esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Furthermore, we found that our proposed threshold-setting method usually yielded more liberal RT thresholds than the NT methods. The NT methods produced smaller RT thresholds and flagged a fewer number of students for exhibiting rapid guessing (less than 10% for NT10, NT15, and NT20 and less than 15% for NT25), whereas our proposed approach produced relatively larger RT thresholds across all items and flagged a larger number of students (between 5% and 15%). At the model level, the adjusted IRT models for both our proposed method and the NT methods yielded better model-data fit than the baseline model, indicating the positive impact of removing rapid guesses on the response dataset. The genetic algorithm produced the best model-data fit indices among the six threshold-setting methods, including random search and the NT methods. The superiority of the genetic algorithm over random search was an expected finding, because random search, despite its efficiency, does not guarantee the best results, especially when the search space is large [57]. On the contrary, the genetic algorithm performs a more systematic search of the target parameter, albeit in a slower search process. Finally, the ability estimates produced by the baseline and adjusted IRT models showed that bias in ability estimation, especially for low-achieving students, was reduced when the threshold-setting methods were used for removing rapid guesses from the response dataset. This finding was in line with the previous studies that reported the positive impact of removing rapid guesses on the recovery of item and ability parameters (e.g., [58]). In conclusion, the findings of this study suggest that our data-driven approach based on the random search and genetic algorithms can be an effective solution for detecting rapid guessing in low-stakes assessments. With our proposed method, researchers can define a reasonable \"search space\" for potential RT thresholds and allow the optimization algorithms to search for the most optimal threshold for each item. This could prevent researchers from making the subjective assumption that the same criterion (e.g., 10% of the average RT value as in the NT10 method) can correctly identify rapid guessing across all items (e.g., [10]). Furthermore, our proposed method enables researchers to consider the impact of rapid guessing on the psychometric model underlying the low-stakes assessment while searching for the optimal RT threshold for each item. Our findings suggest that researchers can combine data-driven approaches such as random search and genetic algorithm with psychometric theory to detect rapid guessing more accurately in low-stakes assessments, instead of simply evaluating the impact of rapid guessing on various psychometric indices such as reliability (e.g., [59]).
## 2 Theoretical Analyses
### Fill State in Backfilled Openings
As the value of \(\varphi^{\prime}\) increases, \((K_{0})_{\rm v}\sigma^{\prime}{}_{\rm v}>K_{\rm a}\sigma^{\prime}{}_{\rm v}\), and the backfill remains in an at-rest (elastic) state (with \(K=(K_{0})_{\rm v}\)). For both cases illustrated in Figure 1a,b the theoretical condition for distinguishing between the at-rest and active states is given by \((K_{0})_{\rm v}\sigma^{\prime}{}_{\rm v}=K_{\rm a}\sigma^{\prime}{}_{\rm v}\) (or \((K_{0})_{\rm v}=K_{\rm a}\)). Combining Equations (2) and (4) leads to the following relationship:  
\[\frac{\
u}{1-\
u}=\frac{1-\sin\varphi^{\prime}}{1+\sin\varphi^{\prime}} \tag{5}\]  
For a given friction angle \(\varphi^{\prime}\), the critical value of Poisson's ratio \(\
u_{\rm c}\) of the fill can be given by rearranging Equation (5):  
\[\
u_{\rm c}=\frac{1-\sin\varphi^{\prime}}{2}\] (6a) For a given Poisson's ratio \(\
u\), the critical value of the internal friction angle \(\varphi^{\prime}{}_{\rm c}\) becomes:  
\[\varphi^{\prime}{}_{\rm v}=\sin^{-1}(1-2\
u) \tag{6b}\]  
Figure 1: Stress states in backfills along the vertical centerline (VCL) of the opening illustrated by Mohr-Coulomb planes, with constant (**a**) internal friction angle \(\varphi^{\prime}\) and (**b**) Poisson’s ratio \(\
u\). The dotted line circle indicates an active state while the solid line circle indicates an at-rest state. Equation (6) can thus be used to calculate the critical values of \(\varphi^{\prime}\) or \(\
u\) that define the transition between an at-rest state and active state near the center of backfilled openings with rigid confining walls. This analysis indicates that when \(\
u\leq v_{\rm c}\) or \(\varphi^{\prime}\leq\varphi^{\prime}{}_{\rm c}\), the backfill is in an active state because the stress state induces yielding; otherwise, an at-rest state prevails (as illustrated in Figure 1). Sometimes, the values of angle \(\varphi^{\prime}\) and Poisson's ratio \(\
u\) can be linked through a unique (and consistent) value of \(K_{0}\) (i.e., \((K_{0})_{\varphi^{\prime}}=(K_{0})_{\
u}\)), based on Equations (3) and (4) for an elastoplastic model. This would lead to a special situation, as shown in the following relationship [27; 37; 38; 39; 40; 41]:  
\[\
u=\frac{1-\sin\varphi\prime}{2-\sin\varphi\prime} \tag{7}\]  
As the \(\
u\) value given by this equation always exceeds that of Equation (6a), the fill state associated with Equation (7) corresponds to an at-rest condition (with \(K=(K_{0})_{\
u}=(K_{0})_{\varphi^{\prime}}\)) [42; 43]. The validity of this theoretical analysis is further assessed below using numerical simulations conducted with FLAC 2D.
# Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing
## 3 Autonomous Mobile Robots
### Path Planning  
Path planning can be defined as the determination of the path that mobile robots will follow to reach a target point from a starting point. This process involves the calculation of alternative paths to reach the target and determining which points to pass through one by one. The distance taken must be short, the time to reach the target point must be optimized, and no factors must make the path difficult [43]. Different approaches based on different distance definitions are used to calculate the path between the robot and the target [40; 41]. A hybrid system for cooperative driving systems, combining discrete events and continuous vehicle dynamics, was presented [44]. A control algorithm combined the artificial potential field (APF) approach with model predictive control (MPC) to achieve synchronous path planning and motion control [44].  
In this study, the A* algorithm is used for path planning. The A* algorithm is a heuristic method that computes the path between projected starting and destination positions at the lowest cost [45]. It has been widely used in the field of mobile robots due to its least-cost trajectory design [45]. A* is an algorithm designed on a weighted graph. It is considered a search algorithm. It is a search algorithm that aims to find a path from a given initial node of a graph to the goal node at the lowest cost (taking the shortest path and the shortest amount of time). To find the path with the lowest cost, it builds a path tree starting from the initial node. These paths are advanced by extending one edge of the graph at a time until the algorithm's performance criteria are met [45]. The algorithm uses heuristics to optimize its search. A function can be defined as one that can estimate, to some extent, the cost from one node to another node. A disadvantage is that all nodes created are kept in memory, which increases the computational load. The cost calculation of each node visited to reach the destination is expressed as shown in Equation (1) [45].  
\[f(n)=g(n)+h(n) \tag{1}\]  
where \(n\) is the current node, \(g(n)\) is the cost from the current node to the initial node-equation, \(h(n)\) is a heuristic function, and \(f(n)\) is the cost from the current node to the destination node. The heuristic function specifies the heuristic cost from a node to a given destination.
## 4 Designed Schemes
### Cooperative Effective Energy and Reliable Deliver (CoEERD) Routing  
This section involves the detail of CoEERD scheme, which is the cooperative version of the EERD scheme. The cooperation of the single RLN with DSN is used for the network reliability. The basic steps of the CoEERD are given below.  
Relay and Destination Node Selection  
This section describes the selection of single RLN and DSN. The selection process of the DSN in CoEERD is similar to the EERD scheme. In CoEERD scheme, when an SRN transmits a CP, all the neighbors get it. The best forwarder nodes (RLN and DSN) are chosen based on the function parameters. The node nearest with respect to the sink node and holding the highest residual energy and lowest BER is marked as DSN. RLN and DSN selection is accomplished based on the information of the nodes about their neighbors.
# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 1 Introduction
To address the aforementioned challenges, this paper employs deep neural networks to adaptively process input signals and automatically extract features, showcasing their significant advantages in image recognition. From the perspective of the entire energy spectrum, the dependency between counts and channel addresses is taken into account. A novel radioactive nuclide identification method is proposed, which uses the Gram Angular Summation Field (GASF) algorithm for data processing and integrates it with the ResNet deep residual network. First, to address the issue of im137 lanced counts in the energy spectrum, the PAA algorithm is applied for dimensionality reduction. Then, the GASF algorithm is used to transform the one-dimensional data into two-dimensional images, fully leveraging the spatial features and dependencies within the data. Next, the two-dimensional images are fed into a deep residual network, where multiple rounds of convolution automatically extract features. Finally, the Softmax layer is employed for nuclide classification, and the effectiveness and accuracy of the proposed method are validated through comparative experiments.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Cognitive Load Management (Based on Cognitive Load Theory)
#### 4.2.4 Feedback and Self-Assessment in Managing Cognitive Load  
Within the immersive realm of VR language education, feedback and self-evaluation are crucial, especially in balancing cognitive demands. Well-structured feedback mechanisms can significantly slash unnecessary cognitive strain by offering learners clear insights into their performance, highlighting improvement areas and solidifying correct practices.  
Instant feedback in VR, powered by up-to-the-minute analytics and AI-guided evaluations, provides learners with an immediate perspective on their linguistic proficiencies and areas needing attention. This promptness ensures that mistakes are nipped in the bud, averting the solidification of wrong practices. For example, suppose a student recurrently mispronunces a particular term or misapplies a grammar rule. In that case, on-the-spot feedback can steer them correctly, ensuring that these missteps do not evolve into persistent habits.  
Conversely, self-evaluation gives learners the reins, prompting them to introspect on their accomplishments and bolstering metacognitive capabilities. By intermittently scrutinizing their progress, learners can chart achievable objectives, recalibrate their approaches, and take charge of their educational voyage. In the VR milieu, tools such as interactive tests, immersive simulations, and scenario-driven exercises can be harnessed to boost this self-reflection, offering students a chance to measure their grasp in an engaging, lifelike framework.  
By embedding feedback and self-assessment mechanisms in VR language education platforms, cognitive load can be deftly managed, ensuring that students stay focused, enthused, and on a clear path to language proficiency.
## 5 Results on ML/DL-Based Modulation Recognition
### Key Characteristics of Modulation Recognition Techniques  
Table 17 provides a comparison of the key characteristics of modulation recognition (MR) techniques in a typical UWA communication system. These characteristics were already explained in Section 3.2. Table 17 shows that the bandwidths vary across studies, ranging from 1 kHz to 100 kHz, with some studies not specifying bandwidth values. Transmission distances also differ, spanning from a few meters to several kilometers, depending on the experimental setup. The modulation schemes considered include PSK, QAM, FSK, DSSS, OFDM, LFM (Linear Frequency Modulation), CW, and SSB (Single Sideband), showing a wide range of modulation types used in underwater acoustic communication. Some studies focus on a limited set of modulation schemes, while others evaluate multiple types. In summary, these MR methods span 1 kHz-100 kHz bandwidths, cover ranges from meters to over 5 km, and support diverse modulation formats, demonstrating their versatility in UWA systems.
## 4 Materials and Methods
### Phytochemical Analysis
#### 4.2.4 Determination of Antioxidant Capacity by Electrochemical Method  
The electrochemical experiments were performed by using a potentiostat (PGSTAT 128 N Autolab, Metrohm, Belgium) equipped with Nova 2.1.2 software. A three-electrode electrochemical cell was used with a glassy carbon electrode (GCE, 3 mm diameter, BAS) as working electrode, a Pt wire as counter electrode, and Ag/AgCl as reference electrode [72]. The GCE was polished with 2 \(\upmu\)m diamond paste and subsequently rinsed with distilled water and ethanol before each measurement. The electrochemical behavior of the SM extract was analyzed by differential pulse voltammetry (DPV) with the following parameters: start potential \(-\)0.5 V, stop potential +1.5 V, step 0.01 V, modulation amplitude 0.05 V, modulation time 0.05 s, and interval time 0.1 s. Ascorbic acid equivalent antioxidant capacity (AEAC) was used as a standard for the evaluation of extract antioxidant capacity [57]. The calibration curve of ascorbic acid (in the range from 10\({}^{-5}\) M to 5 \(\times\) 10\({}^{-3}\) M) was performed by DPV, obtaining the following equation: y = 0.1836 + 6537.5428 \(\times\) X, (R\({}^{2}\) = 0.998).
# Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si
## 5 Fabrication and Testing
### Test Setup
To calculate directivity from the measured gain values for comparison with the simulated ones, the radiation efficiency of the RFID tag specimen was measured at frequencies of 2.4 and 5.8 GHz using the classic Wheeler Cap method [27; 28; 29]. The antenna under test was placed in a cylindrical box (tin can) of approximately 9.7 cm in diameter and 6 cm in height, covered with a circular aluminum lid with a small opening for the antenna feed coaxial cable. Following the suggestions of [28], care was taken to ensure good electrical contact between the box and the lid, and to place the specimen as near to the center of symmetry of the box as possible. Using the same VNA (PicoVNA 106) with test leads and calibration data as above, the complex reflection coefficient \(|S_{11}|\) was measured. The measured value was subsequently transformed by phase rotation along a lossless transmission line, as suggested in [29], to achieve a good approximation of the antenna input impedance via a series equivalent circuit. Taking the real part of the input impedance calculated via \(|S_{11}|\) as the loss resistance, the radiation efficiency of the tag specimen is given by the standard Wheeler Cap formula [27]:  
\[\eta=\frac{\text{Re}\{Z_{i}\}-\text{Re}\{Z_{w}\}}{\text{Re}\{Z_{i}\}} \tag{10}\]  
where \(Z_{i}\) and \(Z_{w}\) are the input impedances without and with the cap, respectively. It was verified that \(\text{Re}\{Z_{i}\}>\text{Re}\{Z_{w}\}\) at both frequencies of interest, i.e., the measured values were consistent with the underlying assumption of a series equivalent circuit for the antenna under test.
## 3 Result and Discussion
### Principal Strain Rate with Additional Campaign Observation  
The strain was calculated using the VISR algorithm, which calculates the strain value from the interpolated velocity around the Kendeng Fault. Figure 8a is a visualization of the principal strain rate. The visualization shows that the compression and extension processes around the Kendeng Fault have a high spatial variation. Compression and extension values around the Kendeng Fault have varying values, with a range of values in compression of \(-\)2.24 to 0.086 upstream/year and a range of extension values of \(-\)0.73 upstream/year. Large compression strains are also observed around the Grobogan District (111\({}^{\circ}\) E, 7.2\({}^{\circ}\) S) and Nganjuk District (111.9\({}^{\circ}\) E, 7.4\({}^{\circ}\) S).  
Figure 7: Comparison of the strain in this study and previous research: (**a**,**b**) strain visualization of [PERSON] et al. [9], (**c**,**d**) strain visualization of [PERSON] et al. [20], (**e**,**f**) strain visualization of [PERSON] et al. [18], and (**g**,**h**) strain visualization of this research.  
We observed primarily compressional zones along the Eastern Java back-arc thrust, except for the east side of the Cepu and Demak Faults, which showed extensional tendencies (Figure 8). However, those extensions may arise from a need for observation points since the distance between the surrounding stations is relatively far compared to other segments. Therefore, we need an additional observation station across the east side of the Cepu and Demak Faults.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 2 Study Area and Data
### Ground Reference Data  
Because it is difficult to obtain reliable ground truth data to evaluate forest fire detection performance, researchers often use ancillary remote sensed data [20; 21] or smoke plumes from high-resolution images [22] to verify forest fire products. In this study, we obtained ground-based data using visual image interpretation. We made a false color composite image (Figure 4a) using band 7, band 6, and band 5 of the IRS data to serve as a primary reference. The HJ-1B CCD images (Figure 4b) and the official MODIS active fire products (MOD14A1) (Figure 4c) were also referenced. MOD14A1, which offers global daily active fire detections at a 1-km resolution, were downloaded from the Level 1 and Atmosphere Archive and Distribution System (LAADS) website [23]. The ground reference data are shown in Figure 5. The active fire can be found in the four satellite images acquired on 28 April, 29 April, 3 May, and 11 May.  
Figure 3: Brightness temperature (BT) images from band 7 (**a**) and band 8 (**b**) acquired on 22 May 2009.  
Figure 2: Temporal distribution of the images used from the two months. Each spot represents an acquired image.  
Figure 4: Reference images acquired on 29 April 2009: (**a**) HJ-1B IRS false color image (R: band 7, G: band 6, B: band 5), dark blue represents water (green circle 1), blue represents smoke (green circle 2), bright red represents active fire (green circle 3) and reddish brown represents post fire (green circle 4); (**b**) HJ-1B CCD false color image (R: band 4, G: band 3, B: band 2); and (**c**) MODIS fire product.  
Figure 5: Ground reference data showing the evolution of the fire fronts in the Yinanhe forest on: 28 April 2009 (**a**); 29 April 2009 (**b**); 3 May 2009 (**c**); and 11 May 2009 (**d**).
# Adaptive Antenna Array Control Algorithm in Radiocommunication Systems
###### Abstract  
An important element of modern telecommunications is wireless radio networks, which enable mobile subscribers to access wireless networks. The cell area is divided into independent sectors served by directional antennas. As the number of mobile network subscribers served by a single base station increases, the problem of interference related to the operation of the radio link increases. To minimize the disadvantages of omnidirectional antennas, base stations use antennas with directional radiation characteristics. This solution allows you to optimize the operating conditions of the mobile network in terms of reducing the impact of interference, better managing the frequency spectrum and improving the energy efficiency of the system. The work presents an adaptive antenna algorithm used in mobile telephony. The principle of operation of adaptive systems, the properties of their elements and the configurations in which they are used in practice are described. On this basis, an algorithm for controlling the radiation characteristics of adaptive antennas is presented. The control is carried out using a microprocessor system. The simulation model is described. An algorithm was developed based on the Mathcad mathematical program, and the simulation results of this algorithm, i.e., changes in radiation characteristics as a result of changing the mobile position of subscribers, were presented in the form of selected radiation characteristics charts.  
algorithm width; algorithm mean square; adaptive antennas +
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# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Modulation
#### 2.2.3 Input Multiplexing  
When data \(d\) of length \(N\) is submitted for transmission, the 1-D binary sequence is first multiplexed onto \(N_{\text{lo}}\) channels through  
\[\begin{split} d_{\text{mux}}[k,\,l]&=d[n],\quad\text{ where}\\ n&=(k-1)\,N_{\text{lo}}+l,\\ k&\in\big{[}1,\,N_{\text{sym}}\big{]},\\ l&\in[1,\,N_{\text{lo}}].\end{split} \tag{12}\]  
The multiplexed sequence length \(N_{\text{sym}}\) is  
\[N_{\text{sym}}=\bigg{\lfloor}\frac{N}{N_{\text{lo}}}\bigg{\rfloor}, \tag{13}\]  
therefore \(N\) needs to be an integer multiple of \(N_{\text{lo}}\). To assert this, we use a simple zero-padding algorithm. If bits are added, they will remain in the data on reception and need to be removed on a higher level later on. This can be avoided, by matching the data length to the desired number of channels in advance, but for a more flexible and general approach, we implement the zero-padding approach and truncate to byte-sizes of 8.
## 5 Results
### Measurement Validation  
The construct assessment results are presented in Table 2. Cronbach's alpha and composite reliability are used to assess internal consistency. Cronbach's alpha value was 0.929, which is well past the thresholds recommended by [PERSON] [57] and [PERSON] [58]. This indicates a high level of reliability, while the composite reliability (CR) for each measurement dimension exceeded the recommended threshold of 0.7 [59]. A confirmatory factor analysis (CFA) was conducted for a total of four factors and 12 analytical items, including structural, convergent, and discriminant validity. The standardized loading coefficient values for each factor exceeded the recommended threshold of 0.7 [59], indicating a strong correlation. In addition, convergent validity was demonstrated as the average variance extracted (AVE) for all structures [60] ranging from 0.549 to 0.771 and exceeding 0.5, indicating high convergent validity. Subsequently, the discriminant validity was investigated between the four factors using the Pearson correlation coefficient and AVE square root values. As per [PERSON] and [PERSON] [60], the square root of the average variance extracted (AVE) should be greater than its highest correlation between the construct and any other construct. According to Table 3, the discriminant validity test satisfied [PERSON] and [PERSON]'s [60] criteria. In general, the analyses indicate that the scales demonstrated sufficient evidence of internal consistency, convergent validity, and discriminant validity to be included in the model.