esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Existing Routing Protocols
### Rumor Routing  
Rumor routing [19, 20] is a kind of directed diffusion and is used for applications where geographic routing is not feasible. It combines query flooding and event flooding protocols in a random way. It has the following assumptions:  
* The network is composed of densely distributed nodes.
* Only bi-directional links exits.
* Only short distance transmissions are allowed.
* It has fixed infrastructure.  
In case of directed diffusion flooding is used to inject the query to the entire network. Sometimes the requested data from the nodes are very small and thus the flooding is unnecessary, so we can use another approach which is to flood the events when the number of events is small and the number of queries is large. The queries are rooted to that particular nodes that are belongs to the interested region. In order to flood events through the network, the rumor routing algorithm employs long-lived packets, called agents. When a node detects an event, it adds such event to its local table (events table), and generates an agent. Agents travel the network on a random path with related event information. Then the visited nodes form a gradient towards the event. When a node needs to initiate a query, it routes the query to the initial source. If it gets some nodes lying on the gradient before its TTL expires, it will be routed to the event, else the node may need to retransmit, give up or flood the query. Unlike directed diffusion, where data can be routed through multiple paths at low rates, Rumor routing only maintains one path between source and destination. Rumor routing performs well only when the number of events is small. For a large number of events, the cost of maintaining agents and event-tables in each node becomes infeasible if there is not enough interest in these events from the BS. Moreover, the  
Figure 6: Directed Diffusion Protocol.  
overhead associated with rumor routing is controlled by different parameters used in the algorithm such as time-to-live (TTL) pertaining to queries and agents.
## 2 Relay Node Deployment Problem for TT-WSN  
### Network Model  
This research studies a TT-WSN with size \(l_{x}\times l_{y}\). The equipment deployed includes \(N_{s}\) SNs, \(N_{r}\) RNs, and one sink node (BS), as shown in Figure 1. All SNs have the same initial energy, i.e., RNs only forward without collecting information and it is assumed that their energy is unlimited. SNs and RNs follow the unit disk communication model. The condition for any two devices to communicate with each other is that their Euclidean distance be less than or equal to the communication distance. SNs can send information to SNs, RNs or the BS, while RNs can only send information to other RNs or the BS. The routing protocol is provided by the shortest energy consumption path calculated by the SPFA algorithm [22]. The medium access protocol uses the S-MAC protocol [23]. All SNs collect data and send it to the BS. This process is a work cycle; for simplicity, it is assumed that each work cycle takes 10 min.
## 5 Thermodynamic Evaluation  
### Energy Analysis and Results  
The energy performances of stand-alone and integrated designs are compared under 100% rated output load, where the extra power generation is examined by an equal fuel consumption rate. The net power generation of WTEU in the integrated design is defined as (\(P_{\text{w,int}}\)):  
\[P_{\text{w,int}}=P_{\text{tot,int}}-P_{\text{c}} \tag{4}\]  
where \(P_{\text{tot,int}}\) is the net total power generation of integrated design, MW; \(P_{\text{c}}\) is the net power generation of CFPU, MW, considered invariant in both designs.  
The net waste-to-energy efficiency (\(\eta_{\text{en,w}}\)) and the net total energy efficiency (\(\eta_{\text{en,tot}}\)) are expressed as:  
\[\eta_{\text{en,w}}=\frac{P_{\text{w}}}{m_{\text{w}}\times q_{\text{w}}} \tag{5}\]  
\[\eta_{\text{en,tot}}=\frac{P_{\text{tot}}}{m_{\text{c}}\times q_{\text{c}}+m_{ \text{w}}\times q_{\text{w}}} \tag{6}\]  
where \(P_{\text{w}}\) is the net power generation of WTE system, MW; \(P_{\text{tot}}\) is the net total power generation of two systems, MW; \(m_{\text{w}}\) and \(m_{\text{c}}\) are consumption rates of MSW and coal, kg/s; \(q_{\text{w}}\) and \(q_{\text{c}}\) are net calorific values of MSW and coal, kJ/kg.  
The energy analysis results of initial and proposed designs are depicted in Table 14. As the coal-fired and WTE systems are combined, not only does the mass flow of reheat steam entering the CFPU boiler increase, but the work output of STs augments as well. In addition, the consumption rates of coal and MSW keep constant. As a result, the gross power generation is increased by 3.91 MW. Moreover, the total auxiliary power is decreased by 0.33 MW because of several devices' removals in the proposed WTE design, including condensate pump (CP) and circulating water pump (CWP). Accordingly, the net total power generation of hybrid system is improved by 4.25 MW, increasing the net total energy efficiency from 41.84% to 42.12%. Considering the power generation of CFPU is invariant, the net power generation of WTE system is raised from 8.00 to 12.25 MW, increasing the net waste-to-energy efficiency from 19.73% to 30.21%. Energy analysis results expose that the proposed design improves the thermal efficiency of WTE system.  
Figure 4: Variation in the mass flow of each extraction steam stage of CFPU before and after system integration.
## 4 Implementation of Autonomous Task Planning
### Simulation Results Analysis  
Set up a UAV swarm to search and strike ground escape targets, with three UAVs and eight targets, respectively. The generation location and initial motion direction of ground escape targets are random. Within time \(T\), after the mission UAV detects and eliminates all ground targets, it is determined that the mission swarm has won the turn.  
Figure 5a illustrates the winning rate of the mission UAV in the 3v8 scenario, while Figure 5b shows the corresponding reward. Due to the uncertainty of the initial position, motion trajectory, and avoidance strategy of ground targets in each round of training, we used the results corresponding to every 100 rounds of training when drawing the win rate curve and reward value curve. They can demonstrate the effectiveness of the algorithm for UAV training. By analyzing Figure 5a, we can see that the winning rate increases with the number of rounds, reaching over 95% in 10,000 rounds. Although there are fluctuations, the independent adjusted winning rate is still high. The average reward also increases with the number of training rounds.  
Figure 6 shows the strike process of the mission UAVs on the ground targets in the 3v8 scenario. The red dots represent the mission UAVs and their trajectories, and the blue dots represent the ground targets and their trajectories. When the mission UAV strikes the ground target, the blue dot will disappear and only the trajectory of the ground target will remain. According to the different ground target distribution and trajectory, the UAV swarm can independently complete the mission planning.  
After training, the agent is demonstrated by the expert system. At this time, the UAV swarm shows a higher level of intelligence, and has independently evolved the patrol strike tactics. Three UAVs deploy their formation from the initial position, travel in a straight queue in the unknown area, and complete the locking strike after finding the target. Based on the new target position, plan the strike path. The three aircraft cooperated with each other to complete the attack on all eight targets within the specified time.  
Figure 5: Winning rate and reward curve. (**a**) Winning rate; (**b**) Reward.
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 3 Materials and Methods  
### Normal Equal-Loudness-Level Contours  
The human ear has the ability to perceive sound waves at different frequencies; however, this perceptual capacity can vary significantly, depending on the frequencies. [PERSON] and [PERSON] developed a chart, called the equal-loudness contours, based on their research and experimental studies on this subject. This chart was later standardized under the name ISO 226 Standard [12]. This standard includes the equal-loudness contours, which define the sound pressure levels required for sounds at different frequencies to be perceived as equally loud by the human ear. The equal-loudness contours for different phon levels are shown in Figure 1.  
Equal-loudness contours were determined by taking as reference the frequency of 1000 Hz, which is well perceived by the human ear. These contours are specifically designed considering the psychoacoustic effects on humans. The contours are created based on the average responses of individuals aged between 18 and 25 years, who are consideredologically normal, forming an ideal ear model [12].
# Boosting Targeted Black-Box Attacks via Ensemble Substitute Training and Linear Augmentation
## 3 Related Work
### Jacobian-Based Augmentation  
Previously, [PERSON] et al. proposed the idea of Substitute Models Attack [15], which mainly includes training a local substitute model for the target black-box model via Jacobian-based augmentation and then using the substitute model to craft adversarial examples via white-box attack methods. This is the original and the most popular flowchart of generating substitute models attacks. In addition, in Figure 1, we describe the flowchart of this method in detail.  
Figure 1: Flowchart of Substitute Training [15]. This pattern mainly contains four steps: Initialization, Labeling, Substitute Training and Augmentation. The adversaries label and augment the dataset repeatedly for training substitute models that fit the target model’s decision boundary better and better. The crucial operation in this flowchart is Augmentation, which in other words is the definition of the augmenting function \(f\).  
For a target model \(O\), we first choose the initial dataset \(S_{0}\) and the architecture of substitute model \(F\). In addition, then we continue to label the initial dataset \(S_{\rho}\) so that we can train the substitute model \(F\) with the training dataset \(\{(x,O(x)):x\in S_{\rho}\}\). After substitute training, we generate the augmentation based on dataset \(S_{\rho}\) and the substitute model \(F\) to get \(S_{\rho+1}\), which we describe as  
\[S_{\rho+1}=f(S_{\rho},F), \tag{2}\]  
where \(f\) is the crucial element of substitute models' attack because it determines how we augment the dataset. Here in Jacobian-based augmentation, the augmenting function \(f\) is constructed as  
\[S_{\rho+1}=\{x+\lambda\cdot\text{sgn}(J_{F}[O^{*}(x)]):x\in S_{\rho}\}\cup S_{ \rho}, \tag{3}\]  
where \(\lambda\) is a parameter of the augmentation: it defines the size of the step taken in the direction identified by the Jacobian matrix, and \(sgn\) is the sign function.  
After the augmentation, we continue to label the new dataset \(S_{\rho+1}\) like we proceeded with \(S_{\rho}\) before, and train the substitute model \(F\) with \(\{(x,O(x)):x\in S_{\rho+1}\}\). Here, we called \(\rho\)_subtitute training epoch_, denoted as _epoch_\(\rho\). In addition, the method we present above is just a complete epoch, which we may repeat several times to finally get our substitute models. By the way, the number of substitute training epochs is mainly determined by the constraint of query times.  
This is the main flowchart of Substitute Models Attack, and, in this paper, we follow this flowchart to train our substitute models. As the paper describes, the Jacobian-based Augmentation method acquired a high misclassification rate for both the local and the remote image classification system.
# Effect of Parameters on Vapor Generation in Ship-to-Ship Liquefied Natural Gas Bunkering
## 1 Introduction
[PERSON] and [PERSON] analyzed the parameters influencing the BOG quantity during unloading from an LNG carrier to a terminal and proposed an optimization strategy. The results revealed that the unloading of lean LNG (with the minimum LNG consumption) from the LNG receiving tank led to the lowest net BOG quantity, and that the operating pressure of the receiving tank was an important factor affecting the BOG quantity [20]. [PERSON] et al. performed dynamic simulation to obtain BOG generation profiles during of LNG vessel loading process. The effect of various factors on jetty BOG profile, including heat leak, initial-temperature of LNG ship-tank, BOG compressor capacity, and maximum cooling-rate for ship-tank, were studied. Results show that jetty BOG generation ranges from 1.2% to 2.5% of total amount of LNG transfer. LNG loading time increased by about 8 h due to the ship-tank being warmer by 30 \({}^{\circ}\)C [21]. In this study, we conducted a comprehensive and detailed dynamic simulation to estimate the amount of vapor generation (namely BOG) and vapor return during ship-to-ship LNG bunkering. The amounts of vapor generation and vapor return were analyzed with varying parameters, such as the temperature difference between tanks, transfer rate, pipe thermal insulation performance, LNG composition, and pressure control. The contribution of each parameter was assessed. The main difference between this study and those in References [16; 17] is the calculation of the amount of vapor generation and vapor return during bunkering based on the more realistic and detailed conditions of ship-to-ship LNG bunkering. The previous studies and simulations did not consider the thermal insulation of the tanks and pipes, pressure drop of the hose, or composition of the LNG, all of which can affect the amounts of vapor generation and vapor return. The main objective of this study was to investigate the variation trends of vapor return and vapor generation, and those of pressure and temperature during bunkering. Furthermore, we proposed a method of pressure control for minimizing vapor return and vapor generation during bunkering.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 4 GIM Derivation from Multi-Source Data in Modip Latitude  
Based on the above simulation, we derived the global TEC with the combination of IGS (G), JASON of satellite altimetry (A), COSMIC occultation (C) and the IRI-2016 model (I) using a spherical harmonic function in solar-modip latitude, which is referred to as GACI hereafter. The height in the SL assumption, sampling rate and cutoff elevation were set to 450 km, 300 s and 25\({}^{\circ}\), respectively. Data on DOYs 81-83, 172-174, 264-266 and 355-357 in quiet geomagnetic conditions (\(\mid\mathrm{Dst}\mid<30\)) were selected to examine the method of GACI.  
IRI-2016 was only used to provide ionospheric information for the areas without any observations, where virtual TEC from the IRI model were added at the center of each grid that had no observation data at a resolution of 2 h, with a latitude interval of 2.5\({}^{\circ}\) and a longitude interval of 5\({}^{\circ}\). The observations from 22:00 UT of the previous day to 02:00 UT of the next day were combined for estimating 14 sequential groups of SH coefficients and 1 group of DCBs and systematic biases in 1 day. The least-squares method was used for estimating the SH coefficients and DCBs, and the piece-wise linear (PWL) function was introduced to establish connections of VTEC from different sessions. DCBs for all satellites and receivers were treated as daily constant values, with a zero-mean condition imposed on the DCBs of the satellites. The systematic biases of GNSS with JASON-2 of the satellite altimetry, COSMIC occultation and virtual observations from the IRI model were solved together. Since the accuracy of the data from ground-based GNSS, satellite altimetry, COSMIC occultation and the IRI model were not consistent, the weights of these data in different systems needed to be considered in the data combination. The Helmert variance component estimation method of variance component estimation (VCE) was used to determine the weights of different data. Details can be found at [PERSON] et al. (2014); [PERSON] et al. (2022) and [PERSON] et al. (2019).
## 1 Introduction
Many of the above studies have shown that the use of CFD numerical modeling significantly enhances the effectiveness of pump optimization and serves as a valuable tool for the detailed analysis of flows, vortices, and other parameters. However, the need for further improvement in impeller design remains a pressing challenge. In this regard, the aim of this study is to minimize the losses caused by hydraulic and local resistances to improve the pump's flow characteristics. To overcome this challenge, a hydraulic calculation model was developed to determine the initial optimal geometric parameters of the impeller and volute of the pump. The study applied numerical modeling methods using Ansys CFX, along with experimental data from an existing centrifugal pump to verify the simulation results. The choice of these methods is justified by their high accuracy and ability to conduct a detailed investigation of the pressure and velocity fields, as well as provide in-depth analysis for optimizing the pump design, ultimately leading to a significant improvement in its energy efficiency. The results of this study are expected to significantly improve the efficiency (COP) of centrifugal pumps, which in turn will reduce the energy consumption and enhance the reliability of pumping systems. The research enables a more precise identification of the parameters to which the pump efficiency is most sensitive and evaluates their impact on the overall energy efficiency of the pump.
## 1 Introduction
Therefore, limited numerical modelling research is reported regarding the essential connections between electrostatic field properties, collection efficiency (ESP performance), and ESP design. This research is aimed to present a detailed three-dimensional numerical model of an electrostatic precipitator (ESP). The numerical model comprises the gas flow, electrostatic, and particle motions in the solution domain. The model is employed to evaluate the turbulent airflow, particle charging, and transport behaviors in the solution domain. The collection efficiency of the wire-plate type ESP is also investigated in this interacting media, including electric field, fluid dynamics, and particle dynamics. In addition to the numerical model, an improved analytical model proposed a generalized and relatively easy estimation procedure for the ESP collection efficiencies of an extensive range of particles between 20 nanometers and 10 \(\upmu\)m. The numerical model is also used to validate the analytical model collection efficiency results. The different geometric and operational parameters, including the particle properties, are considered to obtain the collection efficiency of the ESP.
# Experimental Validation of Non-Marker Simple Image Displacement Measurements for Railway Bridges
## 5 Verification Results of Field Tests
### Measurement Results of Bridge B  
Vertical displacements of bridge B under two, up- and down-bound, eight-car trains were obtained using a ring-type gauge, laser measurement, and non-marker image measurements A (industrial video camera) and B (compact digital camera). The compact digital camera is not an industrial camera but a general consumer product, and the displacement was measured using its video recording function (See Table 6 for the equipment details).  
Figure 21 shows the displacement responses during the train passage using each method. Bridge B is a two-span continuous pre-stressed concrete rigid frame bridge. Thus, an uplift that causes displacement on the upper side occurs when the train passes on adjacent spans. Further, the timing is reversed for up- and down-bound train passages. Rach figure shows, in addition to the entire views, magnified images of the displacement measurement results when the maximum and minimum displacements occurred.  
Figure 22 shows the maximum displacement of bridge B. The maximum displacement was slightly larger during the passage of the down train. In the non-marker image measurement, the maximum displacement was slightly larger during the passage of the down train.  
Figure 21: Comparison of measurement results of displacement responses of bridge B: (**a**) up train passing at 220 km/h, (**b**) down train passing at 257 km/h.  
Figure 20: Estimated results of bridge deflection shape by non-marker image measurement: (**a**) displacement response at the bridge midspan, and bridge displacement when (**b**) the train is entering, (**c,d**) the train is passing, and (**e**) the train is exiting.  
surement A, an error of about \(-0.8\) mm occurred in the case of down train 1. Figure 21b shows the measured response, and it can be confirmed that spike-like noise is accidentally duplicated when the maximum deflection occurs (\(\sim\)8.9 s). Moreover, the entire displacement response of non-marker image measurement A, in this case, tends to shift downward. Therefore, it may be possible to reduce these errors after measurement by performing smoothing and drift correction. This post-processing is considered in the next section. With the except of the above cases, it was possible to evaluate the maximum displacements of a concrete bridge with a relatively smooth surface with an error of \(\pm 0.3\) mm or less compared to the ring-type gauge, even using the non-marker image measurement under the current conditions.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 5 Control Strategies for the Mitigation of the Impacts Induced by Induction Motor Loads
### Fault Ride Through Strategy for Grid-Following Units
#### 5.1.1 Simulation Results and Discussion
Aiming to provide a better understanding regarding the influence of the proposed voltage/reactive current hysteresis tuning, a sensitivity analysis was also performed. In this sense, three distinct sets of parameters were compared: (Case 3.1) \(V_{1}\) = 0.5 p.u., \(V_{2}\) = 0.9 p.u., \(V_{3}\) = 0.9 p.u. and \(V_{4}\) = 1.02 p.u. ; (Case 3.2) \(V\) = 0.5 p.u., \(V_{2}\) = 0.9 p.u., \(V_{3}\) = 0.9 p.u. and \(V_{4}\) = 1.04 p.u. ; (Case 3.3) \(V_{1}\)= 0.5 p.u., \(V_{2}\) = 0.9 p.u., \(V_{3}\) = 0.9 p.u. and \(V_{4}\) = 1.06 p.u. The corresponding results are depicted in Figures 20-23. From the conducted analysis, it was verified that successful IM load recovery occurs only in Case 3.2 and Case 3.3, indicating that the CI-RES reactive current contribution in Case 3.1 is not sufficient. Furthermore, it was observed that higher values of \(V_{4}\) allow an increased reactive current injection by the CI-RES during the post-fault period (Figure 22), revealing the less reactive power regulation effort by the BESS power converter (Figure 21) and a faster voltage recovery at the IM load terminals (Figure 23). From Figure 22, it is also notable that lower values of \(V_{4}\) lead to a higher reactive power injection from the BESS power converter. On the other hand, it is noticeable that higher values of \(V_{4}\) cause a post-fault steady-state network voltage increase. Therefore, \(V_{4}\) should not be too high, in order to prevent overvoltages. Moreover, as the adopted control parameters influence the post-fault voltage, the loads' active power consumption is also affected because of the adopted load model. Consequently, slight variations in the network frequency are observed as a result of this sensitivity analysis (Figure 20). Figure 19: L3 large industrial motor (left) and refrigerator load type motor (right) speeds and terminal voltages for different reactive current-voltage injection profiles adopted in CI-RES. Figure 21: BESS power converter active and reactive power outputs for different shapes of the CI-RES voltage/reactive current hysteresis tuning. Figure 22: WF1 reactive current output and terminal voltage following different voltage/reactive current hysteresis tuning. Figure 20: BESS power converter synthetic frequency and terminal voltage regarding different shapes of the CI-RES voltage/reactive current hysteresis tuning.