esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 3 Experimental Cases
### Device Model and Objective Function
#### 3.2.1 Chiller Model  
The coefficient of performance of the cooler can be expressed as follows:  
\[COP=\frac{r}{\left(\frac{t_{0}+273.15}{t_{0}+273.15}-1\right)r+z_{1}\frac{t_{0 }+273.15}{t_{0}+273.15}-z_{2}} \tag{1}\]  
where r is the load rate of the chiller, representing the condensing temperature (\({}^{\circ}\)C); tv is the evaporating temperature (\({}^{\circ}\)C); and z\({}_{1}\) and z\({}_{2}\) are the parameters to be determined.  
\[t_{o}=t_{w\rho,E}+\frac{Q_{o}}{c_{w}G_{w\rho}(1-e^{-\frac{UU_{o}}{c_{w}G_{w \rho}}})} \tag{2}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Equipment Type** & **Parameters** & **Quantity** \\ \hline Chiller & Rated cooling capacity 2040 kW, rated & \\  & power 361.7 kW & 2 \\ Chilled water pump & Rated flow 466 m\({}^{3}\)/h, rated power 55 kW & 3 \\ Cooling water pump & Rated flow 466 m\({}^{3}\)/h, rated power 45 kW & 3 \\ Cooling tower & Motor power: 11 kW & 2 \\  & Flow rate: 900 m\({}^{3}\)/h & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equipment parameters.  
Figure 14: Schematic diagram of the HVAC system composition.  
\[t_{v}=t_{w,v,E}+\frac{Q_{v}}{c_{w}G_{w,v}(1-e^{-\frac{U\alpha_{v}}{c_{w}c_{w}c_{w}}})} \tag{3}\]  
\[Q_{o}=c_{w}G_{w,\rho}(t_{w,\rho,E}-t_{w,\rho,L}) \tag{4}\]  
\[Q_{v}=c_{w}G_{w,v}(t_{w,v,E}-t_{w,v,L}) \tag{5}\]  
Here, \(c_{w}\) is the specific heat capacity of water. \(t_{o}\) and \(t_{v}\) are determined by the inlet water temperature of the chiller and the heat exchange between the condenser and the evaporator. \(t_{o}\) is determined by the outlet temperature of cooling water \(t_{w,o,E}\) (\({}^{\circ}\)C) and the cooling water flow rate \(G_{w,o}\) (kg/s). \(t_{v}\) is determined by the outlet temperature of the chilled water, \(t_{w,v,E}\) (\({}^{\circ}\)C), and the chilled water flow rate \(G_{w,v}\) (kg/s). \(t_{w,o,L}\) (\({}^{\circ}\)C) is the inlet temperature of the cooling water, and \(t_{w,v,L}\) (\({}^{\circ}\)C) represents the inlet temperature of the chilled water. \(U_{Ao}\) and \(U_{Av}\) represent the total heat-transfer coefficients (W/\({}^{\circ}\)C) of the condenser and evaporator, respectively.  
The power of the chiller is a function of the cold load, chilled water outlet temperature, and chilled water flow rate as the input parameters.  
\[P_{chiller}=\frac{Q_{e}}{COP} \tag{6}\]  
\(Q_{e}\) represents the chilled load (kW) and is calculated based on the last chilled water flow (\(G_{w,e}\)) and supply and return water temperature (\(t_{w,v,e}\), \(t_{w,v,L}\)) difference, as follows:  
\[Q_{L}^{T}=c_{w}G_{w,e}^{T-1}(t_{w,v,E}^{T-1}-t_{w,v,L}^{T-1}) \tag{7}\]
## 2 Problem Formulation
### Voltage Stability Index (VSI)  
The evaluation of static voltage stability requires the calculation of a significant metric known as the voltage stability index (VSI), which serves as an appropriate indicator of the proximity of the power system to voltage collapse. Various techniques exist for calculating this index, including the VSI-index proposed in [15]. From Figure 1:  
\[\big{(}V_{i}\angle\theta_{i}\big{)}^{*}\cdot I_{i,i+1}=P_{i}-jQ_{i} \tag{9}\]  
Figure 3: Vector representation of voltage and current phasors.  
Based on Equations (9) and (1), we obtain the following:  
\[V_{i}^{2}-V_{i}\cdot V_{i-1}+\sqrt{\left(P_{i}^{2}+Q_{i}^{2}\right)\cdot\left(r_{ i,i-1}^{2}+x_{i,i-1}^{2}\right)}=0 \tag{10}\]  
The roots of Equation (10) are real if:  
\[V_{i-1}^{2}-4\cdot\sqrt{\left(P_{i}^{2}+Q_{i}^{2}\right)\cdot\left(r_{i,i-1}^{ 2}+x_{i,i-1}^{2}\right)}\geq 0 \tag{11}\]  
Using the previous equation, the stability index for the \(i\)th bus is derived as follows:  
\[VSI_{i}=V_{i-1}^{4}-4\cdot(P_{i}x_{i,i-1}-Q_{i}r_{i,i-1})^{2}-4\cdot(P_{i}r_{i,i-1}+Q_{i}x_{i,i-1})^{2}\cdot V_{i-1}^{2}\geq 0 \tag{12}\]  
The stability threshold of this index varies from one to zero. The bus with the minimum value is the one most sensitive to voltage collapse.
## 5 Proposed Heuristic
### Stage III: Constructing the Routing Tree  
The final stage of our proposed heuristic is about constructing the routing tree for each RP. Our emphasize in this process is constructing a balanced routing tree in which the sensor-nodes can contact with the RP with less amount of hops. As explained in Algorithm 3 the sensor-nodes which participate in the voting process start to broadcast a special message called \(\alpha\) to the rest of sensor-nodes, in Line 2. When a sensor-node receives the \(\alpha\) message(s) from other sensor-nodes, it follows the closest  
Figure 7: A communication range with more than two RPs in which the sojourn location(s) should be selected.  
sensor-node. When all of the sensor-nodes could reach to their related RP, from Lines 15-22 a process begins that identifies triangles between sensor-nodes as illustrated in Section 4.2.2 in the Constrain (5) and removes the triangles in the network.  
```
Input: i) The set of all the sensor-nodes that can contact with RPs, directly, \(\mathrm{Y}=\{v_{1},...,v_{w}\}\); Output: i) Routing Tree for each RP;
1 for\(j=1\) to \(W\)do
2 Broadcast a message to all of the neighbor-nodes \(v_{j}\);
3 end for
4 for\(j=1\) to \(|S-(W+\Delta)\)do
5 if\(s_{j}\) receive message \(\alpha\)then
6 Select the sensor-node with closest distance and follow it;
7 Add the set \(\varphi\);
8
9 end if
10
11 end for
12 for\(j=1\) to \(|S-(\varphi+W+\Delta)\)do
13 if\(s_{j}\) received a message then
14 Choose the closet sensor-nodes with \(\alpha\) message;
15
16 end for
17
18 end for
19 for\(j=1\) to \(S-|W+\Delta|\)do
20 for\(k=1\) to \(S-|W+\Delta|\)do
21\(j\
eq k\) ;
22 if\(s_{j}\) can contact with \(s_{k}\) directly AND \(s_{j}\) can contact with the next node of \(s_{k}\) directly AND \(s_{k}\) is not RPthen
23 Make \(s_{j}\) to contact with the next node of \(s_{k}\);
24
25 end if
26
27 end for
```  
**Algorithm 3**Constructing Balanced Routing Tree
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Ablation Study  
#### 4.4.1 Contribution of Each Module  
To thoroughly evaluate the individual contributions and overall synergistic effects of each module in the proposed algorithm, we conduct tests on the DICM, MEF, and NPE datasets. Qualitative and quantitative assessments are set up for scenarios where the TCE-L module acts alone, the TCE-C module acts alone, the MFF module is removed (without MFF module, -w/o MFF), the TCE-L module is removed (without TCE-L module, -w/o TCE-L), and the TCE-C module is removed (without TCE-C module, -w/o TCE-C). Based on the results presented in Figure 16 and Table 6, the TCE-L module notably enhances the brightness of low-light images, but it sacrifices some edge details and amplifies noise to some extent, rendering the image's detail hierarchy slightly blurred. Furthermore, the image experiences significant color distortion, resulting in lower PSNR and SSIM values. The TCE-C module effectively boosts the contrast of low-light images, but over-enhancement leads to severe overexposure, making the image appear unnatural and compromising its quality, giving higher BRISQUE and PIQE values. Processing without the MFF module, while being capable of combining the strengths of both the TCE-L and TCE-C modules, tends to overexpose the bright areas of the original image, particularly the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learning** & **Datasets** & **LOLv1** & \
\begin{tabular}{c} **LOLv2-** \\ **Real** \\ \end{tabular} &
\begin{tabular}{c} **DICM** \\ **Synthetic** \\ \end{tabular} & **DICM** & **MEF** & **NPE** & **ExDark** & **Avg** \\ \hline \multirow{2}{*}{Conventional} & MSRCR & 0.496 & 0.499 & 0.493 & 0.492 & 0.492 & 0.492 & 0.492 & 0.494 \\  & Dong & 0.497 & 0.489 & 0.511 & 0.540 & 0.488 & 0.631 & 0.504 & 0.523 \\ \hline \multirow{2}{*}{Supervised} & KinD++ & 0.503 & 0.498 & 0.501 & 0.487 & 0.539 & 0.456 & 0.501 & 0.498 \\  & RetinexNet & 0.504 & 0.500 & 0.492 & 0.496 & 0.510 & 0.566 & 0.500 & 0.510 \\  & Retinexformer & 0.505 & 0.494 & 0.506 & 0.515 & 0.519 & 0.531 & 0.510 & 0.511 \\ \hline \multirow{4}{*}{Unsupervised} & EnlightGAN & 0.499 & 0.499 & 0.517 & 0.526 & 0.537 & 0.494 & 0.513 & 0.512 \\  & Zero-DCE++ & 0.495 & 0.493 & 0.509 & 0.546 & 0.539 & 0.487 & 0.504 & 0.511 \\  & CLIP-LIT & 0.491 & 0.495 & 0.508 & 0.529 & 0.499 & 0.601 & 0.504 & 0.518 \\  & NeRCo & 0.512 & 0.502 & 0.515 & 0.525 & 0.549 & 0.533 & 0.505 & 0.520 \\  & Colie & 0.518 & 0.511 & 0.519 & 0.508 & 0.453 & 0.490 & 0.508 & 0.501 \\  & Ours & 0.493 & 0.492 & 0.491 & 0.477 & 0.509 & 0.436 & 0.497 & 0.485 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparison results of (LOLv1, LOLv2-real, LOLv2-synthetic, DICM, MEF, NPE, and ExDark) datasets using the BRISQUE metric. A lower value of the BRISQUE metric indicates better performance. The first, second, and third best scores are highlighted with red, blue, and green colors, respectively.
## 4 Discussion and Conclusions
It is, thus, recommended to train the employee on how to properly manage sensitive data of mobile devices, ways to respond to any security breaches, and the understanding of BYOD policy. This study found that employees' cost-benefit consideration of mobile security practices is strongly affected by the information security culture of the organization. The culture is affected by various factors, among which include the training conducted, organizational policies and enforcement, and responsibilities assigned. It is imperative that organizations develop a culture that prioritizes ethical tone [68], information security, and policy compliance via appropriate encouragements, enforcement, and sanctions. Based on the findings in [PERSON] and [PERSON] [66], it could be deduced that compliance efforts that are linked to job security could be worth considering. We also support the recommendations forwarded by [PERSON] et al. [69] that organizations should consider the elements of people, policy management, and technology in BYOD program implementation, which is applicable to mobile security practice. Ordinal logistic regression was implemented to explore the relationship between the demographic and work-related data of the employees and their mobile security practice. The analysis result revealed that the variables \"Gender\", \"Job function\", \"Past security experience\", and \"Perceived risk\" are significantly associated with the mobile security practice of the employees. Analyses of parameter estimates have shown that being female (gender1), prior security incidents (incident1), and perceiving information security incidents as quite likely (risk4) negatively influence mobile security practice, while the job function of security and risk management (jobfunction7) has a positive influence on mobile security practice. From the test of parallel lines, the proportional odds assumption was satisfied for the ordinal logistic regression model, which means the coefficients of independent variables are the same across all different categories of the dependent variable.
## 2 Materials and Methods
### Identification of Endophytic Yeast  
The endophytic yeast (Ec2) genus was identified using molecular techniques, with the internal transcript spacer regions (ITS1 and ITS4) as markers. For this purpose, the yeast was inoculated on cellophane paper, which was on the PDA medium in the Petri dishes. The culture was incubated for 3 days at 22 \({}^{\circ}\)C. The yeast was then recovered from the cellophane paper, and the genomic DNA was extracted using the CTAB method with the modifications described by [PERSON] et al. [14]. The ITS sequences were amplified using the ITS-1 and ITS-4 primers described by [PERSON] et al. [21]. Forward primer: ITS-1 5\({}^{\prime}\)-TCCGTAGGTGAAACCTGCGG-3\({}^{\prime}\)and reverse primer: ITS-4 5\({}^{\prime}\)-TCCCTCCGCTTATTGTATAGC-3\({}^{\prime}\). The PCR reaction was performed as in [PERSON] et al. [14]. PCR products were sequenced by the Unidad de Secuenciacion y Tecnologias Omticas (Pontificia Universidad Catolica de Chile, Santiago, Chile). Fungal ITS region sequences from Ec2 were manually edited using Geneious Prime(r) 2020.0.4 software. The consensus nucleotide sequence was submitted to the GenBank NCBI database with the accession number OP880438.1. This sequence was then aligned with the sequences present in the database using the Basic Local Alignment Search Tool (BLASTn) to search sequences that presented high similarity with them. Considering the sequences with the highest score, new sequences of strain types were selected for different species of the genus _Naganishia_, and a sequence from _Filobasidium oriense_ was used as the outgroup. Geneious Prime(r) 2020.0.4 software was used to prepare a dendrogram after an alignment of the sequence using the clustal W algorithm and Tamura-Nei genetic distance model with the neighbor-joining method. The resampling for dendrogram was 1,000 bootstrap, and the support threshold was 50% [22].
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 3 Research Methods
### The Network Structure of YOLOv7-Tuna
#### 3.1.2 CBS-C Module  
To enhance the detection accuracy of the model, the CoordConv [23] is introduced into the head network. Firstly, the 1 \(\times\) 1 convolution within the CBS module is replaced with CoordConv to implement the CBS-C module, as illustrated in Figure 3. Secondly, the RepConv [24] in the three target detection heads is replaced with CoordConv to enhance the spatial positional awareness of the target detection heads.  
The structure of CoordConv is depicted in Figure 4. Figure 3(a) illustrates traditional convolution, where H, W, and C denote the height, width, and channels of the traditional convolution, respectively, which are mapped to H\({}^{\prime}\), W\({}^{\prime}\), and C\({}^{\prime}\). Figure 3(b) represents CoordConv, which achieves mapping by incorporating additional channels containing coordinate information. One channel is used to represent the i coordinate, and another channel is used to represent the j coordinate. Concretely, the i-coordinate channel is an \(h\times w\) rank-1 matrix with its first row filled with 0's, its second row with 1's, its third with 2's, etc. The j-coordinate channel is similar, but with columns filled in with constant values instead of rows. Additionally, a linear transformation is applied to the i and j coordinates, normalizing them within the range of [\(-\)1, 1]. For two-dimensional convolution, the two coordinates (i, j) can represent any input pixel.  
Figure 3: (a) CBS module. (b) CBS-C module.  
Figure 2: (a) ELAN module. (b) ELAN-DS module.  
The introduction of coordinate information into the input feature map implies that each pixel contains its coordinates relative to the center of the image. By introducing coordinate information, CoordConv can provide the network with additional spatial positional information, thereby enhancing the network's spatial structural awareness. Traditional convolutional layers treat features at different positions equally, whereas CoordConv adjusts the weights of the convolutional kernels based on the coordinates of the pixels. This enhances the network's ability to perceive positional information, aiding in learning more generalizable feature representations. Therefore, our proposed CBS-C module enhances the network's spatial awareness and ability to handle positional information, resulting in improved accuracy in predicting target positions and shapes, as well as enhancing the network's ability to learn generalized features.
## 4 Experiment
### Error-Free Data Transmission Test for Optical-Acoustic Cooperative Communication  
It can be seen from the previous experimental results that the bit error rate performance of the underwater optical communication system was greatly affected by the receiver's offset angle, and the effective receiving field angle of the receiver without bit error was small. Through the CSR-ARQ optical-acoustic cooperative communication protocol, error-free data transmission can be realized under the condition of only establishing a one-way optical communication link, which reduces the requirements for alignment accuracy of the optical communication system.  
We carried out field experiments in a laboratory pool. In the experiment, the optical communication transmission rate was set to 2 Mbps, and the transmission file was a picture with a size of about 783 kB. Ten groups of transmission experiments were carried out, and the ROV was relocated to a new position in each experiment. The experimental results are shown in Figure 18.  
Figure 17: The received signal strength varies with the communication distance.  
Figure 18: The throughput test results of each error-free data transmission scheme.  
According to the data in Figure 18, the throughput of the system was higher after adopting the CSR-ARQ protocol. The throughput of the SR-ARQ solution was relatively low, which we could also predict from the analysis of the simulation. We counted the number of frames sent by the transmitter and the number of frames received by the receiver in the first 5 s of each program experiment and analyzed the current packet loss rate of each group of experiments. It was found that the packet loss rates of the third, fifth, and eighth groups were close to 0. In this case, all ARQ protocols were almost ineffective, so the throughput of each scheme was almost the same. Table 3 shows the statistical results of the packet loss rate of group 6 and group 9. The packet loss rates of both groups were less than 10%, and the experimental results were close to the simulation results.  
The experimental results show that the underwater optical-acoustic cooperative communication system designed in this paper can realize the error-free data transmission of a 2 Mbps communication rate within 7.6 m distance of the indoor clear pool. The CSR-ARQ scheme proposed in the paper had the best performance under the file sizes of 750-1000 kB. In practice, large-capacity data files can be split into subfiles within this range, and the CSR-ARQ scheme can be used for segmented and reliable transmission.
## 4 Numerical Examples
### Example 4--Reliability-Based Topology Optimization without Considering Self-Weight  
The optimization process in the example in Section 4.3 was developed based on the reliability analysis proposed by [30]. In this example, geometry, volume, modulus of elasticity, and compliance obtained from the deterministic analysis were considered as random variables. Coupling structural reliability analysis into the BESO-3D and SESO-3D methods demonstrated effectiveness and robustness when optimizing the bridges illustrated in Figure 6a,b. It is worth mentioning that the optimal configurations obtained are as slender as the previous ones, without significant differences in terms of structural design. For this analysis, a target reliability index \(\beta_{t}=3.0\) was considered, which is equivalent to a probability of structure failure equal to \(P_{f}=0.001358\).  
The initial design parameters are presented in Table 1, where nelx (length), nelx (height), and nelx (width) represent the geometry of the structure, F represents the distributed external load, E represents the modulus of elasticity, V represents the volume of the structure, and C represents compliance and are considered random variables with normal distribution, while Poisson's ratio (\(\
u\)) has a constant distribution.  
Figure 9a,b illustrates, respectively, the optimal configurations obtained by the two methods presented in this article. It is noteworthy that both BESO and SESO converged with three FORM iterations and 81 topology iterations with computational costs, respectively, equal to 73.02 min and 50.41 min. It is noteworthy that the computational performance of SESO for all examples presented in this article is superior to BESO.  
It is observed that the optimal configuration for both methods is equivalent. It is noteworthy that BESO has a configuration closer to SESO when considering the reliability analysis of the structure; the arc presented in Figure 6a tends here for inclined cables. It is noteworthy that the allowable stress used as a constraint in the examples is derived from the Deterministic Topology Optimization (DTO) procedure. The problem is initially solved using DTO, and the resulting von Mises stress from this optimization is utilized as the permissible stress. In this example, the value of \(\sigma^{*}=153.84\) N \(\times\) m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution Parameter** & **Distribution Type** & **Mean (\(\mu\))** & **Standard Deviation (\(\sigma\))** \\ \hline nek (mm) & Normal & 120 & 0.1 \\ \hline nely (mm) & Normal & 20 & 0.1 \\ \hline nelz (mm) & Normal & 10 & 0.1 \\ \hline E (GPa) & Normal & 1 & 0.1 \\ \hline \(\
u\) & Constant & 0.30 & 0 \\ \hline F (1000 N/m\({}^{2}\)) & Normal & 1 & 0.1 \\ \hline Volume (mm\({}^{3}\)) & Normal & 0.30 & 0.1 \\ \hline Compliance (N.mm) & Normal & \(4.82\times 10^{6}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coefficients in constitutive relations.  
Figure 8: Deterministic optimal topologies: (**a**) BESO and (**b**) SESO.
## 2 Materials and Methods
### Methods
#### 2.3.5 EVI Assessment
Therefore, we construct the projection index function as \(Q_{a}=S_{z}D_{z}\), where S\({}_{Z}\) is the standard deviation of \(Z_{i}\), and \(D_{z}\) is the local density of \(Z_{i}\). \[S_{z}=\sqrt{\frac{\sum_{i=1}^{n}(Z_{i}-E(Z_{i}))^{2}}{n-1}}\]  
\[D_{z}=\sum_{i}^{n}\sum_{k}^{n}(R-r_{ik})\cdot u(R-r_{ik})\]  
where \(E(Z_{i})\) is the mean value of the sequence [\(Z_{i}\) ] i = 1, 2, \(\ldots\), n]; \(R\) is the radius of the window with local density; \(r_{ik}\) represents the distance between sample projection values, where \(r_{ik}=|Z_{i}-Z_{k}\) i u (\(R-r_{ik}\)) is the unit step function, when \(R\geq r_{ik}\), the function value is 1, otherwise it is 0. Subsequently, we optimize the window radius \(R\), providing the function for optimizing the local density window radius \(R\), evaluating multiple \(R\) values to find the optimal \(R\) value that produces the highest projection index \(Q\), while considering the balance of weights, and finally obtaining the optimal projection direction vector \(\mathbf{a_{j}}\) through weighted standard deviation evaluation in its optimal solution. In this study, the synthetic information reflecting each characteristic indicator can be calculated based on the optimal direction vector \(\mathbf{a_{j}}\), and \(\mathbf{EVI}\) can be evaluated based on the variance level of the synthetic information. Therefore, indicator standardized values and standardized weights are used to calculate \(\mathbf{EVI}\), with the equation as follows:  
\[W_{i}=a_{j}^{2}\]  
\[EVI=\sum_{j=1}^{n}w_{j}x_{j}\]  
Since the calculated results are mostly concentrated in the middle range, the quantitative evaluation results of ecological vulnerability in the study area are classified into three categories using the natural breaks (Jenks) classification method in ArcMap based on the actual \(\mathbf{EVI}\) values from four study years (2005, 2010, 2015, and 2020). This data-driven approach maximizes the contrast between vulnerability classes and ensures meaningful ecological distinctions: low vulnerability area (\(\mathbf{EVI}\) < 0.25), moderate vulnerability area (\(0.25<\mathbf{EVI}\) < 0.50), and high vulnerability area (\(\mathbf{EVI}\) > 0.50).