esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Study on the Blast Resistance of Ultra-Wide Concrete Twin-Edge Box Girders Coated with Polyurea
### Parametric Analysis
#### 4.2.2 Analysis of the Impact of Different External Detonation Positions on the Box Girder
The position of the chambers and their relative distance from the boundaries affect the intensity of the blast impact and the degree of restraint, thereby influencing the vertical shift of the baseplate. The closer the chamber is to the boundary, the more effective the polyurea layer is in reducing the peak vertical displacement. This is because the polyurea coating provides effective protection in regions near the boundary, effectively mitigating the peak vertical displacement of the baseplates. (3) Distribution pattern of vertical displacement in ultra-wide box girders across the transverse direction  
When the blast is located over the middle of the T-beam section, at the quarter-span, above the innermost web, over the center of the box girder, and above the outermost web, the temporal evolution curves of vertical displacement beneath the transverse diaphragms are shown in Figure 35. The vertical displacement trends for specimens PG and PPCG were generally consistent across various test conditions. Under test conditions 6, 7, 8, 11, and 14, the peak vertical displacements at the bottom of the transverse diaphragm for specimen PG were 12.82 mm, 10.07 mm, 9.67 mm, 6.08 mm, and 3.27 mm, respectively, while the corresponding peak vertical displacements for specimen PPCG were 12.26 mm, 9.84 mm, 9.46 mm, 5.97 mm, and 3.25 mm, respectively. Specim PPCG exhibited reductions in peak displacements of 4.37%, 2.28%, 2.17%, 2.17%, and 0.61%, respectively, in comparison to specimen PG. Among these conditions, the highest peak vertical displacement observed at the bottom of the transverse diaphragm was in condition 6, with the explosion centered  
Figure 33: Temporal evolution curves of vertical shift at the baseplate center of the box chamber for condition 10.  
over the middle of the T-beam section, followed by condition 7, where the explosion occurred at the quarter-span of the T-beam section. The lowest peak displacement was observed in condition 14, where the explosion was positioned between the web of the third box chamber and the outer cantilever section. The results indicate that the peak shift of the box girder just beneath the explosion epicenter, as well as the effectiveness of the polyurea coating in blast resistance, both decrease consistently from the middle of the T-beam section outward towards the outermost web. This indicates that the middle T-beam section is the weakest point in blast resistance for the polyurea-coated twin-chamber box girder. Overall, the specimen PPCG coated with polyurea exhibited improved blast-resistance performance across different test conditions, as evidenced by the relative reduction in peak vertical displacements at the bottom of the transverse diaphragm in comparison to the uncoated specimen PG.
# Automated Identification of Sensitive Financial Data Based on the Topic Analysis
## 2 Methodology
### Sensitive Data Classification Algorithm  
The pseudo-code of the sensitive data classification model based on subject domain division is presented below after being derived through the mathematical deduction of the equation. For each instance, its cosine similarity to each centroid is first calculated to divide it into corresponding clusters. Then, based on the samples in each cluster, the centroid of the cluster is recalculated. Iterate in this order until the center of mass no longer changes or the maximum number of iterations is reached. See Algorithm 1.  
```
Input: Enter original text set \(X=\{x_{1},x_{2},\ldots,x_{u}\}\), number \(K\) of clusters, the maximum number \(M\) of iterations, and initial centroid set \(C=\{c_{1},c_{2},\ldots,c_{k}\}\) mapped from the subject term Output: Output sample clustered centroid set \(C^{\prime}=\{c^{\prime}_{1},c^{\prime}_{2},\ldots,c^{\prime}_{k}\}\) for\(m\) = 1 to \(M\)do//\(m\) represents the number of iterations; for\(i\) = 1 to \(u\)do//\(i\) represents the sample number; \(max\gets 0\); for\(j\) = 1 to \(K\)do//\(j\) represents the cluster number; \(d_{ij}\gets S_{cosine}\big{(}x_{i},c_{j}\big{)}\) // Calculate the cosine similarity to each center of mass; if\(d_{ij}>max\)then\(Y_{i}\gets j\) // Record the clustering of the \(i\)th sample \(C_{Y_{i}}\gets C_{Y_{i}}\cup\{x_{i}\}\) // Divide \(x_{i}\) into the corresponding cluster for\(j\) = 1 to \(K\)do\(c_{j}\leftarrow\frac{1}{|C_{j}|}\sum_{x\in C_{j}}x\) // Recalculate the centroid of each cluster return \(C\) // Output result
```  
**Algorithm 1**Sensitive data classification algorithm based on subject domain division.
## 5 Case Study  
### Experimental Setup  
To prove the method's effectiveness, a single wing LSDM structure is used to validate the application of the assembly error model and dynamic tolerance allocation in the ground assembly process. As shown in Figure 13, there are nine sets of truss hinges in the LSDM. Additionally, each set of truss hinge typically exists in pairs that have coincident projections in the X-O-Z plane. Figure 13c shows the actual assembly scene of the LSDM. The parallel robot and the collaborative robot are used to realize the microgravity for the hinges. A laser tracker is used to measure each hinge's spatial coordinates. The coordinate system is first established with its origin point at the center of hinge a and the positive of the \(Z\)-axis along the normal direction of the satellite antenna plate to the ground. Then, the theoretical coordinates and initial tolerance of each hinge are shown in Table 2.  
According to Section 2, for each truss rod's length and mass, the angle between the rod and the horizontal plane affect the rod's elongation. The projected rod length, the equivalent mass, and the theoretical value of the angle between the rod and \(X\)-axis are shown in Table 3.  
Another factor of the assembly error model in this paper is the hinge clearance. As shown in Figure 14, there are three hinge types in the LSDM, and the different types have different clearances, as shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hinges** & \begin{tabular}{c} **X Designed** \\ **Coordinates** \\ \end{tabular} & \begin{tabular}{c} **Z Designed** \\ **Coordinates** \\ \end{tabular} & \
\begin{tabular}{c} **X Initial** \\ **Tolerances** \\ \end{tabular} &
\begin{tabular}{c} **Z Initial** \\ **Tolerances** \\ \end{tabular} \\ \hline a & 0.000 & 0.000 & \(\pm 0.5\) & \(\pm 0.5\) \\ b & 32.000 & 1225.000 & \(\pm 0.5\) & \(\pm 0.05\) \\ c & 167.006 & 1220.008 & \(\pm 0.5\) & \(\pm 0.05\) \\ d & 2540.581 & \(-139.011\) & \(\pm 0.05\) & \(\pm 0.5\) \\ e & 449.138 & \(-24.575\) & \(\pm 0.5\) & \(\pm 0.5\) \\ f & 2692.000 & 1230.000 & \(\pm 0.5\) & \(\pm 0.05\) \\ g & 2627.721 & 648.845 & \(\pm 0.2\) & \(\pm 0.2\) \\ h & 2999.851 & 68.252 & \(\pm 0.05\) & \(\pm 0.5\) \\ j & 5552.000 & 1220.001 & \(\pm 0.5\) & \(\pm 0.05\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The designed coordinates and initial tolerances of the hinges in X-O-Z plane. Figure 13: The LSDM’s model and the actual assembly scene. Figure 14: LSDM hinge types.
## 3 Control Methodology  
A nested feedback control technique comprising two loops, as shown in Figure 3, is implemented to produce a sustained response of the p53 pathway. The outer loop consists of the p53-MDM2 pathway and its controller. The purpose of this control loop is to determine the required amount of Nutlin (\(n_{ref}\)) in the pathway to achieve the desired p53 response. This provides us the reference dosage of Nutlin that must be present inside the cell to achieve the desired p53 response. However, as discussed previously in Equation (6), the total extracellular Nutlin concentration is decomposed into two portions. One major portion is the concentration of blood-plasma-bound Nutlin, \(N_{Qb}\), and the other is the extracellular free Nutlin, \(N_{ext}\). The later one is the only available portion of Nutlin that can be transferred inside the cell.  
For the inner control loop, the equation becomes:  
\[u_{nd}(t)=P+I+D \tag{11}\]  
\[P=k_{p}e_{nc}(t) \tag{12}\]  
\[I=k_{i}\int_{0}^{t}e_{nc}(\tau)d\tau \tag{13}\]  
\[D=k_{d}\frac{de_{nc}(t))}{dt} \tag{14}\]  
\[u_{nd}(t)=k_{p}e_{nc}(t)+k_{i}\int_{0}^{t}e_{nc}(\tau)d\tau+k_{d}\frac{de_{nc} (t)}{dt} \tag{15}\]  
Figure 3: Block diagram representation of the two-loop negative feedback control scheme for Nutlin PBK dosage design. In this control strategy, the outer loop generates the reference dosage, while the inner loop tracks this dosage reference while taking into account the cellular dynamics of Nutlin.  
The outer loop equation becomes:  
\[n_{ref}(t)=k_{p}e(t)+k_{i}\int_{0}^{t}e(\tau)d\tau+k_{d}\frac{de(t)}{dt} \tag{16}\]  
Therefore, to maintain the reference Nutlin dosage \(n_{ref}\) inside the cell, another feedback loop was introduced. The inner feedback control loop is designed to consider the Nutlin dynamics of the PBK model. In this cascaded control arrangement, both the inner and outer loops are running simultaneously, where the outer loop generates the reference Nutlin dosage and the inner loop ensures that this dosage is received inside the cell considering the cellular dynamics of the drug. It is pertinent to mention that in this cascade arrangement, the inner loop should be reasonably fast as compared to the outer loop, so that the inner loop can effectively track all the changes in the reference. We require a simple and fast controller with a relatively easy implementation, especially in vivo. Therefore, we implemented the proportional-integral-derivative (PID) controller in both feedback loops. The outer PID controller is the function of the error between the desired state vector \(X_{ref}\) and the output state vector \(X(t)\). In the same fashion, the inner feedback loop PID controller is a function of the error between \(n_{ref}\) and the Nutlin present in the cell \(n_{c}\).
## 2 Study Area and Datasets
### Satellite Data Collection and Preprocessing  
#### 2.2.1 KOMPSAT-5 SAR Data  
KOMPSAT-5, known as Arirang 5, is the first Korean X-band SAR satellite based on the Korea National Space Program, which was launched in 2014 and operates between an altitude of 500 km to 600 km. KOMPSAT-5 acquires images in three different modes with varying qualities: standard, high resolution and wide swath modes. KOMPSAT-5 images collected in standard mode were used in this work.  
In this study, KOMPSAT-5 images obtained on 19 September 2019 in HH polarization and 22 September 2019 in VV polarization, 2019 were used (See Table 1). The images are resampled to 3 m and the incidence angle is approximately 40\({}^{\circ}\). Preprocessing procedures including radiometric calibration, terrain correction and speckle filtering to transform the raw digital number (DN) into the sigma naught in dB and to get the speckle noise reduced using filtering methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Satellite** & **Band** & **Date** & **Spatial Resolution** & **Frequency** \\ \hline KOMPSAT-5 & HH, VV & 19, 22 September 2019 & 3 m & 9.66 GHz \\ Sentinel-1 & VH, VV & 15 September 2019 & 10 m & 5.405 GHz \\ Sentinel-2 & Blue, Green, Red, NIR, SWIR1, SWIR2 & 11 September 2019 & 10 m and resampled 3 & / \\ \hline \end{tabular}
\end{table}
Table 1: List of configurations and acquisition time of satellites.
## 2 PEMFC Power Supply Modeling
### Model of PEMFC  
Equations (1) and (2) involve static and dynamic characteristics of PEMFC, respectively. The variable double-layer capacitor effect is applied to simulate the dynamics of PEMFC [3; 4].  
\[v_{fc}=N(E_{Nernst}-V_{ohm}-V_{con}-V_{C}) \tag{1}\]  
\[\frac{dV_{C}}{dt}=\frac{1}{C_{act}}\bigg{(}i_{fc}-\frac{V_{C}}{R_{act}}\bigg{)} \tag{2}\]where \(E_{Nernst}\) is the reversible voltage, and \(V_{ohm}\) is the ohmic drop. \(V_{C}\) is the voltage of capacitor \(C_{act}\), and \(V_{act}\) is the activation drop. \(v_{fc}\) is PEMFC voltage, and \(N\) is the number of cells. \(i_{fc}\) is PEMFC current, and \(C_{act}\) is variable capacity. \(R_{act}\) is activation polarization resistance.  
The relation between \(v_{fc}\) and \(i_{fc}\) can be described as [4]:  
\[v_{fc}=g\Big{(}i_{fc},P_{H_{2}},P_{O_{2}},T\Big{)} \tag{3}\]  
where \(T\) is the working temperature of PEMFC. \(\lambda\) is the average water content in the membrane, which is based on the membrane humidity [3]. \(P_{H_{2}}\) and \(P_{O_{2}}\) are the pressure of hydrogen and oxygen, respectively.
# Review of Hygroscopic Coating on Aluminum Fin Surface of Air Conditioning Heat Exchanger
## 2 Desiccant-Coated Heat Exchangers
### Factors Affecting the Performance of DCHE  
To improve the dehumidification performance of the DCHE system, the factors affecting the operation of the system are discussed, including airflow temperature, air velocity, cooling water temperature, and inlet air humidity content.  
[PERSON] et al. [50] found that reducing the cooling water temperature can improve the performance of the system; in addition, the performance of the system increases with the increase of the inlet air temperature and relative humidity. [PERSON] et al. [51] proposed that adsorption and desorption velocities increase with the increase of air velocity, but air velocity has little effect on the equivalent diffusion coefficient of water molecules in the desiccant layer. [PERSON] et al. [52] used the Taguchi method to rank the influencing factors of the heat and mass transfer performance of the DCHE system. For the influencing factors of heat transfer coefficient and moisture transfer, the order was wind speed and water temperature. [PERSON] et al. [53] represented that the main factors affecting the proportion of desiccant dehumidification are the cycle time and airflow rate. When the desiccant dehumidification ratio is close to 50%, the performance of the system is the best.  
It could be analyzed from the above that reducing the temperature of cold water, increasing the relative humidity and temperature of the airflow, and increasing the air velocity can improve the dehumidification performance of the DCHE system. It should be mentioned that there will be an optimal critical value of the air velocity, greater than this value will take away the water molecules in the desiccant coating, thereby reducing the dehumidification performance of the DCHE system.
## 3 Results and Discussion
### Volatile Compound Composition
#### 3.1.4 Tarpenes and Norisoprenoids  
Terpenes and norisoprenoids in wine are mainly derived from grape, and these volatile compounds are extracted into wine during the fermentation process [14]. Although terpenes and norisoprenoids are present in wine at relatively low levels, their low odor thresholds mean that these volatiles significantly contribute their flavor notes to the overall aroma of wine [50]. It has been confirmed that terpenes and norisoprenoids possess floral and fruity scents, and the composition of terpenes and norisoprenoids in wine can indicate the varietal features of wines made of different grape varieties [51]. In the present study, a same terpene and norisoprenoid compositions (nine terpenes and five norisoprenoids) were found in both clone wine samples before the aging process (Table 2). Meanwhile,these volatiles, except for _cis_-rose oxide, also exhibited the similar concentrations in these wines. During the aging period, the concentrations of citronellol, \(\beta\)-damascenone, and riesling acetal were significantly decreased in these wines, by 62-70%, 46-55%, and 11-31%, respectively (Figure 1d). The decreased concentrations of these volatiles might be the result of acid-catalyzed rearrangements [8,29]. However, increased concentration of vitispirane A (241-280%), 1,1,6-trimethyl-1,2-difluoraphthalene (TDN, 161-224%), citronellyl acetate (109-267%), vitispirane B (156-188%), and linalool (16-18%) were observed in these wines during the aging process. It should be noted that the concentration of _cis_-rose oxide increased in the clone 169 wine but decreased in the clone 191 wine during the oak barrel aging period. After an aging process of 12 months, the clone 169 wines contained significantly (\(p\leq 0.05\)) higher concentrations of three terpen (linalool, citronellyl acetate, and citronellol) and four norisoprenoids (\(\beta\)-damascenone, riesling acetal, vitispirane A, and vitispirane B) (Table 2). In addition, the wines aged in the Bordeaux oak barrel showed significantly higher levels of citronellyl acetate after the aging process than the wines in the Wolin oak barrel.
## 6 Simulation and Results
### Delay  
We define delay here as the required time to complete a whole round. It comprises the time taken to sense and process data at the leaf nodes, verify and aggregate data at the cluster heads, and finally verify and decrypt data at the access point.  
From the results in Figure 10, we can see that the average delay of one round of RWFS is 0.086 s, which is 65% less than the delay of the [PERSON] et al. scheme. This can be explained by the [PERSON] et al. scheme having an additional homomorphic MAC (H-MAC) that is computed for each sensed data, along with a MAC. One calculation of the H-MAC alone was found to be 1.3 ms on average. This makes RWFS \(1.3\times n\) ms faster than the [PERSON] et al. scheme.
# An Optimized Framework for WSN Routing in the Context of Industry 4.0
## 3 Proposed Framework
### Initial Premises  
In order to provide the mathematical analysis of OLSR and AODV configuration parameters, we determined three initial premises that serve us with a better description of the behavior of these routing protocols: Maximal speed of node's movement: s = 10 ms\({}^{-1}\); transmitting range with radius: r = 200 m; and the bit error rate: BER = \(10^{-3}\). The transmitting range was chosen as an intermediate value in an outdoor environment, which refers to the 1 Mbps data rate of 802.11b technology. For the intention of simplification, we suppose that the transmitting range has a circular shape with the same transmitting power in each direction, see Figure 1. The bit error rate \(10^{-3}\) is a typical value for an outdoor wireless environment.  
Simultaneously moving straight from each other, we can calculate the maximum duration of communication according to Equation (1). Where dist is the maximum distance between nodes and s is the speed (speed) of their motion. In other words, it is the time when a node leaves the transmitting range of the second one and the communication will be ended. With respect to our initial premises, the value of the time interval t is according to Equation (2). This value suggests that the communication between two moving mobile nodes(n1 and n2), refer to Figure 2, will not be dismissed earlier than 20 s. We have assumed a time of 20 s and distance of 400 m. From a practical point of view, the time interval can be considered to be the initial value for the determination of the Hello interval and other routing protocol parameters. Figure 2 shows that mobile nodes are simultaneously moving directly away from each other:  
\[t=\frac{dist}{s} \tag{1}\]  
\[t=\frac{dist}{s}=\frac{400}{20}=20\;s \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Meaning** \\ \hline s & Speed \\ \hline r & radius \\ \hline BER & bit error rate \\ \hline t & time \\ \hline dist & distance \\ \hline G & graph \\ \hline V & vertices \\ \hline E & edges \\ \hline \(Si\) & set of nodes \\ \hline \(Sm\) & set of multipoint nodes \\ \hline \(Ss\) & set of source nodes \\ \hline \(Sr\) & set of relay nodes \\ \hline \(Sd\) & set of destination nodes \\ \hline P & probability \\ \hline Ni & nodes \\ \hline Tc & topology control \\ \hline \end{tabular}
\end{table}
Table 1: Symbols used in mathematical analysis.
###### Abstract
To solve the above problems, this paper investigates the IEWS reliability evaluation approach, considering multi-state models of equipment in the WS. The main contributions of this paper are as follows:  
1. Establishing a multi-state reliability model for equipment in the WS based on their failure mechanisms to accurately assess the reliability level of the IEWS. This study investigates the failure mechanism of water supply pipes and creates a multi-state reliability model considering leaks. Furthermore, a multi-state reliability model of an RO desalination plant is established based on the process technology concept of the plant, taking into account inefficient operation. 2. Modeling the optimal load shedding of the IEWS and establishing reliability indices. An optimal load shedding model of the IEWS considering water-energy coupling is proposed, and a reliability evaluation index system for the IEWS is established in terms of the probability of the loss of electric and water loads when the expected value of electricity and water is not supplied. 3. Modeling IEWS reliability models based on Markov chain Monte Carlo (MCMC) simulation. An IEWS reliability evaluation approach based on MCMC simulation is established to effectively quantify the reliability level of electric and water supply in the IEWS. This approach serves as a basis for rational and scientific decision making during the system's design and construction, as well as for successfully quantifying the reliability level of the IEWS. The rest of the paper is organized as follows. Section 2 proposes the multi-state reliability models of equipment in the WS, Section 3 proposes an optimal load shedding model of the IEWS and a reliability evaluation index system, and Section 4 discusses the proposed IEWS reliability evaluation model and solution method based on the multi-state models of equipment in the WS. Section 5 presents the numerical case study and Section 6 concludes with the research focus, research methods, and conclusions of this paper.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 4 A Connectivity Analysis Based on the CNN-BiLSTM-AM Model
### Input-Output Data  
Considering the impact of the marine environment, hydrological and meteorological parameters such as humidity and the signal-to-noise ratio can be measured by the receiver, with both historical and current data accessible. These data are used as input features, including the channel properties, marine environmental conditions, and ship navigation parameters, which are all obtained via measurements. The input data \(X\) are defined by Equation (30) as follows:  
\[\mathbf{X}=\left(\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{N_{i}}}\right) \tag{30}\]  
Each feature is a vector that includes multiple parameters collected at a specific time point.  
\[\mathbf{x_{i}}=\left(v_{i}^{1},v_{i}^{2},\theta_{i}^{1},\theta_{i}^{2},d_{i}^ {1},d_{i}^{2},\gamma_{i}^{1},\gamma_{i}^{2},h_{i}^{1},h_{i}^{2}\right) \tag{31}\]  
* **Speed** : \(\mathbf{v_{1}}=[v_{1}^{1},v_{2}^{1},\ldots,v_{N_{i}}^{1}]\), \(\mathbf{v_{2}}=[v_{1}^{2},v_{2}^{2},\ldots,v_{N_{i}}^{2}]\)
* **Direction Angles**: \(\mathbf{\theta_{1}}=[\theta_{1}^{1},\theta_{2}^{1},\ldots,\theta_{N_{i}}^{1}]\), \(\mathbf{\theta_{2}}=[\theta_{1}^{2},\theta_{2}^{2},\ldots,\theta_{N_{i}}^{2}]\)
* **Distance**: \(\mathbf{d_{1}}=[d_{1}^{1},d_{2}^{1},\ldots,d_{N_{i}}^{1}]\), \(\mathbf{d_{2}}=[d_{1}^{2},d_{2}^{2},\ldots,d_{N_{i}}^{2}]\)
* **Signal-to-Noise Ratio**: \(\mathbf{\gamma_{1}}=[\gamma_{1}^{1},\gamma_{2}^{1},\ldots,\gamma_{N_{i}}^{1}]\), \(\mathbf{\gamma_{2}}=[\gamma_{1}^{2},\gamma_{2}^{2},\ldots,\gamma_{N_{i}}^{2}]\)
* **Wave Height**: \(\mathbf{h_{1}}=[h_{1}^{1},h_{2}^{1},\ldots,h_{N_{i}}^{1}]\), \(\mathbf{h_{2}}=[h_{1}^{2},h_{2}^{2},\ldots,h_{N_{i}}^{2}]\)  
Figure 7: Algorithm network structure.  
The problem of maritime wireless communication connectivity analysis is addressed by predicting the future ship-to-shore connectivity based on historical marine environmental data. The output data \(\mathbf{Y}\) are represented as  
\[\boldsymbol{Y}=[\boldsymbol{y_{1}},\boldsymbol{y_{2}},\ldots,\boldsymbol{y_{N_{o} }}] \tag{32}\]  
where \(\boldsymbol{y_{i}}\) indicates the estimated connection probability of the two-hop link at the \(i\)-th time slot. The total number of output data points is \(N_{o}\), meaning that the method predicts the connection probability at \(N_{o}\) different time instances.