esa-sceva/satcom-chunk-collection huggingface dataset:
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
###### Abstract  
Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.  
Mobile robot; SLAM; low-cost; laser range finder; Fast Fourier Transform +
Footnote †: journal: Applied Physics Letters
# Dark Fermentation of Fruit Juice Effluents: Effects of Substrate Concentration
## 2 Materials and Methods
### Analytical Methods  
A comprehensive set of analyses was conducted to determine the concentrations of substrates and products in both the liquid and gas phases. Liquid samples were immediately centrifuged at 13,000 rpm and filtered through a 0.45 \(\upmu\)m membrane. The filtered samples were either analyzed promptly or stored at \(-4\)\({}^{\circ}\)C to prevent degradation. Substrate concentrations, including glucose, fructose, and sucrose, were quantified using high-performance liquid chromatography (HPLC, Agilent, Santa Clara, CA, USA) equipped with a refractive index detector (Series 1200). Separation of the components was achieved with a Zorbax Carbohydrate Column (4.6 \(\times\) 150 mm, 5 \(\upmu\)m) at 35 \({}^{\circ}\)C, using a mobile phase of 84% acetonitrile and 16% water (\(v/v\)) at a flow rate of 1.2 mL/min. Lactic acid was similarly analyzed via HPLC (Agilent) using a UV-DAD detector and a Zorbax SB-Aq column (4.6 \(\times\) 150 mm, 5 \(\upmu\)m), with a mobile phase composed of 99% water and 1% acetonitrile (\(v/v\)) in a pH 2 phosphate buffer (0.05 M).  
For the analysis of acetic, propionic, and butyric acids, gas chromatography (Perkin Elmer) with a flame ionization detector (FID) and a Crossbond Carbowax Column (15 m \(\times\) 0.32 mm ID, 0.25 \(\upmu\)m df) was employed. The oven temperature was initially set to 140 \({}^{\circ}\)C for 1.5 min, followed by a temperature ramp of 25 \({}^{\circ}\)C/min up to 190 \({}^{\circ}\)C, where it was held for 2 min. The injector and detector temperatures were maintained at 200 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively, with nitrogen as the carrier gas.  
Total Suspended Solids (TSSs) were measured by filtering the liquid samples through a Millipore 0.7 \(\upmu\)m membrane, followed by dehydration at 105 \({}^{\circ}\)C for 24 h. The filtered samples were then ignited at 550 \({}^{\circ}\)C for 2 h to determine dry bacterial biomass.  
The composition of gases produced during acidogenic fermentation, specifically H\({}_{2}\) and CO\({}_{2}\), was continuously monitored online using a multi-component gas analyzer (Rosemount Analytical NGA 2000 MLT, Emerson, Ferguson, MO, USA). The gas detection system was connected directly to the bioreactor outlet, with data acquisition and pH control managed via SCADA software, utilizing BioXpert software v1.12 in conjunction with a Biocontroller (ADI 1030 Bio Controller, Delft, The Netherlands).
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Statistical Results
#### 3.2.2 Observation Removal Strategy
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Error Type** & **Threshold Type** & **Parameter Type** & \begin{tabular}{c} **Permutations and** \\ **Combinations** \\ \end{tabular} & \begin{tabular}{c} **Proportion of Days** \\ **with Improvement** \\ \end{tabular} & \begin{tabular}{c} **Best** \\ **Improvement** \\ \end{tabular} \\ \hline \hline \multirow{8}{*}{MT} & \multirow{8}{*}{\
\begin{tabular}{c} Scintillation \\ parameters \\ \end{tabular} } & \(\sigma_{\phi}\) & 8/14 & 84.3\% \\ \cline{3-5}  & & S4 & 4/7 & 79.7\% \\ \cline{3-5}  & & \(\sigma_{\phi}\), S4 & 7/14 & 83.3\% \\ \cline{3-5}  & & ROTI & 8/14 & 87.7\% \\ \cline{3-5}  & & MP2 & 5/14 & 69.7\% \\ \cline{3-5}  & & Standard \\ \cline{3-5}  & & parameters & MP1 & 5/14 & 59.9\% \\ \cline{3-5}  & & MP2, ROTI & 8/14 & 88.5\% \\ \cline{3-5}  & & MP1, ROTI & 7/14 & 91.7\% \\ \cline{3-5}  & & MP1, MP2 & 5/14 & 89.9\% \\ \cline{3-5}  & & MP1, MP2, ROTI & 8/14 & 90.3\% \\ \cline{3-5} Height & & Scintillation \\ \cline{3-5}  & & parameters & \(\sigma_{\phi}\), S4 & 8/14 & 90.4\% \\ \cline{3-5}  & & ROTI & 8/14 & 88.2\% \\ \cline{3-5}  & & MP2 & 3/14 & 23.8\% \\ \cline{3-5}  & & MP1 & 8/14 & 53.4\% \\ \cline{3-5}  & & MP2, ROTI & 8/14 & 84.0\% \\ \cline{3-5}  & & MP1, ROTI & 7/14 & 82.9\% \\ \cline{3-5}  & & MP1, MP2 & 5/14 & 42.5\% \\ \cline{3-5}  & & MP1, MP2, ROTI & 7/14 & 82.9\% \\ \hline \hline \multirow{8}{*}{MT} & \multirow{8}{*}{
\begin{tabular}{c} Scintillation \\ parameters \\ \end{tabular} } & \(\sigma_{\phi}\) & 11/14 & 80.5\% \\ \cline{3-5}  & & S4 & 4/7 & 79.7\% \\ \cline{3-5}  & & \(\sigma_{\phi}\), S4 & 9/14 & 83.6\% \\ \cline{3-5}  & & ROTI & 8/14 & 80.5\% \\ \cline{3-5}  & & MP2 & 5/14 & 68.4\% \\ \cline{3-5}  & & MP1 & 7/14 & 59.8\% \\ \cline{3-5}  & & MP2, ROTI & 8/14 & 87.9\% \\ \cline{3-5}  & & MP1, ROTI & 10/14 & 82.6\% \\ \cline{3-5}  & & MP1, MP2 & 6/14 & 79.8\% \\ \cline{3-5}  & & MP1, MP2, ROTI & 8/14 & 85.3\% \\ \cline{3-5}  & & \(\sigma_{\phi}\) & 9/14 & 79.6\% \\ \cline{3-5}  & & Scintillation \\ \cline{3-5}  & & S4 & 4/7 & 60.8\% \\ \cline{3-5}  & & \(\sigma_{\phi}\), S4 & 9/14 & 81.5\% \\ \cline{3-5}  & & ROTI & 9/14 & 77.9\% \\ \cline{3-5}  & & MP2 & 6/14 & 16.3\% \\ \cline{3-5}  & & MP1 & 8/14 & 75.7\% \\ \cline{3-5}  & & MP2, ROTI & 8/14 & 86.3\% \\ \cline{3-5}  & & MP1, ROTI & 9/14 & 79.2\% \\ \cline{3-5}  & & MP1, MP2 & 5/14 & 35.7\% \\ \cline{3-5}  & & MP1, MP2, ROTI & 8/14 & 83.5\% \\ \cline{3-5}  & & \(\sigma_{\phi}\), S4 & 9/14 & 79.6\% \\ \cline{3-5}  & & & ROTI & 9/14 & 77.9\% \\ \cline{3-5}  & & MP2 & 6/14 & 16.3\% \\ \cline{3-5}  & & MP1 & 8/14 & 75.7\% \\ \cline{3-5}  & & MP2, ROTI & 8/14 & 86.3\% \\ \cline{3-5}  & & MP1, ROTI & 9/14 & 79.2\% \\ \cline{3-5}  & & MP1, MP2 & 5/14 & 35.7\% \\ \cline{3-5}  & & MP1, MP2, ROTI & 8/14 & 83.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the height and 3D positioning errors improvement through observation removal.
# Alfafa Stalk Crushing Simulation Test and Parameter Optimization Method
## 5 Discussion  
At present, scholars have conducted research and achieved many results on the material crushing device. [PERSON] et al. [17] designed a corn straw crushing and scattering device, and through simulation tests and physical tests, they determined that when the crushing knife shaft speed is 700 r/min, the device has better working performance and the corn straw crushing qualification rate is 93.65%. [PERSON] et al. [18] studied the cutting and crushing mechanism of cotton straw based on ANSYS/LS-DYNA 13.0. The results showed that when the rotation speed was 1800 r/min, the crushing pass rate was 84.6% and the removal rate was 95.1%. [PERSON] et al. [19] studied the crushing performance of the square bale straw crusher and analyzed the effect of feeding speed and spindle speed on the crushing effect. Among these existing studies, longitudinal studies were mainly conducted on a specific device, while direct horizontal comparative studies of different structures were rare.  
The crushing device used as a test platform often has a high-speed range and requires certain requirements for the frame. In order to avoid resonance problems, the natural frequency of the frame is often required to be higher than the excitation frequency. Previous research has highlighted the significance of crushing work for the subsequent production of high-quality feed. However, there is a lack of comparative experiments and analysis on the crushing effects of typical crushing structures. In comparison to other research methods, the use of discrete element analysis as a related technology is well-established. Through utilizing EDEM software, this study was able to observe the dynamics of stalk particles during the crushing process. The findings revealed that for certain crushing structures, the average speed of the stalk particles no longer significantly increases when the rotational speed exceeds a certain value. This could be attributed to the fact that when the crushing tool rotates too fast, the stalks maintain a relatively constant speed due to inertia. Consequently, some cutters are unable to effectively contact the stems that have not experienced significant positional changes, resulting in a plateau in the average speed. However, to consider simulation efficiency, this study utilized a smaller number of stalks and focused more on exploring the changes in the crushing effect over time. Therefore, the specific values obtained may differ from those observed during actual crushing operations. In the future, as computer technology and software performance continue to advance, it will be possible to construct a model that closely resembles the actual scenario in terms of quantity and stem appearance, leading to more accurate results. At the same time, for materials similar to alfalfa stalks, more detailed parameters can be further explored based on the results of this study, such as the angle of the blade, the diameter of the cutter head, etc., to promote further optimization of the stalk crushing device.
## 1 Introduction
### Contributions  
All of the aforementioned works are geared toward either conducting modulation identification or STBC recognitions for MIMO systems. However, practical systems necessitate paired conducting modulation and STBCs identifications in order to function properly. There are just two published studies on the joint identification of the previously mentioned parameters, and both of them focus exclusively on single-user and single-carrier transmissions [43; 44]. The following is a list of the notable contributions that this work delivers.  
* We introduce a new strategy for jointly classifying STBC and modulation parameters for usage in multi-user broadcasts of upstream single-carrier frequency division multiple access (SC-FDMA) systems.
* The mathematical investigation that is presented in this work demonstrates that the precise ML solution to this problem is too complicated to be used in practical settings. As a result, we have created a new mechanism that operates in a recursive fashion. The space-alternating generalized expectation-maximization (SAGE) procedure [45; 46] is employed to separate the layered signals that have arrived at the BS.
* The expectation phase of the SAGE algorithm takes advantage of the soft information delivered by the channel decoder in order to limit the amount of multiple access interference that is produced by other users. This results in a search strategy that is comprised of several one-dimensional scans, as opposed to the more complex multi-dimensional scan.
* The proposed method of identification is supplemented by the creation of estimates of the channel impulse responses of the existing users.  
The proposed approach has several benefits, including those listed below.
* As long as the decoding procedure is a soft-decision one [47], the suggested classification method operates with any error-correcting code. Due to this, the suggested algorithm can easily be incorporated into the currently used wireless standards, providing a great deal of flexibility.
* The developed classification algorithm is dynamic in that it can be used to a wide variety of STBCs and modulation techniques with no special adjustments needed.  
The remaining portion of the work is divided up into the following parts. Section 2 explores the formulation of the challenge under consideration and the system modeling. Section 3 details the identification algorithm that is being proposed. Section 4 reports on practical concerns and interpretations. Section 5 presents and analyzes the simulation findings. Section 6 offers the closing observations.
## 3 Results
### Active Nerve Axon  
The numerical solution of Equation (13) demonstrates how the interaction between the activating function and the Hodgkin\(-\)Huxley membrane model leads to the propagation of the action potential (Figure 5). Initially, the passive response resembles the activating function and is proportional to the negative derivative of the electric field, \(-dE_{x}/dx\). Then, after about a 2-ms delay (latency), the action potential fires at the point where \(-dE_{x}/dx\) is positive and propagates in two directions (left and right). The simulation shown in Figure 5 is for a stimulus just above the threshold; stronger stimuli have shorter latencies. The action potential has a peak of about +40 mV and propagates about 5 cm in 5 ms at a speed of approximately 1 cm/ms. After about 2 ms, the action potential repolarizes, becomes refractory, and finally returns to rest. A slightly smaller stimulus does not trigger an action potential, and excitation is an all-or-none phenomenon. This behavior is consistent with that observed by [PERSON] and [PERSON] [PERSON] and [PERSON] (1999). Because we are primarily interested in the excitation of the action potential, we did not study the recovery and refractoriness associated with repolarization.  
Figure 4: The transmembrane potential as a function of spatial frequency \(k\) (Equation (5)). For low values of \(k\), the transmembrane potential is relatively large; however, at high values of \(k\), it is much smaller, suggesting that excitation is easier at small spatial frequencies.  
Figure 5: The propagation of the action potential is due to the activating function for \(b=1\) cm and \(Q=4089\) mV/cm. Near \(t=0\), the transmembrane potential is proportional to the activating function. However, by \(t=2\) ms, depolarization reaches the threshold and triggers an action potential, which then propagates in both directions away from its stimulation site. A slightly weaker stimulus would not have excited the action potential; thus, excitation is an all-or-none phenomenon.  
Our main results are presented in Figure 6 and Table 2, which show that the activating function is affected by the spatial length \(b\) of the electric field gradient. When \(b\) decreases, which is similar to reducing the coil size and the distance between the microcoil and the targeted neuron, the threshold peak of the electric field gradient increases. However, when the electric field distribution is several centimeters or more (as during transcranial magnetic stimulation), increasing \(b\) has little influence on the threshold peak of the electric field gradient (Table 2).
## 1 Introduction
[18] found that nS effectively improved the mechanical properties of mortars mixed with recycling plastic waste, but also increased the self-shrinkage. Although many studies have been done on the autogenous shrinkage of UHSC, these previous studies showed some weak points. First, most researchers have only focused on measuring the autogenous contraction strain of UHSC, but few studies have explored the relationship between the simultaneous measurement of internal relative humidity, internal temperature, and autogenous shrinkage. Second, most previous studies focused on autogenous shrinkage at a constant temperature, such as 20 \({}^{\circ}\)C. However, in real construction projects, due to the effect of binder hydration, the internal temperature of hardening concrete will initially increase and then decrease due to heat convection. The mechanism responsible for the interaction between hydration heat, temperature variance, and autogenous shrinkage needs more investigation. This study's primary purpose is to observe the effect of nS on the autogenous shrinkage of UHSCs. In addition, the internal relative humidity (IRH) and internal temperature (IT) are measured simultaneously by improving the experimental installation on ASTM C1698-09 [19]. The research methods implemented include the internal relative humidity, autogenous shrinkage, internal temperature, compressive strength, X-ray diffraction, and isothermal calorimetry tests. The innovative points of this study are summarized as follows: First, the new measuring instrument is modified to simultaneously determine the autogenous shrinkage, internal relative humidity, and internal temperature. Second, the trends in the autogenous shrinking of UHSC are divided into two stages, the variable temperature stage (Stage I) and the room temperature stage (Stage II). The mechanism of autogenous shrinkage in the two stages is explained. The deformation of autogenous shrinkage is closely related to the internal relative humidity in stage II and shows a good linear relationship. Finally, we find a very interesting phenomenon: in stage I (until 2 days of age), the internal relative humidity of UHSC undergoes a reduced-increased process as the binder hydration proceeds. In stage II, the internal relative humidity of UHSC continuously decreases with the progress of binder hydration. This phenomenon is different from the continuous decrease of the internal relative humidity of concrete from the final setting time reported in previous studies [20].
# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 4 Fractal Feature Analysis of Microseismic Waveform Signals
### Fractal Box Dimension Calculation Method
The sampling time interval \(\Delta t\) of the sensor for mine acquisition signal is 1/10,000 s. The rectangular box width \(\Delta w=k\delta_{1}\) needs to meet the following two conditions: the first condition is that it be not less than the time interval \(\Delta t\), and the second condition is that it be not more than half of the sampling time T/2. The height of the rectangular box \(\Delta t=k\delta_{2}\) also needs to meet two conditions: the first is that its value must be greater than 0, and the second is that its value cannot exceed the maximum or minimum amplitude value (A\({}_{max}\) or A\({}_{min}\)) in the microseismic signal waveform. The minimum value of \(k\) is 1, and the maximum value is \(k=lg_{2}(T/\Delta t)\). Therefore, the width \(\Delta w\) of the rectangular box and the height \(\Delta t\) of the rectangular box can indicate that the microseismic signal has a scale-free zone as long as the following two mathematical formulas are satisfied. \[\Delta t\leq\Delta w=2^{k-1}\Delta t<\frac{T}{2}(1\leq k\leq lg_{2}\bigg{(} \frac{T}{\Delta t}\bigg{)}) \tag{10}\]  
\[\Delta t=|\mathrm{A}_{max}-\mathrm{A}_{min}|\times\Delta w/t \tag{11}\]
# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 1 Introduction
### Task Complexity and Time Estimation  
One approach to defining task complexity is to consider the number of elements comprising a task [21; 22]. A high-complexity task includes many elements; a low-complexity task is comprised of few elements. As a task's complexity level increases, an individual needs to process more information, leading to a higher demand for attentional resources [22; 23; 24].  
Several studies have revealed that in non-VREs, individuals estimate time more inaccurately, as tasks become more complex. For example, [PERSON] and colleagues [25] designed verbal tasks with three levels of complexity--reading words (low complexity), naming objects (mid complexity), and giving synonyms for the words on the cards (high complexity). Of the three task conditions, the participants made the largest time estimation errors during the synonym-providing task, the highest task complexity. More recently, [PERSON] and [PERSON] [26] found that participants estimated the elapsed time to be longer than the actual time as the complexity of two tasks--a pin-to-hole assembly task and a task that required moving an object to a target area--increased. [PERSON] [27] used a figure-tracing task in which participants were instructed to trace a six-point star within a double boundary line. At the low complexity level, the participants performed the task with a pencil; at the high complexity level, the participants used a mirror-drawing apparatus. The study revealed that the subjects tended to make larger errors in both prospective and retrospective time estimations during high-complexity tasks compared to those of low complexity.  
Although many researchers have discussed the effect of task complexity on time estimation under non-VRE conditions, few studies have considered VREs. A recent study by [PERSON] et al. [28] used the VRE to investigate the impact of cognitive task types--verbal vs. spatial memory tasks--on time estimation. They observed that cognitive tasks significantly impacted participants' time estimation in the VRE. However, we noticed that the researchers did not consider the complexity of each task type. As the effect of task complexity on time estimation in the VRE remains unknown, our first research question (RQ) was as follows:  
**RQ1:**_Does task complexity influence time estimation in the VRE?_
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
###### Abstract  
Given the critical role of true triaxial strength assessment in underground rock and soil engineering design and construction, this study explores sandstone true triaxial strength using data-driven machine learning approaches. Fourteen distinct sandstone true triaxial test datasets were collected from the existing literature and randomly divided into training (70%) and testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive strength (UCS, \(\sigma_{c}\)), intermediate principal stress (\(\sigma_{2}\)), and minimum principal stress (\(\sigma_{3}\)) as inputs and maximum principal stress (\(\sigma_{1}\)) at failure as the output. The model was optimized using the Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting the model structure and activation function characteristics, the final model was made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models with different activation functions were trained and validated on the training set. Based on the comparison of prediction accuracy and meridian plane analysis, an HHO-MLP model with high predictive accuracy and meridional behavior consistent with theoretical trends was selected. Compared to five traditional strength criteria (Drucker-Prager, Hoek-Brown, Mogi-Coulomb, modified Lade, and modified Weibols-Cook), the optimized HHO-MLP model demonstrated superior predictive performance on both training and testing datasets. It successfully captured the complete strength variation in principal stress space, showing smooth and continuous failure envelopes on the meridian and deviatoric planes. These results underscore the model's ability to generalize across different stress conditions, highlighting its potential as a powerful tool for predicting the true triaxial strength of sandstone in geotechnical engineering applications.  
true triaxial strength; failure envelope; HHO; MLP; strength criterion; sandstone +
Footnote †: journal: Applied Sciences  
Article