. Thus, it is accepted by \(OiC\). The average encryption time is 0.018 seconds while considering the very tough conditions and huge traffic overhead. This average time is very small and accepted by the data owners.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
###### Abstract  
Smart cities and smart buildings must provide their customers with many services, including those associated with health, productivity, and energy efficiency, among others. Short-range wireless systems can provide all of these services, but the significant growth of wireless networks operating within a smart building (SB) can produce the phenomenon of spectrum shortages. Spectrum shortages could be resolved using Cognitive Radio (CR)-based systems to improve the efficiency of electromagnetic spectrum use by taking advantage of the reusable spectrum available in the building's interior. This study proposes a mechanism using two interference conditions to quickly estimate the minimum amount of effective spectrum availability (ESA) inside an SB. The results show that an SB contains ESA distributed across 36% to 98% of the building's area for reuse, as a function of the height of the building and of the distance from the base station (BS) of the primary system.  
c pacs: 9
## 3 Results and Discussion
### Changes in OA Composition during Fermentation of BBP-Meju with Different Salt Concentrations  
OAs are closely related to the flavor and nutrition of various fermented foods and have been shown to play an essential role in BBP-meju fermentation [44]. The dynamic changes in malic acid, citric acid, succinic acid, lactic acid, tartaric acid, and acetic acid during BBP-meju fermentation with different salt concentrations are shown in Figure 3. The levels of total OAs in BBP-meju with different salt concentrations increased and reached 28.17 \(\pm\) 1.43 mg/g (L), 22.72 \(\pm\) 0.18 mg/g (M), and 16.69 \(\pm\) 1.02 mg/g (H), respectively, on day 49 (Figure 3b). During the fermentation, the levels of total OAs in BBP-meju increased with salt reduction (Figure 3b). This result was consistent with the study by [PERSON] et al. [4]. Specifically, malic acid, citric acid, and succinic acid were the major OAs in high-salt meju. In contrast, malic acid, lactic acid, and citric acid were the major OAs in medium- and low-salt meju (Figure 3a). Malic acid, citric acid, and succinic acid were known to be key acidulants in BBP fermentation, imparting a natural sour flavor to BBP and inhibiting the growth of undesirable microorganisms, thereby prolonging the shelf life [45]. The average content of lactic acid was 7.77 \(\pm\) 0.18 mg/g (L), 5.55 \(\pm\) 0.04 mg/g (M), and 0.95 \(\pm\) 0.05 mg/g (H) at the three salt concentrations, showing significant variation with salt concentration (Figure 3c). Meanwhile, the average content of acetic acid increased significantly from 0.56 \(\pm\) 0.01 mg/g under high-salt conditions to 2.38 \(\pm\) 0.03 mg/g under low-salt conditions (Figure 3c). _Lactobacillus_ has been reported to convert oxaloacetate and pyruvate to acetic or lactic acid [4]. Therefore, the high levels of lactic acid and acetic acid in low-salt meju may be related to the accumulation of _Lactobacillus_ in the fermentation system.  
Figure 3: OAs during fermentation of BBP-meju with different salt concentrations: (**a**) content of OAs; (**b**) relative abundance of OAs; (**c**) average content of OAs in BBP-meju samples with different salt concentrations. Where letters a, b, and c indicated the significant differences (_p_ < 0.05).  
Figure 2: FAAs during fermentation of BBP-meju with different salt concentrations: (**a**) content of amino acids; (**b**) relative abundance of amino acids; (**c**) average content of amino acids in BBP-meju samples. Asterisks indicate a statistically significant difference from the control group (* \(p\) \(\leq\) 0.05, *** \(p\) \(\leq\) 0.01).  
Based on these results, FAAs and OAs exhibited continuous changes during the BBP-meju fermentation. Hence, the samples from days 7 and 35 were further selected for subsequent analysis.
## 4 Results and Discussion
### Crack Reconstruction for ECPT in Successive Scanning Mode  
As mentioned above, using ECPT in a successive scanning approach, different segments of surface cracks can be detected by sequentially moving the coil to different positions. Considering that only one segment of each crack can be recognized in an IR image sequence captured at a certain coil position, it is necessary to reconstruct a crack with all segments in different IR images. Here, Crack I is taken as example to illustrate the process of crack reconstruction. In order to reconstruct the entire shape of Crack I, the original IR images obtained under five coil positions are cut into some image patches. These patches containing the distinct segments features of Crack I are then selected to put together in sequence, which finally obtains the reconstructed image of Crack I, as shown in Figure 9a. As a comparison, the images reconstructed by using the PCA processing method are also used to reconstruct Crack I in the same approach above and the integrated image of Cracks I is shown in Figure 9b. It is found that the whole shape of Crack I is quite rough and broken in Figure 9a, but Figure 9b shows more distinct features of Crack I. It demonstrates the effectiveness of the PCA method on the enhancement of detectability for ECPT. Compared with the true shape of Crack I shown in Figure 4c, we can find that Crack I has been reconstructed in a more continuous and precise manner, as shown in Figure 9b. Therefore, with the help of crack reconstruction, we can obtain the necessary information to characterize the whole crack based on the detailed detection results of crack segments, which further prove the feasibility of using ECPT to detect surface cracks on the matrix of PDC bit in the proposed successive scanning mode. However, there is still a shielding effect from the annular coil in Figure 9b, which interferes with the complete shape restoration of the crack.  
Figure 8: Image reconstructed by (**a**) the first principal component and (**b**) the second principal component.  
The problem may be solved by designing a new coil with special geometry, which will be achieved in our future investigation.
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 3 System Model
### Optimization Problem  
From Equation (8), we can see that the scheduling result of subtasks from different users can also affect the overall completion time. Thus, we define \(\mathcal{S}_{i,j}^{k}=\{s_{i,j}^{k},\forall k\in\{i\}\cup\mathcal{M},\forall i \in\mathcal{N},\forall j\in\ V_{i}\}\) to denote the scheduling order of subtasks. Our aim is to find a suitable task offloading decision \(\mathbf{D}\), duplication scheme \(\mathbf{I}\) and a scheduling result \(S_{i,j}\) for all the subtasks from different users to minimize total latency while satisfying the energy constraints. The duplication-based energy-aware task offloading problem can be formulated as:  
\[\mathbf{Opt}:\min_{\{l_{i,j},d_{i,j},s_{i,j}\}}(\sum_{i=1}^{N}t_{i,|\varphi_{i}|}^{F}))\forall i\in M\ \forall k\in\mathcal{N}\] (12) \[\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
Given an application DAG, as shown in Figure 2, we utilize the average execution time \(\overline{t_{i,j}}\) to label the value on each node and use the average transmission \(\overline{C_{i,j}}\) to mark the weight on each link. Furthermore, the average earliest start time \(\overline{t^{ES}_{i,j}}\) can be computed recursively by traversing the DAG downward, starting from the entry node, where the \(\overline{t^{ES}_{i,1}}=0\):  
\[\overline{t^{ES}_{i,j}}=\max_{l\in pred(j)}\{(\overline{t^{ES}_{i,l}}+\overline {t_{i,l}}+\overline{C_{l,j}}\} \tag{14}\]  
The average latest start time \(\overline{t^{LS}_{i,j}}\) can be obtained recursively by traversing the DAG downward, starting from the exit node, where the \(\overline{t^{ES}_{i,|p_{i}|}}=\overline{t^{LS}_{i,|p_{i}|}}\):  
\[\overline{t^{LS}_{i,j}}=\max_{k\in succ(j)}\{(\overline{t^{LS}_{i,k}}-\overline {C_{j,k}}\}-\overline{t_{i,k}} \tag{15}\]  
After we obtained \(\overline{t^{ES}_{i,j}}\) and \(\overline{t^{LS}_{i,j}}\) from these equations, shown in Table 3, according to the DAG in Figure 2, the DAG is divided into a set of sub-tree. The root of each sub-trees is defined as a critical node (CN), whose \(\overline{t^{ES}_{i,j}}\) is equal to the \(\overline{t^{LS}_{i,j}}\). The critical nodes are shown by the thick edges connection them, which are node \(a\), node \(c\) and node \(e\). The list algorithm starts with critical node (node \(a\)) with the minimum value of average earliest time and if the node has parent nodes, assign the highest priority to them. Then, we assign the priority to this critical node. By repeating the above steps, we can obtain the rank of all the nodes shown in Figure 2.
## 2 Complexity and Algorithms
### ILP Formulation  
Next, we describe an integer linear program (ILP) formulation for Pattern-Guided \(k\)-Anonymity employing the ideas behind the fixed-parameter algorithm of Theorem 3. More specifically, our ILP contains the integer variables \(x_{i,j}\) denoting the number of rows from type \(i\) being assigned into an output row type compatible with pattern vector \(j\). The binary variable \(u_{j,l}\) is \(1\) if instance \(l\) of pattern vector \(j\) is used in the solution; that is, there is at least one row mapped to it, otherwise, it is set to \(0\). Furthermore, \(n_{i}\) denotes the number of rows of type \(i\), \(\omega_{j}\) denotes the costs of pattern vector \(j\), and \(k\) is the required degree of anonymity. Let \(\hat{p}_{i}\leq t\) denote the number of instances of pattern vector \(i\), and let \(c(i,j,l)\) be \(1\) if mapping row \(i\) to pattern vector \(j\) produces pattern vector instance \(l\), otherwise \(c(i,j,l)=0\). With this notation, we can state our ILP formulation:  
\[\min\sum_{i=1}^{t}\sum_{j=1}^{p}x_{i,j}\cdot\omega_{j} \tag{5}\] \[\sum_{i=1}^{t}c(i,j,l)\cdot x_{i,j}\leq u_{j,l}\cdot n \begin{array}{c}1\leq j\leq p\\ 1\leq l\leq p_{j}\end{array}\] (6) \[\sum_{i=1}^{t}c(i,j,l)\cdot x_{i,j}+k\cdot(1-u_{j,l})\geq k \begin{array}{c}1\leq j\leq p\\ 1\leq l\leq p_{j}\end{array}\] (7) \[\sum_{j=1}^{p}x_{i,j}=n_{i} 1\leq i\leq t. \tag{8}\]  
The goal function (5) ensures that the solution has a minimum number of suppressions. Constraint (6) ensures that the variables \(u_{j,l}\) are consistently set with the variables \(x_{i,j}\); that is, if there is some positive variable \(x_{i,j}\) indicating that the instance \(l\) of pattern vector \(j\) is used, then \(u_{j,l}=1\). Constraint (7) ensures that every pattern vector instance that is used by the solution contains at least \(k\) rows. Constraint (8) ensures that the solution uses as many rows from each row type as available.  
We remark that, as Theorem 3, our ILP formulation also yields fixed-parameter tractability with respect to the combined parameter \((t,p)\). This is due to the famous result of [PERSON] [29], and the fact that the number of variables in the ILP is bounded by \(O(tp)\). Theorem 3; however, provides a direct _combinatorial_ algorithm with better worst-case running time bounds. Nevertheless, in the experimental section, we decided to use the ILP formulation and not the combinatorial algorithm based on the experience that there are very strong (commercial) ILP solvers that, in practice, typically perform much better than the worst-case analysis predicts.
## 3 Proposed Algorithms Explanation
### Cooperative Localization Free Energy Efficient Routing Protocol (Co-LFEER)
#### 3.2.2 Selection of Destination and Relay Nodes and Data Forwarding.  
The selection criteria of the DSN in the Co-LFEER protocol is the same as for the LFEER protocol. The protocol describes the DSN and RLNs having the same parameters for selection: residual energy, number of hops and bit error rate. The source node selects the only one RLN and the DSN to transmit the information bags in the direction of the final DNN. When the source nodes send the information packets in the broadcast nature, all nodes which are in the field of the source nodes receive the data packets. Then, only one DSN is chosen. Based on the function values, the RLNs are selected. A node is chosen as a RLN which has the second highest value of the cost function. The first highest function value is assigned to the DSN. The overall selection and data forwarding mechanism is provided in Algorithm 1.  
```
1\(Z\):Number of Nodes
2\(B_{t}\):Bit Error Rate Threshold
3\(S_{n}\):Sink Node
4\(DSN\):Destination
5\(RLN\):Relay Node
6\(R_{e}\):Residual Energy
7\(n\):A Number of Hops
8\(BER\):Bit Error Rate
9\(R\):Total Number of Rounds
10\(i\):Starting Number
11 for\(i\) = 1:1:Z do
12 Data Reached= false
13 while(Data Reached)
14 if(Sink is Available)
15 Data Reached= true
16 elseif(Sink is not Available)
17 if\(R_{e}\)>0
18 Find the Nearby Nodes
19 Find the \(DSN\) Amongst the Nearby Nodes
20 The \(DSN\) Found on
21 function (f) = \(R_{e}\) / \(n\times BER\)
22 For the First Highest function (f) value
23 Forward the Data
24 if\(BER\leq 0.5\)
25 Data Accepted
26 else
27 Find the \(RLN\) for the Second Highest Value of the function (f)
28 Forward the Data
29 break
30 end if
31 end if
32 end if
33 end while
34 end for
```  
**Algorithm 2****Selection of Nodes and Data Forwarding.**
## 1 Introduction
To increase the confidence of practitioners and researchers in the ability of FDS to capture tunnel airflow, the average airflow velocity and velocity profile in the Dartford Tunnel (UK) were simulated using FDS 6 and then compared with in situ measurements [12]. The tunnel is 1430 m long with a circular cross-section with a diameter of 8.5 m. Although it was modeled as a tunnel with a square cross-section, the results correlate well with the measurements. This research demonstrates the ability of FDS to simulate jet fans and the airflow they create. In [13,14], the airflow generated by jet fans with a relatively small diameter, which are popular in car park ventilation systems, was investigated. The suitability of different implementations of the turbulence model in the FDS 6 system for simulating such airflow was also discussed. The research confirmed the FDS 6's ability to simulate airflow velocities generated by jet fans in larger enclosures. An important issue related to the modeling of airflow in the tunnel is the effect of reducing the thrust efficiency of the jet fan. This effect was experimentally studied in [15,16]. According to [17], such losses due to momentum transfer and high shear stress of the wall can reach up to 20-30%. Installing the jet fans close to walls causes increased wall shear stress around the jet. In [17], the results of two full-scale measurements of different jet fan installations were investigated in order to analyze the efficiency of the installation. These measurements were used to design and validate a numerical model. In [18], an FDS 6 model of the 898 m long Polana tunnel (Slovakia) was developed and its ability to simulate airflow generated by emergency ventilation was tested. The model was validated using a full-scale in situ ventilation test. A method of adjusting the parameters of the tunnel walls was proposed, reflecting the effect of the reduction in the thrust efficiency of the jet fans and the FDS limitations related to the resolution and type of the numerical mesh. One of the three tested ventilation operation modes was used to set up the model, and then the data from the entire ventilation test were used to validate the model with respect to time-averaged steady-state airflow velocities. A comparison with the experimental data confirmed the very good accuracy of the simulation results of the time-averaged velocity measured by a grid of anemometers located in the tunnel cross-section, but pointed to a relatively lower accuracy of the simulation for the tunnel anemometers. In this paper, we analyze the simulation results presented in [18], propose an improved representation of tunnel anemometers and contribute to a better validation of the model. A substantial improvement in the modeling accuracy of the airflow velocity determined by tunnel anemometers is demonstrated. The data from the ventilation test are used to validate the improved model with respect to the average velocity and the time-averaged velocity of the steady-state ventilation-generated airflow measured by grid and tunnel anemometers.