esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient
## 2 Principle of Aeromagnetic Compensation
### Calibration Flight  
To obtain the compensation parameters of the platform, the aircraft needs to conduct a set of maneuvers according to certain requirements, also known as calibration flight [14; 31]. As shown in Figure 3, the calibration flight is carried out in four orthogonal headings, and the pitch, the yaw, and the roll maneuvers are completed successively in each direction, for which the standard amplitudes of the pitch, the roll, and the yaw are \(\pm 5^{\circ}\), \(\pm 5^{\circ}\), and \(\pm 10^{\circ}\), respectively. The altitude of the calibration flight is usually set at several kilometers to avoid being affected by the magnetic interference from the ground. Usually, the calibration flight is followed by a test flight that is used to evaluate the effectiveness of the estimated compensation parameters.  
Figure 3: Attitude requirement during calibration flight.
## 3 DASH Interferometer Mount
### Optimizing the Flexible Structure
#### 3.3.2 Optimal Phase Position for Flexible Structures  
The Cartesian coordinate system was established with the cross-point of the incident and exit optical axes as the coordinate origin. Without considering the position in the Z direction for the time being, the coordinate position of the center of the mass is (\(X_{0}\), \(Y_{0}\)). The coordinates of the two pairs of flexible structures on the cover (base) are (\(X_{1}\), \(Y_{1}\)), (\(X_{2}\), \(Y_{2}\)), (\(X_{3}\), \(Y_{3}\)), and (\(X_{4}\), \(Y_{4}\)), respectively.  
Accordingly, the force on the four flexible structures is expressed as follows for rotating around the \(Y\)-axis:  
\[\left\{\begin{array}{c}F_{Xi}=-\frac{ma-\left(X_{0}-X_{j}\right)}{X_{j}-X_{ i}}=-\frac{ma-\Delta X_{j,0}}{\Delta X_{j,i}}\\ F_{Xj}=\frac{ma-\left(X_{0}-X_{i}\right)}{X_{j}-X_{i}}=\frac{ma-\Delta X_{j,0} }{\Delta X_{j,i}}\end{array}\right., \tag{12}\]  
where \(\Delta X_{i,0}=X_{0}-X_{i}\), \(\Delta X_{j,0}=X_{0}-X_{j}\), \(\Delta X_{j,i}=X_{j}-X_{i}\), \(i=1\), \(4\), and \(j=2\), \(3\).  
Similarly, in the case of rotating around the \(X\)-axis, the force is expressed as follows:  
\[\left\{\begin{array}{c}F_{Yi}=-\frac{ma-\left(Y_{j}-Y_{0}\right)}{Y_{j}-Y_{ i}}=-\frac{ma-\Delta Y_{j,0}}{\Delta Y_{j,i}}\\ F_{Yj}=\frac{ma-\left(Y_{0}-Y_{i}\right)}{Y_{j}-Y_{i}}=\frac{ma-\Delta Y_{j,0} }{\Delta Y_{j,i}}\end{array}\right., \tag{13}\]  
where \(\Delta Y_{j,0}=Y_{0}-Y_{i}\), \(\Delta Y_{j,0}=Y_{j}-Y_{0}\), \(\Delta Y_{j,i}=Y_{j}-Y_{i}\), \(i=3\), \(4\), and \(j=1\), \(2\).  
According to Equations (12) and (13), to reduce the force on the flexible structure of the four bonding surfaces, the distance between the bonding locations should be increased, and that between the bonding locations and the center of mass should be reduced in the \(X\) and \(Y\) directions.  
Additionally, according to the vibration mode of the DASH interferometer, the phase of flexible structure is \(90^{\circ}\), as shown in Figure 10.  
Figure 9: Schematic illustration of the bonding surface bowing effect.
## 2 Method
### Implementation  
Figure 3 illustrates the implementation flowchart of the proposed algorithm. In summary, the algorithm consists of five main steps:  
1. Performing a 2D Fourier transform of the received pressures \(p_{u}(x,z=0,t)\) and transmitted pressures \(s_{u}(x,z=0,t)\).  
2. Using Equations (17) and (25) for tilt compensation to extrapolate both received and transmitted wave fields from the coordinate system \(Oxz\) to \(Ox^{\prime}z^{\prime}\).  
3. Using Equations (21) and (24) for wave-field extrapolations to obtain both received and transmitted wave fields at depth \(z^{\prime}\) in layer \(L\).  
4. Image reconstruction at depth \(z^{\prime}\) by taking a correlation between the two wave fields using Equation (29).  
5. Obtaining the full-matrix image by superimposing all B-scan images using Equation (30).  
It should be noted that the substitution of \(k_{x}\) with \(k_{x}^{\prime}\) is implemented by an interpolation in the \(k_{x}\) domain. Note also that a frequency truncation is applied, as the frequency spectrum of ultrasonic signals is band-limited. After the 2D Fourier transform, the spectra \(P_{u}(k_{x},z=0,\omega)\) and \(S_{u}(k_{x},z=0,\omega)\) are truncated to obtain a subset corresponding to \(\omega\in[2\pi f_{\min},2\pi f_{\max}]\), where \(f_{\min}\) and \(f_{\max}\) are the lower and upper cutoff frequencies of the array, respectively. Therefore, the SNR of the image is improved due to the removal of high-frequency noise. The efficiency is also improved due to the dataset size reduction.  
The proposed algorithm provides an accurate method for compensating for the oblique incidence of the linear array and achieving oblique incidence full-matrix imaging with a significant efficiency advantage compared with the time-domain TF
## 3 Materials and Methods
### Stock and Flow Diagrams  
A stock and flow diagram, the most common causal loop diagram, combines polarity and stocks and flows quantitatively; it describes the behavior of the dynamic system by mathematic definitions of stocks and flows.  
Quantifying stocks and flows allows for other system constituent variables, constants, and inflow or outflow levels, including input data from an external source into the model and model output data. The external source may be a submodel generator of input data, whereas the output data involve a consumer outside the model (sink). If we use the correct language, these elements give us the breeding ground to describe any system (22).  
Basic relationships and elements in the stock and flow diagram are depicted in the following Figure 1.  
Stocks represent a variable at a point in time. An increase or decrease in the stock reflects flow levels in the stock. Based on specific variables, the flow levels comprise functions of other interdependent model elements [27]. Constants are numerical values that remain invariant unless disturbed by a particular model variable.  
Stocks intertwine with existing and past flows. The formula for describing stock levels at a point in time is as follows [27]:  
\[stock(t)=stock(t-1)+(inflow(t)-outflow(t)) \tag{1}\]  
The net change in a stock can therefore be represented by a differential equation:  
\[\frac{d(stock)}{dt}=inflow(t)-outflow(t) \tag{2}\]  
Therefore, the stock levels may also involve an integral equation as follows:  
\[stock(t)=\int_{t_{0}}^{t}[inflow(x)-outflow(x)]dx+stock(t_{0}) \tag{3}\]  
where _inflow_(_x_) and _outflow_(_x_) represent the level of the inflow and the outflow at time \(x\) between the initial time \(t\)0 and the current time \(t\). The value of the stock at time \(t\) is the net difference between the inflow and the outflow between time \(t\)0 and \(t\) plus the initial value of the stock at time \(t\)0. Flow can be determined in various ways, including constants, mathematical functions of stocks and auxiliary variables, and graphical representations. Auxiliary variables combine the illustrated variables, allowing potential corrections or smooth flow control.  
Figure 1: Depiction of basic relationships and elements in the stock and flow diagram [27].  
The supply and demand transport estimation model involves Vensim PLE freeware from Ventana Systems Company. The free version allows a comprehensive analysis of discreet variables using multi-agent modeling [28].
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 2 Experiment 1
### Discussion  
The goal of this first experiment was to investigate the influence of different sound types in a video game on the players' gaming experience. As a previous study had shown a significant influence of ambient sound on immersion and character sounds on avatar identification in a puzzle-platformer game [23], this was the first attempt to replicate these results in a different game genre while at the same time investigating possible connections to game performance.  
Unfortunately, in this experiment, we did not find any significant effects of sound conditions on aspects of the game experience or performance. We did find a non-significant trend in the effect of character sounds on avatar identification, which points in alignment with earlier results, as well as significant correlations between immersion and fun, as previously found [23], and between perceived competence and performance measures, which suggests that players seem to have a relatively accurate perception of their actual performance.  
There are several possible reasons for this lack of significant effects. It is, of course, possible that there are simply no effects of sound on the game experience at all in LoL or that they are so small that this experiment did not have enough power to find them. Seeing as the effects of sound have been found in other games, however [22; 23], this seems rather unlikely. Another possible reason is the task used, which allowed us to measure gaming performance but also impacted the soundscape. As participants were instructed to stay exclusively in the middle lane of the map, the types of ambient sounds they heard were restricted to mostly wind noises compared to the forest sounds that are present in the jungle parts of the map, for example. This might have made the existing ambient sounds too inconspicuous to have an effect. The task might also have had another effect as participants were instructed to land as many _last hits_ as possible, their attention might have been completely focused on the task at hand, limiting the cognitive resources available for a deeper experience of the game world around them.  
To investigate whether similar effects of sound as found in other studies can be observed in LoL without the gameplay restriction, we conducted a second experiment where participants were free to explore the entire map of LoL.
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 6 Numerical Landslide Predictions  
Numerical simulations demonstrated that Case 3 (the reservoir water level slowly reduced from 175 m to 145 m, accompanied by the 50-year rainfall) was the most detrimental to the Huasushu slope's stability.  
We extracted data from surface displacement monitoring points GPS2-1 and GPS2-2 (Figure 22) on the main sliding section plane of the slope and the equivalent plastic shear strains at the representative points on the sliding belt under the most unfavorable working condition (Figures 23 and 24). A numerical prediction model for the deformations and failure of the slope was established by fitting these data.  
Figure 21: (**a**) Stability coefficient for a 175 m to 145 m water level drop. (**b**) Stability coefficient for a 162 m to 145 m water level drop.  
To obtain a prediction model for the equivalent plastic strain, representative points A, B, and C on the sliding belt were selected for analysis, as shown in Figure 25. The trend of the equivalent plastic strain at each point is shown in Figure 26.  
Figure 23: (**a**) Horizontal displacement at monitoring point GPS2-1. (**b**) Vertical displacement at monitoring point GPS2-1.  
Figure 24: (**a**) Horizontal displacement at monitoring point GPS2-2. (**b**) Vertical displacement at monitoring point GPS2-2.  
Figure 22: Location of monitoring points in a three-dimensional model.  
Comprehensive monitoring of displacement and equivalent plastic strain trends shows that under Case 3, the displacement at monitoring point GPS2-1 at the middle and upper parts of the landslide was significantly larger than at the front monitoring point, GPS2-2, indicating that the displacement was mainly concentrated in the middle and upper parts of the slope.  
In Case 3, the equivalent plastic strains at the rear point, C, and central point, B, of the sliding belt both exceeded zero, while the equivalent plastic strain at the front point, A, was zero, indicating that the slope damage mainly occurred in the middle and upper parts, but an overall landslide did not occur. Further analysis shows that the rear of the slope is steeper and exhibits a larger range of tensile shear damage, while the middle of the slope is slower, and the scope of the damage is less extensive.
## 2 Experimental Program  
### Materials  
Ordinary Portland cement (PC) complying with ASTM C150 specifications was used as the main constituent for the binder formulated with silica fume (SF) and class F fly ash (FA) as supplementary cementitious materials. Table 1 lists the physicochemical properties of the employed types of cement. The specific gravities of PC, SF, and FA were 3.15, 2.2, and 2.7, respectively. Furthermore, Arabian Peninsula-based sands [characterized as red dune (RS) and white (WS)] were employed as fine aggregates. The specific gravities of RS and WS at saturated surface dry conditions (SSD) were 2.65 and 2.74, respectively. An Axios Max X-ray fluorescence (XRF) machine was utilized to determine the chemical composition of the binder constituting powders. The particle-size distribution (PSD) analysis of the fine powders was conducted using a laser diffraction particle size analyzer (LA-950). Microstructural analysis was conducted employing a Versa 3D dual beam field emission scanning electron microscope (SEM). Figure 1a depicts the grain size distribution curves for PC, FA, and SF, whereas Figure 1b and Table 2 illustrate the particle size distribution analysis and physical properties of the employed aggregates.  
In this experimental investigation, a hybrid system of three fibers (designated as A, B, and C) bright high-carbon and high-performance strength microsteel straight fibers were used as discontinuous reinforcement of the developed UHPC mixes. The physical and mechanical properties (as received) of the microsteel fibers are given in Table 3. The mix design of the UHPC is detailed in Table 4. It is worth noting that a polycarbonate ether-based water reducer superplasticizer [commercially recognized as Master Glenium 51 (Master Builders Solutions UK Ltd, Manchester, UK)] was identified as SP and employed in the current study to control the workability of the prepared mixes. This SP had a density and water content of 1080 kg/m\({}^{3}\) and 65.19%, respectively. To this end, the quantity of water contained in the SP was corrected in the calculation of the amount of mixing water after the water-to-binder ratio was optimized as 0.165.
## 3 Privacy and Security Threats in IoT Domain
### Privacy Attacks  
Smart home systems, which collect and process data through different sensors and devices, present considerable possibilities for enhancing home management and security. However, they also expose individuals to a wide range of serious privacy and security vulnerabilities. Therefore, developing more secure systems requires a thorough understanding of these vulnerabilities and potential privacy attacks.  
* The devices typically gather sensitive information about user behaviors, routines, and preferences. Improper protection or sharing of data can lead to privacy leakage.
* Most devices store and process information in cloud services. If these cloud assistants were insecure, it would be simple to access the stored information or even modify the device configuration.
* Attackers could monitor smart home devices' data to track users' habits, routines, and lifestyles, targeting them with stalking and burglary advertisements. This could help an attacker intercept communication on smart home devices to steal or manipulate sensitive data.
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
###### Abstract  
In this study, the characteristics of the alkali-silica reaction (ASR) expansion of steel slag itself, mortar bars, and concrete specimens using steel slag as aggregate are individually investigated by the expansion test method, to determine if steel slag aggregate in concrete can provide volume stability. The results show that when steel slag is aged in water for one month, its self-expansibility is below the permitted limit of 1.5% according to the JIS A 5015 standard. The ASR test results show that the ASR expansion of the mortar bars continuously increased with the increase in the test period. However, all mortar bars were below the permitted limit of 0.1% after 14 days according to the ASTM C 1260 standard. In contrast, the ASR expansion of concrete specimens was above the criteria prescribed by the ASTM C 1293 standard. From the results, the expansion of concrete specimens could not be controlled within the permitted limit by the ASTM 1293 criteria, even if the expansivity of steel slag did not exceed the criteria. Meanwhile, considering the crack propagation patterns of the concrete specimens, the cracks due to ASR expansion developed and connected even when the expansion was below the permitted limit. Besides, when mineral admixtures were used as the binder in the concrete specimens, there were discrepancies in the results between the expansion rate and the crack properties, such as maximum length and total crack length. Therefore, to accurately determine the change in volume due to ASR expansion in concrete using steel slag as coarse aggregate, it is necessary to check the crack patterns in addition to evaluating the expansion rate.  
Keywords:expansion properties; steel slag; immersion expansion in water; alkali-silica reaction (ASR); concrete expansion due to ASR +
Footnote †: journal: Applied Sciences
## 8 5G Potentials and Promises for Robot Applications
### Wide Area Coverage and Inexpensive Localisation Systems  
Compared to other technologies such as Wi-Fi, UWB, etc., 5G will be available almost anywhere, indoors or outdoors, since the cellular infrastructure is widely deployed in cities. Using Vehicle-to-Everything (V2X) also makes it feasible to take advantage of 5G in areas without full coverage. For example, in a collaborative scheme, parts of the device(s) or vehicle(s) can play the role of anchor or Pseudo BS for others. 5G is also considered to be an inexpensive solution because there is no specific equipment to set up as long as operating under the coverage of BS. Accordingly, taking advantage of the available infrastructure of 5G, in some cases, might remove the need for costly and energy-consuming GPS devices.  
In addition, 5G confers robustness to the localisation system. Robustness is an essential feature in highly mobile scenarios, in the case where safety is of great concern. For example, consider a vehicle or a group operating in a wide area where some part of it might be GPS denied. In such a situation, relying only on GPS information may lead to failure.
## 3 Nonlinear Modeling of the Semi-Active Energy Regenerative Suspension with an EHA
### Parameter Identification of Nonlinear Model  
In order to provide experimental sample data for the model parameter identification, a physical prototype of an EHA, which is mainly composed of a hydraulic cylinder, a hydraulic motor, and a brushless DC motor, is developed, and according to the national standards of QC/T545-1999 \"Test method for automobile telescopic shock absorber\", a force characteristics test of an EHA semi-active suspension system is carried out on an ES-6-230 numerical control hydraulic vibration table as shown in Figure 4. In the experiment, the vibration table input excitation uses sinusoidal input at the frequency of 1 Hz and the amplitude of 30 mm. An LTR-1 pull pressure sensor is used to collect the suspension's output force signal in the passive state, and the change of the suspension's output force with time is shown in Figure 5. In order to eliminate the influence of initial conditions on the test results, the sampling time starts from 5 s.  
The parameters of the nonlinear model are continuously approximated by the least squares method based on the experimental data, and the objective function of the algorithm is expressed as:  
\[\{k_{s},c_{s}\}=\underset{k_{s},c_{s}}{\text{argmin}}\|F_{c}(k_{s},c_{s})-F_{e }\|_{2}^{2} \tag{4}\]  
where \(k_{s}\) is the equivalent spring stiffness, \(c_{s}\) is the inherent damping coefficient of the system, \(F_{c}\) is the theoretical model value, and \(F_{e}\) is the actual test value.  
Figure 4: Force characteristic test of the EHA semi-active suspension system.  
Figure 5: Comparison of simulation and experimental results of output force.  
Through the identification of the parameters, the equivalent spring stiffness \(k_{s}\) of the EHA semi-active suspension system is 13,000 N/m, and the inherent damping coefficient \(c_{s}\) is 500 N \(\cdot\) m/s. The above parameters are introduced into the two degrees of freedom suspension dynamics model of the quarter-vehicle, and the simulation results of the EHA actuator output force are compared with the test results, as shown in Figure 5.  
Figure 5 shows that the output force of the EHA actuator obtained by parameter identification is in good agreement with the actual output force, which indicates that the established dynamic model of the two degrees of freedom suspension system for a quarter-vehicle is accurate, and the adopted method of model parameter identification is feasible and effective.
## 4 Column Streaming-Based Mapping Algorithm
### Convolution Computation Mapping Strategy  
Figure 5 shows DycSe's data mapping strategy for convolution computation, achieved by the column streaming methods. Figure 5 is composed of three blocks, input features (I/f), weight filters (W/f), and the PE array (module), where the convolution computation executes; the weight values in the W/f block preload to the PE array in column form. The filter size decides the weight-mapped position and how the PE module is configured.  
A \(k\times k\)-sized W/f is decomposed into \(k\) columns. When \(k=3\) in Figure 5's case, the first column, \(\{w_{0,0},w_{0,1},w_{0,2}\}\) in blue, preloads into the array, and the weight column stays in the PE array until the input feature data finishes the multiplication.  
Figure 5: Convolution computation mapping strategy.  
The input feature is decomposed into \(j\) data (pixel) sets, and a data set is steamed into the PE array at each clock cycle. The variable \(j\) is also decided by the mapping method that, according to the PE module reconfiguration, \(j\) in Figure 5 is 21. The 21 inputs of data are streamed into the PE array in two subsets, through Bus1 and Bus2. Variable \(j\) can be obtained by using (3). In (3), variable \(k\) represents the kernel size, variable \(c\) represents the streaming data set, and variable _method_ represents the mapping methods. Variable \(AH\) means PE array heights.  
\[\left\{\begin{array}{c}j_{k,c,m}=AH\times 2-1,\;\;k\leq 5,\;c=0,\;method=1\\ j_{k,c,m}=AH\times 2-2,\;\;k\leq 5,\;c>0,\;method=1\\ j_{k,c,m}=AH\times 2-k,\;\;k\leq 5,\;c\geq 0,\;method=2\\ j_{k,c,2}=AH\times 2-1-k,\;\;k>5,\;c\geq 0,\;method\equiv 2\end{array}\right. \tag{3}\]