esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Query Combination Behavior in Exploratory Searches
## 3 A Hierarchy of Query Combination Behavior
### A Hierarchy of QCB
* Topic specialization query first: This pattern describes users' issue dominative queries in advance of the topic specialization queries. Users who exhibited  
Figure 2: The hierarchy of QCB. this pattern of QCB were exploring search objects in topics. The most common example of this type of QCB pattern was the search session in Figure 1a. In this session, the CPQ \"dogs\" was a topic specialization query, and the component query \"pitbull\" was a dominative query. The user's information need was to find the information of \"pitbull\" in topic \"dogs\". 2. Relation discovery QCB which falls into this category demonstrates a desire by the user to find the potential relations between CPQs. We found that users exhibit this category of QCB after analyzing the search results yielded by CPQs and learning the relations between CPQs, having discovered the objects that they were seeking. We also found that CPQs in these QCB sessions are semantically similar. According to the kinds of relations between CPQs, two patterns emerged: * Similarity-oriented pattern: The search objects of users who exhibit this pattern are seeking similarities between CPQs. The commonest example of this pattern is the QCB in Figure 1b. As we discussed above, the user was exploring to find musical resources about the song \"Top of the world\", and the similarity relation between the two CPQs is the name of the song--this is evidence that the user was exploring to find the similarities of the CPQs by combining the CPQs. * Distinction-oriented pattern: The search objects of users who exhibit this pattern are seeking distinctions between CPQs. For example, a search session including <\"estrogen\", \"progetin\", \"estrogen and progestin\">, where the user was exploring to find distinctions between \"estrogen\" and \"progetin\", would be included in this pattern. In this session, neither the CPQ \"estrogen\" nor the CPQ \"progetin\" was the search object that the user had in mind. In fact, what the user really wanted was to explore the distinctions between the two CPQs. 3. Query diversification QCB which falls into this category demonstrates a desire by the user to discover potential information which could not be found by issuing CBQ. Note that users who perform this QCB category are not exploring relationships between CPQs but discovering the new meaning of the CBQ. Therefore, CBQ in this pattern usually contains meanings that cannot be found in the search results for each CPQ. There is only one pattern in this category of QCB, as follows: * Diversification mining: The most common example of this type of QCB pattern may be the search session <\"black\", \"sheep\", \"black sheep\">. The combined query \"black sheep\" contains a new meaning, which is not present in \"black\" and \"sheep\".
# A Constrained Louvain Algorithm with a Novel Modularity
## 2 Related Work
### Two Optimization Algorithms  
At present, two optimization algorithms are popular. One is the [PERSON]'s fast algorithm, which adopts the greedy technique. Another is the Louvain algorithm. The simple description is given below.  
[PERSON]'s fast algorithm:  
In 2004, [PERSON] proposed a fast algorithm to detect the community structure. The algorithm starts with a state in which each node is a single community. Then, the communities are joined together in pairs at each step so that \(Q\) has the greatest increase. The progress can be represented as a \"dendrogram\". Generally, we can select the best community partitions by looking for the maximal value of \(Q\).  
Louvain algorithm:  
The Louvain algorithm is an optimization algorithm based on modularity \(Q\). It detects communities through removing the node to its adjacent community to maximize the modularity \(Q\).  
The Louvain algorithm consists of two phases. Phase one aims to optimize the modularity by iteratively removing nodes. (1) Initially, each node is assigned to a community. For each node \(i\), calculate the modularity gain \(\Delta Q\) by removing it to the community of its neighbor \(j\). Find the maximal gain \(\Delta Q_{max}\) of modularity. If \(\Delta Q_{max}>0\), remove the node \(i\) to the community of the neighbor with \(\Delta Q_{max}\). (2) Repeat (1) until the modularity cannot be increased by removing any one node. Phase two constructs a new network, whose nodes are the communities found in phase one. The new weights of the links are obtained by summing up the weights between the nodes of two corresponding communities. Moreover, the self-loop of a node is obtained by summing up the weights of links within the corresponding community. We executed phase one and phase two iteratively until the modularity was not improved.  
The Louvain algorithm is very efficient, because the number of community is decreased drastically and the calculation of the modularity gain is simple. However, it is possible that the nodes in different communities is merged in the final partition due to the resolution limit of modularity \(Q\).
## 2 Materials and Methods
### MODIS Vegetation Indices  
In this study, the monthly optical vegetation indices NDVI and kNDVI for the period 2001-2020 were synthesized based on the daily MODIS Surface Reflectance (SR) product MOD09A1 V6 ([PERSON] et al., 2017), using monthly averaged method. These products have a spatial resolution of 500 m. The kNDVI, as proposed by [PERSON] et al. (2018), is a novel index designed to address the saturation effects often encountered with NDVI in high-biomass regions. It incorporates kernel methods to effectively handle the non-linear relationships present in vegetation reflectance data, thus enhancing the performance of vegetation indices in areas with dense biomass.  
The kNDVI uses a Gaussian kernel function to modify the NDVI calculation, allowing for a more accurate representation of biomass changes in densely vegetated areas. The calculation formula for kNDVI is given by:  
\[\text{kNDVI}=\tanh\left(\left(\frac{n-r}{2\sigma}\right)^{2}\right) \tag{1}\]  
In this formula, (\(\sigma\)) is typically simplified for ease of calculation, set as the average of the Near-Infrared (NIR) and red reflectance, i.e., (\(\sigma=0.5*(n+r)\)). However, as indicated by research from [PERSON] et al., this oversimplification can reduce the effectiveness of kNDVI, suggesting that adjustments to \(\sigma\) should be made for specific applications ([PERSON] et al., 2017). In this study, a distance heuristic estimation method is employed to estimate the (\(\sigma\)) parameter. This method calculates (\(\sigma\)) based on the average distance between all pixels in the region:  
\[\sigma=N^{-1}\sum_{i}^{N}\left|NIR_{i}-red_{i}\right| \tag{2}\]  
Given that kNDVI is significantly influenced by non-vegetated area pixels ([PERSON] et al., 2017), we applied the pixel quality assurance (QA) layer of MOD09A1 to identify and exclude pixels marked as clouds, cloud shadows, aerosols, and snow, ensuring the reliability of the kNDVI calculations in our study.
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 3 Response Surface Analysis
### UCS of Geopolymer-Stabilized Soft Soils
#### 3.1.2 Effect of Alkaline Activator Modulus on UCS of Stabilized Soil  
The alkali activator modulus is the molar ratio of SiO\({}_{2}\) to Na\({}_{2}\)O, and it is one of the important parameters of a geopolymer alkali activator. Figure 2 shows the variations in the UCS of the geopolymer-stabilized soil when the modulus of the geopolymer alkaline activator increased from 0.6 to 1.0 with a 7% alkali activator content. As the modulus increased, the UCS of the stabilized soil initially increased and then decreased, and the UCS of the stabilized soil reached the maximum when the modulus was 0.8. These results indicate that for FA-based geopolymer-stabilized soils, the optimum modulus is 0.8, and a higher modulus weakens the alkalinity and reduces the dissolution rate of the geopolymer silica-alumina materials (FA and slag), which affects the strength of the stabilized soil. OH\({}^{+}\) will react with the metal cations during the clay reaction, destroying the soil's structure and inhibiting the growth of the gel phase material, thus leading to a reduction in the strength of the stabilized soil [29]. When the modulus is less than 0.8, the alkalinity of the material is too strong, the content of the active [SiO\({}_{4}\)\({}^{4-}\)] in the reaction is reduced, and the OH\({}^{+}\) reacts with the metal cations in the clay, destroying the soil's structure and inhibiting the growth of the gel phase substances. This results in a decrease in the strength of the stabilized soil.  
Figure 1: UCS of stabilized soils with different slag contents.
# Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review
## 3 Results
### Demographic Analysis of the Experiments  
Within the compiled body of studies, a discernible pattern emerged: experiments pertaining to the measurement of SA in drivers via eye tracking, along with metrics analyzing driver behavior covering aspects such as hazard perception, attention allocation, driver distraction, visual perception, and vigilance, consistently featured a selection of two distinct vehicle categories for experimental trials--commercial and industrial vehicles.  
For the scope of this review, commercial vehicles refer to vehicles designed to transport products or passengers with or without business purposes, and industrial vehicles refer to any vehicle that is used to lift, stow, load, push, or stack materials. Within the sample of 35 articles that dealt with driving experiments, 29 of them were based on commercial vehicles, which represent 85.4% of the sample; the remaining 14.6% of the sample dealt with industrial vehicles.  
The primary category of industrial vehicles considered in studies on SA included covered forestry harvesters, wheel loaders, excavators, dump trucks, wheeled excavators, mobile cranes [18], truck platooning [27], forklifts [28], and crawler and wheeled excavators [29; 30]. The commercial vehicles were mainly passenger and goods vehicles [31; 32; 33; 34], school vehicles [35], and automated vehicles [36; 37; 38].  
Conversely, with regard to the gender-based distribution of participation, a discernible and noteworthy trend was observed in the significant involvement of male participants. All the selected studies taken together involved 906 participants. In the experiments on commercial vehicles, the participants were composed of 543 men and 334 women. Similarly, the participants in the experiments on industrial vehicles comprised three women and 26 men, as presented in Figure 6.  
The age range of participants revealed that the average ages of male and female drivers were 26.44 and 27.94 years, respectively. In some of the studies, participant eligibility criteria included being the age of 18 years or more and holding a valid driver's license, as per the data collection protocol in those studies [36; 39].  
Figure 5: Keywords co-occurrence analysis publications. The first category (red) deals with the examination of SA, physiological measurement, and driver behavior. The second category (blue) deals with eye tracking, driver behavior, pupillometry, and visual perception. The third category (orange) deals with driver gaze behavior, human–vehicle interactions, and accidents. The fourth category (green) deals with real-time SA, human factors, and driver support. Source: own elaboration using Bibliometrix.
## 3 Controller Design
### PSO-Fuzzy PID Controller
#### 3.3.1 Particle Swarm Optimization Algorithm  
PSO is a metaheuristic optimization technique inspired by the collective behavior of birds, iteratively refining candidate solutions to achieve optimal performance. Each particle in the swarm updates its position based on both its personal best-known position and the globally best-known position, facilitating the efficient exploration of large solution spaces [61]. The PSO method, widely used in nonlinear and power system applications [62; 63], was initially proposed in 1995 [64] and later improved in 1998 with the introduction of an inertia weight coefficient to enhance performance [65]. In the swarm, each particle searches for an optimal position by continuously updating its location according to its best-found position and the global best position. In the PSO algorithm, each particle searches for an optimal position by continuously updating its velocity and position according to its best-found position and the global best position. The velocity and position updates follow these equations [66]. \[vi(k+1)=W\;vi(k)+C1R1(gbest-Xi(k))+C2R2(Pbest-Xi(k)) \tag{4}\]  
\[xi(k+1)=xi(k)+vi(k+1) \tag{5}\]  
\[i=1,\;2,\ldots,\;n\]  
where  
* \(vi\) is the velocity of the \(ith\) particle,
* \(w\) is the inertia weight factor,
* \(k\) is the iteration number,
* \(C1\) and \(C2\) are cognitive and social coefficients,
* \(R1\) and \(R2\) are random variables from 0 to 1,
* \(xi\) is the position of the \(ith\) particle,
* \(pbest\) is the individual best position of particle \(i\),
* \(gbest\) is the best global position of all particles in the swarm,
* \(n\) is the total number of particles. The position of a particle is updated if the following condition is met:  
\[f(xi)<f(P_{best}) \tag{6}\]  
If it is true, the position update is given by  
\[xi=P_{best} \tag{7}\]  
In optimization methods, system performance is typically assessed using a variety of fitness criteria, including integral square errors (ISE), integral absolute errors (IAE), integral time absolute errors (ITAE), and integral time square errors (ITSE). Parameters such as overshoot, undershoot, steady-state error, settling time, and general tightness of the control system are all considered by these fitness criteria [67]. The minimization objective fitness function, donated as \(f\), is used to evaluate the performance of the system. In this study, the output response performance of the system is assessed using the ITAE fitness function, as given in Equation (8). \[\text{ITAE}=\int_{0}^{t}t\;|e(t)|\cdot dt \tag{8}\]where \(e(t)\) is the error signal at time \(t\), and the integral is computed over the entire time period of interest. This fitness function is used to guide the optimization process in PSO. To enhance clarity, Algorithm 1 provides a step-by-step illustration of the PSO algorithm.
## 5 Results
### The Spatial and Temporal Change of GRACE/Swarm-DSI  
Figure 8 shows the time series of GRACE/Swarm-DSI, SCPDSI, SPI-3, SPEI-6 and SPIEI-12 in the ARB during 2003 and 2020. We found that GRACE/Swarm-DSI and four drought indices have the similar change trend. To quantify the relationship between GRACE/Swarm-DSI and four drought indices, we calculated the correlation coefficients and NSEs between them (Table 5). From Table 5, it suggests that GRACE/Swarm-DSI has the strongest correlation with SPIEI-12 (0.6820), followed by SPEI-6 (0.6377), SCPDSI (0.6345) and SPEI-3 (0.5411). It explains that GRACE/Swarm-DSI has a strong correlation with the  
Figure 8: The time-series of GRACE/Swarm-DSI, SCPDSI, SPEI-3, SPEI-6 and SPEI-12 in the ARB during 2003–2020.  
Figure 7: The spatial distribution of uncertainties of GRACE-DSI and Swarm-DSI in the ARB.  
four drought indices. The NSEs between GRACE/Swarm-DSI and SCPDSI, SPEI-3, SPEI-6, SPEI-12 are 0.3348, 0.2783, 0.4044 and 0.4627, respectively. It means that GRACE/Swarm-DSI is within the acceptable range. Overall, it's reliable that GRACE/Swarm-DSI can be used to detect the drought events in the ARB.  
We plotted the spatial distribution maps of long-term trend change and acceleration of GRACE/Swarm-DSI in the ARB during 2003 and 2020 (Figure 9). From Figure 9a, there is an increasing trend in the southwest ARB and the long-term trend changes are between 0.06 and 0.10. A decreasing trend appears in the southeast ARB, and the long-term trend changes in this region are between \(-0.06\) and \(-0.04\). The long-term trend changes in the other study regions are between \(-0.02\) and 0.04, which are smaller than those in the southwest and southeast ARB. We used the Mann-Kendall trend test [54] to analyze the change trend of GRACE/Swarm-DSI series in the green region in Figure 9a, and the result shows \(|Z_{S}|<1.96\), which means that there is no significant change trend in this region at the 95% significance level [55]. Figure 9b shows the accelerations of GRACE/Swarm-DSI are negative, whose values are very small (between \(-0.016\) and \(-0.004\)). Among them, the maximum (\(-0.004\)) occurs in the southwest ARB, while the minimum (\(-0.016\)) appears in the southeast ARB. Combining Figure 9a,b, we found that the increasing trends in the southwest and the decreasing trends in the southeast ARB have slowed.
# Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
## 2 Materials and Methods
### Research Tools  
#### 2.3.1 Enode Sensor  
The Enode Sensor (formerly known as VmaxPro) is a wearable inertial measurement unit (IMU) equipped with an accelerometer and gyroscope, specifically designed for sports performance monitoring and programming. It tracks various aspects of athletic movements, particularly those involving explosive power, such as jumps, sprints, and lifts [42].  
The Enode Sensor is a small, lightweight, and portable device with dimensions of 44 \(\times\) 27 \(\times\) 13 mm and a weight of 16 g, making it easy to attach to the body or sports equipment. It features a 12-h battery life, a wireless connectivity range of 15 m, and a measuring range of >0.15 m/s, enabling the accurate measurement of rapid movements. The sensor connects to a smartphone or tablet via Bluetooth, allowing for real-time data transfer and analysis through the Enode application, which provides detailed visualizations and performance metrics. In this study, the Enode app was operated on an iPhone 14 Pro. After establishing a Bluetooth connection, the application displayed a real-time 3D representation with six marked sides for proper sensor orientation. The sensor was briefly placed on each side for precise calibration, ensuring accurate measurements. Triaxial acceleration data were collected by the sensor's inertial measurement unit (IMU) at a sampling rate of 1000 Hz and transmitted via Bluetooth (\(\sim\)65 Hz) to the iPhone 14 Pro. These features, combined with its compact design and ability to operate within a temperature range of 0-40 \({}^{\circ}\)C, ensured the device's reliable performance under standard testing conditions [42].  
The Enode Sensor measures parameters, such as jump height, flight time, peak power, and force production, during movements, like the countermovement jump. It can also track metrics, such as acceleration and top speed, during sprints and monitor lift velocity and force to optimize training loads and techniques. Furthermore, it analyzes jump biomechanics, including eccentric and concentric phases, to enhance technique and performance [42].  
For countermovement jump assessments, the Enode Sensor must be attached close to the center of mass. This placement minimizes measurement errors by capturing the athlete's total body movement during the jump. Ideally, the sensor is positioned on the side above the hip of each participant, following a standardized protocol to ensure consistent placement and minimize variability. This setup was achieved with the Enode Hip Strap, which has a quick-release buckle, allowing for secure attachment of the sensor and quick changes between athletes [42].
## 2 SPH-FEM Coupling Analysis Method  
### Introduction to SPH Method  
A system state in SPH is represented by a set of particles that can be assigned material properties and interact within the scope of a smoothing function or weighting function. The motion of these particles is governed by the SPH formulation of the physical partial differential equations that characterize the fluid flow. Substitute the SPH approximation into the N-S equation with a function and its derivative, thereby transforming the SPH governing equation of motion into the N-S equation [26].  
\[\frac{D\rho_{i}}{Dt}=\sum_{j=1}^{N}m_{j}\upsilon_{ij}^{\beta}\frac{\partial  
\[\frac{De_{i}}{Dt}=\frac{1}{2}{\sum\limits_{j=1}^{N}m_{j}}\Bigg{(}\frac{P_{i}}{\rho_ {i}^{2}}+\frac{P_{j}}{\rho_{j}^{2}}\Bigg{)}\sigma_{ij}^{\beta}\frac{\partial W_{ ij}}{\partial x_{i}^{\beta}}+\frac{\mu_{i}}{2\rho_{i}}\epsilon_{i}^{\alpha\beta} \epsilon_{j}^{\alpha\beta} \tag{3}\]  
In the formula, \(\rho_{i}\), \(\
u_{i}^{\alpha}\), \(e_{i}\) are the density, velocity component and internal energy of particle \(i\), respectively; \(N\) denotes the number of particles; \(\alpha\) and \(\beta\) denote the coordinate directions; \(x_{i}^{\beta}\) is the coordinate component of x along \(\beta\); \(P_{j}\) denotes the isotropic pressure; \(W_{ij}\) denotes the smooth function between particles \(i\) and \(j\) and \(m_{j}\) denotes the particle mass.  
From Formula (1) to Formula (3), it can be found that SPH can calculate the speed and energy changes of particles in the system, and the calculated data between particles can be connected and interact with each other, and the entire interaction process changes with time. Further, the meshless feature of SPH allows direct processing of large deformations, avoiding the problem of large deformation and distortion of the mesh in numerical calculations.  
Specifically, when the SPH calculation method is applied to blasting, the SPH method can reflect the large deformation in the near area of the blasting through particle motion. Compared with the finite element model, the large deformation and distortion of the mesh are avoided. SPH has the advantage of reflecting the large deformation of blasting through particle motion, which gives the SPH calculation method obvious advantages compared with the finite element method in the calculation of the blasting near area.  
Although SPH has the advantage of simulating large deformations, it suffers from severe boundary defects in the far field of the computational model due to particle inconsistencies. In order to overcome this defect, a coupled analysis method using the SPH method in the near field and the FEM method in the far field was developed and applied. This method combines the advantages of SPH simulation of large deformation and the more stable advantages of finite element boundary calculation, which can effectively overcome the problems of large deformation distortion and boundary instability. Under the action of blasting, the schematic diagram of the deformation of the near and far areas of blasting is shown in Figure 1.
# A Survey of Vehicle to Everything (V2X) Testing
## 4 The Need for V2X Testing
The performance testing is mainly used to test the performance of the V2X network in different scenarios, including latency, communication range, packet loss rate, etc., to ensure that communication can meet the needs of the applications. Ensuring the safety, effectiveness and reliability of V2X applications is also an important goal of testing. The function testing can determine whether an application is valid, whether it can be triggered correctly in a specific scenario, and whether it can ensure vehicles safety. Malicious applications will be removed after function testing. In general, we need to test functionality, performance, interoperability and consistency of the V2X terminal.