esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Underwater Geodetic Network Design  
Figure 1 depicts the basic configuration of the underwater geodetic network (UGN). From bottom to top, we designed two acoustic systems: the seafloor system and the mooring system. The seafloor system comprises three base stations equipped with an omni-direction acoustic transducer. The mooring system consists of an anchor, rope, glass floats, and two buoys. The subsurface buoy is equipped with an omni-direction acoustic transducer, acoustic ranging controller, and battery. The moored sea surface buoy is equipped with an omni-direction acoustic transducer, acoustic ranging controller, battery, satellite communication module, and control display unit. In the mooring system, the buoys are moored on the same rope, and the height of the subsurface buoy can be adjusted for different environments.  
To obtain the position of the geodetic stations, various positioning methods can be applied. Utilizing the moving survey onboard vessels, GNSS-A observations can be performed to determine the absolute coordinates of the seafloor base stations [1; 27]. The subsurface buoy position can then be determined by ranging to the seafloor base stations [18]. Wave gliders or ASVs can also be auxiliary measurement platforms to provide GNSS-A observations and position the subsurface buoy [22; 24; 25]. The network configuration is also expandable. The subsurface buoy can be equipped with an inertial measurement unit (IMU) to account for its motion with the ocean currents or mooring rope. The sea surface buoy can be equipped with a GNSS receiver and antenna to determine its position [23]. Finally, the underwater submersible user, such as a near-bottom ROV or AUV, can obtain its location by receiving the acoustic signals from UGN [12; 32].  
In this study, the basic network configuration, which consists of three seafloor base stations, one subsurface buoy, and one surface buoy, is expressed as the \"3 seafloor base stations + 1 subsurface buoy + 1 sea surface buoy\" (\"3+1+1\") configuration. The seafloor base stations are distributed evenly on a circle, and the distance between the seafloor base stations is set as D. The subsurface buoy is located above the horizontal center of the seafloor base station array with a vertical height of H (0 < H \(\leq\) 3000 m), while the  
Figure 1: Schematic diagram of the underwater geodetic network configuration and user navigation application. The network consists of a seafloor component and a mooring component.  
sea surface buoy is about 3000 m above the seafloor array center. Note that the basic configuration can be expanded or reduced to other configurations, such as the \"4 seafloor base stations + 1 subsurface buoy + 1 sea surface buoy\" configuration, \"3 seafloor base stations + 0 subsurface buoy + 1 sea surface buoy\" configuration, and so on. The network dimension, its positioning range, and kinematic positioning performance at different undersea points are closely related to the seafloor station separation and the subsurface buoy height. Therefore, the performance of the network configuration in the underwater space should be evaluated.
## 3 Thermo-Mechanical Coupling Mathematical Model  
### Freezing Temperature Field  
In the process of artificial ground freezing, the change of the temperature of the soil will cause the change of water migration and stress state, and the stress field will cause the redistribution of the temperature field. For this reason, the coupling of temperature field and stress field should be considered in frost heaving deformation. The controlling equation for the temperature field can be expressed as [24; 25]  
\[C^{*}\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}\left(k^{*}\frac {\partial T}{\partial x}\right)+\frac{\partial}{\partial y}\left(k^{*}\frac{ \partial T}{\partial y}\right) \tag{12}\]  
where \(C^{*}\) is the equivalent volume specific heat; \(k^{*}\) is the equivalent thermal conductivity; and \(T\) is the soil temperature; and \(t\) is the time. \(C^{*}\) and \(k^{*}\) are given by  
\[C^{*}=\begin{cases}C_{f}&T<T_{d}\\ \frac{C_{f}+C_{u}}{2}+\frac{L}{T_{r}-T_{d}}&T_{d}\leq T\leq T_{r}\\ C_{u}&T>T_{r}\end{cases} \tag{13}\]  
\[k^{*}=\begin{cases}k_{f}&T<T_{d}\\ k_{f}+\frac{k_{u}-k_{f}}{T_{r}-T_{d}}(T-T_{d})&T_{d}\leq T\leq T_{r}\\ k_{u}&T>T_{r}\end{cases} \tag{14}\]  
where \(C_{f}\) and \(C_{u}\) denote the volumetric heat capacity of frozen soil and unfrozen soil (i.e., \(C_{f}\)= \(\rho_{f}\)\(c_{f}\), \(C_{u}\) = \(\rho_{u}\)\(c_{u}\)), \(\rho_{f}\) and \(\rho_{u}\) denote the density of frozen soil and unfrozen soil, and \(c_{f}\) and \(c_{u}\) denote the mass specific heat of frozen soil and unfrozen soil. \(k_{f}\) and \(k_{u}\) denote the thermal conductivity of frozen soil and unfrozen soil, respectively. \(T_{d}\) denotes the freezing temperature of soil; \(T_{r}\) denotes the thawing temperature of soil.  
The initial condition of temperature field is  
\[T\rvert_{t=0}=T_{0} \tag{15}\]  
where \(T_{0}\) denotes the initial temperature of soil.  
When the freezing pipe is transformed into one point, and the boundary condition of frozen pipe is expressed as  
\[T\rvert_{(x_{p},y_{p})}=T_{c}(t) \tag{16}\]where \((x_{p},y_{p})\) represents the central coordinate of the freezing pipe and \(T_{c}\) represents the brine temperature.  
At the infinite distance of the frozen zone, the boundary condition is  
\[T|_{(x=\infty,\ y=\infty)}=T_{0} \tag{17}\]
## 4 Results and Discussion
### Parametric Studies
#### 4.2.3 Effect of Amount of Fe-SMA Strip  
Figure 15a shows the load-displacement curves for varying amounts of Fe-SMA strips on the RC beam strengthened with Fe-SMA, and Figure 15b shows the comparison of the initial crack load, yield load, and ultimate load of the RC beam against the amount of Fe-SMA strips. 30 mm\({}^{2}\), 60 mm\({}^{2}\), 90 mm\({}^{2}\), 120 mm\({}^{2}\), 150 mm\({}^{2}\), and 180 mm\({}^{2}\) of Fe-SMA strips were considered as parameters. As shown in Figure 15b, as the amount of Fe-SMA strips increased from 30 mm\({}^{2}\) to 180 mm\({}^{2}\), the initial crack load, yield load, and ultimate load increased significantly by 147.8%, 81.8%, and 78.6%, respectively. However, as the amount of Fe-SMA strip increased from 30 mm\({}^{2}\) to 180 mm\({}^{2}\), the displacement ductility index decreased by about 54.5%. With these results, it was determined that the amount of Fe-SMA strip has an inverse relationship to the serviceability of specimens.
## 3 Parameter Analyses
### Kinematic Relationship and Water Surface Constraint
The back frame centroid position \(R_{c}\) along the radial is written as Appendix A Equation (A2). In accordance with the geometric relationship in Figure 3, relative motion angle \(\gamma\) and the elevation angle \(\beta\) have relationships with the rotation angle \(\varphi\) and \(\alpha\) as \(\alpha+\varphi=\gamma\) and \(\beta+\varphi=90^{\circ}\). Then, the torque  
Figure 3: Moving mass motion and structural rotation. (**a**) Stage 1, mass moving relative to the track, (**b**) Stage 2, structure rotation. Figure 2: Parameter definitions (upright state of FCRA). equilibrium equation can be rewritten as Appendix A Equation (A3). Then, the elevation angle \(\beta\) is expressed as  
\[\tan\beta=\frac{2(1-c)(2a+3 ab+3a_{1}b_{1})}{3\pi c(a+ab+a_{1}b_{1})\sin\gamma}+ \frac{1}{\tan\gamma} \tag{2}\]  
According to the expression \(\beta=90^{\circ}-\varphi\), the reflector in the upright state of \(\beta=90^{\circ}\) becomes horizontal along with the enlargement of \(\varphi\) and the reduction of \(\beta\). Therefore, the upper-limit \(\beta_{\text{max}}\) is \(90^{\circ}\) while the lower-limit \(\beta_{\text{min}}\) depends on the rotation angle \(\varphi\) of the FCRA. The rotation capacity is directly limited by the immersion depth \(h\) of the FCRA into the water's surface as shown in Figures 2 and 3. This critical state of rotation capacity \(\varphi\) is determined as the position (Figure 2) of the back frame end reaching the water's surface. It means that this \(\beta_{\text{min}}\) is equal to the half central angle of the arc under the water's surface, namely \(\theta/2\). According to the Archimedes principle, the relationship between the gravity of the FCRA and the volume of water discharge can be written as \(mg=\rho_{\text{avg}}Vd=\rho_{\text{avg}}LR^{2}\) (\(\theta-\text{sin}\theta\))/2. It is expressed by non-dimensional variables as shown in Appendix A Equation (A4). Substituting \(\beta_{\text{min}}\) into Appendix A Equation (A4), it can be reduced as  
\[\beta_{\text{min}}-\sin\beta_{\text{min}}\cos\beta_{\text{min}}=\frac{n\pi(a+ ab+a_{1}b_{1})}{b(1-c)} \tag{3}\]  
Particularly, the smaller total weight \(m\) becomes, the smaller lower-limit \(\beta_{\text{min}}\) and the larger rotation capacity \(\varphi\) of the FCRA can achieve. In addition, the rotation capacity meets the kinematic relationship between reflector elevation angle adjustment and moving mass rotation, namely Equation (2). Since the reflector elevation angle \(\beta\) turns into the lower-limit \(\beta_{\text{min}}\) under the constraint of Equation (3), the relative motion angle \(\gamma\) becomes larger based on Equation (2). In general, the lower-limit \(\beta_{\text{min}}\) represents the rotation capacity of the FCRA. The lower \(\beta_{\text{min}}\) becomes, the larger the rotation capacity and elevation angle adjustment range, which means that the observation signal range of the FCRA is wider.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 5 Results
### Method Analysis  
**The Effect of Different Mixing Proportions.** We conducted an analysis on the influence of varying mixing proportions \(\tau\) within Mixer, specifically examining the shifts in model performance across distinct mixing proportion of 15%, 30%, 50%, 80%, and 100%. The results are shown in Figure 3. Our findings indicate that, for the Whisper-Large-v2 model, achieving superior performance necessitates a higher proportion. It can be found that for the Whisper-Large-v2 model, a higher proportion of data needs to be mixed to achieve better performance. Although it has a considerable capacity, the Whisper-Large-v2 model is not well versed in this specific data type, as evidenced by its poor direct testing performance. Therefore, incorporating additional information about the data distribution can further enhance its performance. Conversely, in the case of the Whisper-Medium, its limited capacity coupled with an already strong baseline performance means that excessive enhancement of the data can inadvertently hinder the model's ability to learn from genuine data to some extent. In contrast, the Whisper-Large-v3 model exhibits minimal sensitivity to variations in mixing ratios, attributed to its combination of high capacity and extensive knowledge of the data, which allows it to consistently deliver superior performance. But overall, it can be observed that the model is not very sensitive to the mixing proportion \(\tau\), demonstrating the superiority of Mixer. Since the analysis in Table 1 shows that the Mixer-C is more suitable for the Whisper-Large-v3 model, we only analyzed the impact of different mixing proportions on Whisper-Large-v3. For Mixer-C, it can be found that mixing more data leads to better performance. This is understandable because for models with strong baseline performance, enhancing channel information allows the model to gain a more comprehensive understanding of the speech signal, thereby improving performance.  
**The Effect of Different Sampling Distributions.** Since the \(\lambda\) value is sampled from the Beta distribution, to explore the impact of different sampling distributions on the Mixer  
Figure 3: WER (%) of three different model versions along varing proportion \(\tau\).  
and Mixer-C, we analyzed the coefficient \(\alpha\) of the Beta distribution. Table 2 demonstrates the performance impact of the Mixer and Mixer-C on the Whisper-Large-v3 model under various \(\alpha\) values. For Mixer, it can be observed that an \(\alpha\) value of 2 achieves the best performance. Neither excessively large nor excessively small values of \(\alpha\) yield satisfactory results. However, for Mixer-C, both an \(\alpha\) of 2 and 0.2 yield good results. Overall, the choice of \(\alpha\) has a relatively small impact on Mixer-C, indicating that Mixer-C is less sensitive to the hyperparameter \(\alpha\).
###### Abstract  
In order to study the influence of corrosion on the fatigue life of weathering steel bridges, firstly the nominal stress method is employed to identify the key fatigue details of a typical weathering-steel anchor box of a cable-stayed bridge and then, a multi-scale refined finite-element model for the weathering-steel anchor box is established. The established fatigue-life assessment method for corrooded weathering steel is extended to the multi-scale model, the remaining fatigue life for the fatigue details under corrosion are predicted, and the influence of the initial pit size on the fatigue life of weathering steel structures is analyzed. The research results show that corrosion pits can be equated to the introduction of initial cracks in the structure, and the remaining fatigue life for key fatigue details is calculated when the initial crack size is 1 mm, 2 mm, and 3 mm of the fatigue life when the initial crack size is 0 mm, with results of 20.3%, 12.7%, and 11.5%, respectively. Therefore, the research results show that the corrosion pits are equivalent to the introduction of initial cracks in the structure and, even if the initial crack size is small, the fatigue life of the structure is greatly reduced. The fatigue-life assessment method for corrooded weathering-steel structures established in this paper is based on the finite element method and fracture mechanics, which offer universal applicability and generalizability and make up for the lack of a fatigue-life assessment method for corrooded weathering-steel bridges in service.  
w eathering steel; corrosion; fatigue detail; fatigue life; assessment method
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 5 Experimental Evaluation and Discussion
### Results and Discussion  
The experimental results show that a trained model can set the optimal scheduling algorithm in diverse battery model environments without requiring precise physicochemical information about the battery or solving complex mathematical equations. The feature of RL scheduling allows the algorithms to be modified for specific user needs without changing the overall framework.  
We demonstrate that the proposed PCA\(-\)DDQN can be used for power battery scheduling optimization problems. Firstly, the availability of the complete data set reduces the algorithmic difficulty by avoiding learning battery degradation mechanisms and building complex electrochemical models. Secondly, deep RL is usually associated with artificial intelligence. This work shows that RL is comparable to other methods, and its adaptability to aging is well\(-\)suited for battery charging and discharging control. Thirdly, DDQN is a very typical algorithm of continuous state space and discrete action space. How to deal with the continuous action space and reduce the error of the data\(-\)driven model is a problem that needs to be continued to be studied. Finally, temperature is a critical influencing factor in the battery aging process. However, in the publicly available dataset, the experimental conditions are often conducted at a fixed ambient temperature, lacking primary data. The reward value of the temperature factor cannot be clearly given, which should be experimented upon in future research.
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 3 Methodology
### Performance Evaluation Parameters
#### 3.3.3 Tools for Performance Evaluation  
Several simulation tools may be used to evaluate the effect of trust management approaches on fog computing. In our study, we use IFogSim to simulate our proposed scheme. The reasons behind choosing IFogSim are that it is straightforward to build the fog environment in IFogSim and IFogSim fulfills most of the fog environmental setup to evaluate the resource management and scheduling policies.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 3 Smart Grid Cyber-Physical Attacks
### Cyber-Physical Attacks  
Depending on the system architecture, the CPSs' controller can be normally classified into a centralised controller and distributed controller [9]. The centralised controller collects all the global measurements from the basic units or the secondary controllers, which will have a potential attacking surface in between the controller (PMU) and physical units [9]. In this case, cyber attacks mainly happen in logical interface category 10 (the interface between control systems and corporate systems), logical interface category 11 (the interface between sensors and sensor network), and logical interface category 12 (the interface between the sensor network and control system) [5; 9]. The distributed controller, however, only collects the information from local units and neighbour controllers, which is widely used in distributed systems like microgrids [9]. The attacking surface is not only between  
Figure 2: NIAC resilience framework.  
controllers and local devices but also between controllers and neighbour units like logical interface 5, logical interface category 6, and logical interface category 20 [9].  
According to the NISTIR 7628, cyber-physical attacks can commonly be classified into three types [5]:  
1. Physical attacks informed from cyber, which utilise confidential information gathered from cyberspace. For example, with this information, attackers will be able to identify which substations and lines are on high load. Physical attacks against these critical infrastructures will cause more damage than random physical attacks.
2. Cyber attacks enhance physical attacks, which introduce cyber attacks into physical attacks to enlarge the damage or increase the attack/recovery duration. A typical example is attackers conducting DoS attacks after physical attacks to disrupt the recovery operations and enhance the attack consequences.
3. Cyber attacks cause physical damage, which compromises the cyber control systems and applies harmful operation instructions to cause physical damage. The Aurora attack introduced in Section 3.3 is an example.
# Pushover Analysis on Infill Effects on the Failure Pattern of Reinforced Concrete Frames
###### Abstract  
This paper presents a pushover analysis using ABAQUS (Dassault Company, Providence, Rhodes Island, USA) for spatial reinforced concrete (RC) frames. The main purpose is to study the effect of the infills on failure patterns of the RC frames. The Finite Element Method (FEM) model considered an RC frame with fulfilled infills, half-filled infills, and without infills. Column moment, the effective width of the cast in situ slab, and the required ratio of column to beam strength are calculated and analyzed. Research findings indicate that the location of the inflection point varied because of the effect of infills. Some of the calculated values of column moments are larger than those values according to the design code. The effective slab width and the required ratio of column to beam strength are found to be reduced due to the infill effects. The actual effective width of the slab should be considered in the required ratio of column to beam strength. Reasonable advice is proposed and discussed for design purposes.  
structural seismic design; infill walls; reinforced concrete (RC) frames; failure pattern; strong column-weak beam; strong shear-weak bending Article
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 3 Problem Description and Formulation of the VRP-CS
### Assumptions and Decision Variables
#### 3.2.2 Decision Variables  
The VRP-CS model utilizes the following decision variables to determine the optimal routing plan and delivery assignments:  
* \(x_{i\_ijk}\): This is a binary variable indicating the sequence of deliveries. It equals 1 if vehicle \(k\) travels directly from location \(a\) (an optional delivery location for customer \(i\) or the depot if \(i=0\)) to location \(b\) (an optional delivery location for customer \(j\) or the  
Subject to:  
\[\sum_{k\in K}\sum_{b\in L_{j}}y_{jbk}=1,\ \forall j\in N \tag{2}\]  
\[\sum_{k\in K}\sum_{j\in N^{+}}\sum_{a\in L_{k}}\sum_{b\in L_{j}}x_{aijbk}=1\ \forall i\in N \tag{3}\]  
\[\sum_{i\in N^{+}}\sum_{a\in L_{i}}x_{aijbk}=y_{jbk}\ \forall j\in N,b\in L_{i},k\in K \tag{4}\]  
\[\sum_{j\in N^{+}}\sum_{a\in L_{i}}\sum_{b\in L_{j}}x_{aijbk}=\sum_{j\in N^{+}} \sum_{j\in N^{+}}\sum_{a\in L_{i}}\sum_{b\in L_{j}}x_{aijbk}\ \forall i\in N,i\
eq j,k\in K \tag{5}\]  
\[\sum_{i\in N}\sum_{a\in L_{i}}x_{00 iak}=\sum_{i\in N}\sum_{a\in L_{i}}x_{ia00 k}=1\ \forall k\in K \tag{6}\]  
\[r_{ia}+s+\frac{d_{aib}}{v}\leq M\Big{(}1-x_{aijbk}\Big{)}+r_{jb}\ \forall i,j\in N^{+},k\in K \tag{7}\]  
\[y_{jbk}e_{ia}\leq r_{ia}\leq y_{jbk}l_{ia}\ \forall i\in N,a\in L_{i},k\in K \tag{8}\]  
\[\sum_{k\in K}\sum_{b\in L_{j}}y_{jbk}q_{j}\leq Q_{k}\ \forall k\in K \tag{9}\]  
\[\sum_{i\in N^{+}}\sum_{a\in L_{i}}\sum_{j\in N^{+}}\sum_{b\in L_{j}}x_{aijbk} d_{aijb}\leq D_{k}\ \forall k\in K \tag{10}\]  
\[y_{jbk}\leq z_{k}\ \forall i\in N,b\in L_{j},k\in K \tag{11}\]  
\[x_{aijbk}\in\{0,1\}\ \forall i,j\in N^{+},a\in L_{i},b\in L_{j},k\in K \tag{12}\]  
\[y_{jbk}\in\{0,1\}\ \forall j\in N,b\in L_{j},k\in K \tag{13}\]  
In the formulations above, the objective function (1) aims to minimize the total distribution cost. Constraint (2) states that each customer is served only once at one of the optional locations. Constraint (3) states that a customer can only be served by a vehicle at one of the optional locations. Constraint (4) indicates that the service occurs at a location that is passed by a vehicle. Constraint (5) is the flow conservation constraint, which states that a vehicle must leave after serving a customer. Constraint (6) states that a vehicle must start and end its journey at the depot. Constraint (7) states the relationship between the start times of the visits to the adjacent customer points. Constraint (8) states that the vehicle serves the customer strictly within the requested time window. Constraint (9) states that the load of the vehicle cannot exceed its capacity. Constraint (10) represents the constraint on the distance traveled by the vehicles, which cannot exceed a maximum distance. Constraint (11) represents whether the vehicle is in use. Constraints (12) and (13) define the binary variables \(x\) and \(y\).
## 5 Our Proposed Combined Model
### Introduction of Objective Functions and NSGA-III
#### 5.3.1 Objective Functions  
In this paper, we chose three functions to be the objective functions.  
(1) The Theil Inequality Coefficient (TIC) can be indicated as follows:  
\[\text{TIC}(\hat{\mathbf{Y}},\ \mathbf{Y})=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left( \hat{y}_{i}-y_{i}\right)^{2}}}{\sqrt{\frac{1}{N}\sum_{i=1}^{N}\hat{y}_{i}^{2}} +\sqrt{\frac{1}{N}\sum_{i=1}^{N}{y_{i}}^{2}}} \tag{21}\]TIC is always between 0 to 1; a lower numerical value is equal to better performance.  
(2) Root mean squared error (RMSE). A smaller absolute value of RMSE \((\hat{\mathbf{Y}},\ \mathbf{Y})\) indicates a more accurate forecasting performance of the system. It can be defined as follows:  
\[\text{RMSE}(\hat{\mathbf{Y}},\ \mathbf{Y})=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\hat{y}_{i }-y_{i}\right)^{2}} \tag{22}\]  
(3) MAPE can be represented as:  
\[\text{MAPE}(\hat{\mathbf{Y}},\ \mathbf{Y})=\frac{1}{N}\sum_{i=1}^{N}\frac{\left|\hat{y}_{i }-y_{i}\right|}{y_{i}} \tag{23}\]  
The lower MAPE \((\hat{\mathbf{Y}},\ \mathbf{Y})\) is, the more accurate the system.  
Thus, in this combined system, the fitness function for the accuracy and stability objectives can be defined as follows:  
\[\text{\bf Minimize}=\left\{\begin{array}{ll}f_{1}=\text{TIC}(\hat{\mathbf{Y}}, \ \mathbf{Y})\\ f_{2}=\text{RMSE}(\hat{\mathbf{Y}},\ \mathbf{Y})\\ f_{3}=\text{MAPE}(\hat{\mathbf{Y}},\ \mathbf{Y})\end{array}\right. \tag{24}\]