esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Training Methods and Prediction Results of Fully Connected Neural Networks
### Prediction of the Physical Properties of Soils  
Soil physical properties can be divided into two categories. One type involves the inherent properties directly determined by the components of the soil, such as the liquid limit, density, and specific gravity. The other type involves the properties that change with environmental conditions, such as water content and compaction degree, which cannot be predicted based on the properties of the soil. In this study, the mineral composition of the soil was classified and sorted as an input condition for predicting the liquid limit of the soil using an artificial neural network model. Mean square error was used as the optimization objective of the model, but it is not suitable as the first evaluation index for the model's performance. The determination coefficients of the prediction results of the three algorithms are shown in Figure 6. The LM algorithm and BR algorithm have their respective strengths and weaknesses in predicting the liquid limit, but the accuracy difference is not significant, while the GD algorithm has lower accuracy. The increase in the number of hidden layers slightly improved the accuracy of the neural network but not significantly. The best model for predicting the plastic limit is the BR algorithm, and the best model for predicting the liquid limit is the LM algorithm, with a hidden layer structure of (5,5) for both. The prediction results of the model are shown in Figure 7. The determination coefficient values of the best models for plastic limit and liquid limit prediction are R\({}^{2}\) = 0.557 and R\({}^{2}\) = 0.825, respectively, and there is little difference between the determination coefficient values of the training set, test set, and validation set. The training process of the two models is shown in Figure 8. With the increase of the number of epochs, the predicted value gradually converges to the actual value. In addition, the convergence speed of the LM algorithm is much faster than that of the BR algorithm.
## 3 Experiments
### Dataset Preprocess  
First, we used the Intel Berkeley Research Laboratory (IBRL) [34] as the experimental dataset. The IBRL dataset was collected from 54 sensor nodes at the Intel Berkeley Research Lab from 28 February, 2004 to 6 April, with a sampling interval of 31 s. The sensing data in IBRL dataset contains four categories: temperature, humidity, light and voltage. The amount of temperature data is large and the temperature change trend is intuitive, so we select the temperature data as our experimental data. Due to node failure, there were several temperature values above 100 \({}^{\circ}\)C and below \(-30\)\({}^{\circ}\)C. Because the sensor nodes were located indoors, we first removed the apparent anomaly data in the temperature data by taking the threshold of 45 \({}^{\circ}\)C and \(-5\)\({}^{\circ}\)C by a priori knowledge. Then we removed most of the noise in the data by retaining those data the values of which were in \([x_{mean}-3*x_{std},\ x_{mean}-3*x_{std}]\). \(x_{mean}\) denotes the mean value of the sample data, \(x_{std}\) denotes the standard deviation of the sample data.  
In order to reduce the difference between the input sensing data and converge the algorithm more quickly, we mapped the original sensing data to range [0, 1] by using max-min normalization. For this processed sensing data, we divided each node data into two parts: training set and test set, and the split ratio was 8 to 2.  
Then, we tested the performance of the proposed model on different real-world WSN datasets (Argo [35], ZebraNet [36] and CRAWDAD [36]) to evaluate the generalization and robustness of the proposed model.
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Performance Comparison
#### 4.2.2 Qualitative Evaluation
However, its fixed-scenario design incurs redundant computations within predefined  
Figure 6: The results of MTGNet and nine advanced models on the Argoverse 2.0 validation dataset using three widely used evaluation metrics (MR (k = 6) \(\downarrow\), MR (k = 1) \(\downarrow\), Brier-minFDE (k = 6) \(\downarrow\)). “\(\downarrow\)” mean that the larger the better, and the smaller the better [2, 46, 47, 48]. boundaries. By contrast, MTGNet adopts a dynamic strategy: first, a Transformer encoder denoises input data to filter out irrelevant driving behavior noise, ensuring subsequent interaction modeling focuses on critical dynamic features; it then narrows down to subsets of actually interacting agents (number n, n \(<\) N) and subgraph edges (e) achieving a complexity of O(nD + eD) (d is the feature dimension) to avoid non-discriminatory global calculations. Trained on a single NVIDIA RTX 3080 GPU for 128 epochs, MTGNet requires 18.4 min per epoch, significantly faster than HiVT's 23.1 min per epoch--an efficiency gain attributed to its dynamic interaction modeling that minimizes redundant computations. Despite the faster training speed, MTGNet maintains superior prediction accuracy on the Argoverse 2.0 validation set, showcasing a balance of efficiency and performance through its denoising preprocessing and adaptive complexity design.
# Evaluation of PID-Based Algorithms for UGVs
###### Abstract
Thus, in this article, a set of PID control algorithms, referred to as follows, is evaluated and studied:  
* ON-OFF controller;
* PID Heading controller (PID-H);
* PID Cross Track Error controller (PID-CTE);
* PID Cross Track Error + Heading controller (PID-CTE + H);
* PID Vector Field (PID-VF). Thus, this article aims to demonstrate which of the implemented algorithms achieves the most efficient behavior for UGV navigation in unstructured environments (in this case, the unstructured environment under study consists of a forest environment). This paper is organized as follows: Section 1 introduces the motivation behind this work. Section 2 presents topics related to the state of the art of PID controllers. Section 3 details the methodology, including a description of the robotic platform used and its integrated sensors, the PID control algorithms with a detailed explanation of each one employed, and, finally, the performance evaluation metrics. Section 4 shows the results obtained through real-time experiments and the evaluation of the proposed PID control algorithms. Section 5 concludes with an overall analysis of the work carried out.
## 3 Image Processing
### Conversion into the Gray-Scale Image  
As a next step, the conversion of the true color image (RGB) into a gray-level image by removing hue and saturation information while maintaining luminance is done. In this process, the amount of image data decreases. Specifically, the RGB color of 24-bit (\((2^{8})^{3}\) = 16,777,216 color) is converted into 8-bit gray-level (\(2^{8}\) = 256 level).
## 5 Results and Discussion
### Experiment 2: Variation Across \(L\) (Constant \(K=K_{\text{max}}\))  
We ran each algorithm varying \(L\) from \(3T_{0}f_{s}\) to \(8T_{0}f_{s}\) with a constant number of partials \(K_{\text{max}}\) and measured the resulting local and global SRER. In the literature [46], \(L=3T_{0}f_{s}\) is considered a reasonable value for speech and audio signals when using the SM. We are unaware of a systematic investigation of how \(L\) affects modeling accuracy for EDS. Figure 6 shows the local and global SRER (averaged across sounds) as a function of \(L\) expressed as \(q\) times \(T_{0}f_{s}\), so sounds with different \(f_{0}\) values have different frame size \(L\) in samples.  
Figure 6 shows that the SRER decreases with \(L\) for all algorithms. The SM seldom outperforms EDS or eaQHM, but it is more robust against variations of \(L\). For the SM, \(L\) affects both spectral estimation and temporal representation. In the FFT, \(L\) determines the trade-off between temporal and spectral resolution, which affects the performance of the peak picking algorithm for parameter estimation. The temporal representation is affected because the parameters are an average across \(L\) referenced to the center of the frame. In turn, ESPRIT estimates EDS with constant frequency inside the frames referenced to the beginning of the frame, thus \(L\) affects the temporal modeling accuracy more than the  
Figure 5: Comparison between local and global SRER as a function of the number of partials for the three models (the standard sinusoidal model (SM), exponentially damped sinusoids (EDS), and eaQHM). The bars around the mean are the standard deviation across different sounds from the family indicated. The distributions are not symmetrical as suggested by the bars.  
spectral estimation. However, the addition of sinusoids might compensate for the stationary frequency of EDS inside the frame. Finally, the SRER for eaQHM decreases considerably when \(L\) increases because \(L\) adversely affects the frequency correction and interpolation mechanisms. Frequency correction is applied at the center of the analysis frame and eaQHM uses spline interpolation to capture frequency modulations across frames. Thus, adaptation improves the fit more slowly for longer \(L\), generally reaching a lower absolute SRER value.
# Mechanism of Rejuvenation in Aged SBS-Modified Asphalt by Density Functional Theory
## 4 Results and Discussion
### Analysis of Quantum Mechanical Simulations
Similarly, from Figure 9a, it can be found that the more severe the asphalt aging level, the greater the \(E_{\text{binding}}\) between saturate components with asphaltenes. Compared to original asphalt, \(E_{\text{binding}}\) between saturate components with asphaltenes increased 0.0135 Ha in short-term aged asphalt and increased 0.0269 Ha in long-term aged asphalt. The changes in long-term aged asphalt were almost double those in short-term aged asphalt. To this end, it could be said that \(E_{\text{binding}}\) between saturate components in rejuvenator with asphaltenes in aged asphalt could be a qualitative evaluation indicator for the rejuvenation of aged asphalt. Figure 10: Elastic transformation rate of SBS-modified asphalt in different aging levels. Based on the proportions of molar mass from Table 14, \(Q_{\text{transfer}}\) of each molecule was recalculated. And the weighting calculation is presented in Figure 11a,b, which shows \(Q_{\text{transfer}}\) between the light components in rejuvenator and heavy components in SBS-modified asphalt. From Figure 11a,b, \(Q_{\text{transfer}}\) between heavy components molecules in aged SBS-modified asphalt with saturate components molecules in rejuvenator is more than those with aromatic components molecules. This could be related to molecular structure, which means that long-chain molecules were likely to entangle with each other, making the interactions closer. However, it is worth noting that \(Q_{\text{transfer}}\) between aromatic molecules in rejuvenator with resins molecules in short-term aged asphalt was higher than saturate molecules. This was because in the process of the short-term aging of asphalt, aromatic components turned to resins first, then turned to asphaltenes. The interactions between aromatic components and resins were close, making aromatic components be consumed significantly. When the long-term aging process is coming, the interactions between aromatic components with resins were thin. Since saturate components were not affected by asphalt aging, the interactions between saturate components with asphaltenes and resins could be closer due to the decrease of aromatic components. It could be reflected in \(Q_{\text{transfer}}\) that in long-term aged asphalt, \(Q_{\text{transfer}}\) between aromatic molecules in rejuvenator with asphaltenes and resins molecules in aged asphalt were lower, at 0.0297 e and 0.0168 e, respectively. Meanwhile, \(Q_{\text{transfer}}\) between saturate molecules with asphaltenes and resins molecules were higher, at 0.0713 e and 0.0461 e, respectively.
## 4 Discussion
### Potential Application  
Construction projects are one of the main sources of environmental pollution in urban areas due to their pollution, such as harmful gases, noise, dust, solid and liquid wastes during construction [32; 33]. Some construction companies engage in illegal behaviors, such as building without approval and disturbing beyond surveillance. It is necessary to use remote sensing technologies to detect the illegal situations by supervising the full coverage in a region or even a city. The application of this method can accurately and effectively identify construction projects under construction and determine whether the projects comply with regulations by checking with the soil and water conservation plans and authorized scope that have been registered in the National Soil and Water Conservation Supervision and Management System.  
Multi-level image scene identification and understanding is a hot issue in current high-resolution earth observation technology [34]. In addition to identifying construction projects, this study can further investigate the contents of the automatic extraction of disturbance ranges, predicting soil loss from construction projects, and evaluating the distribution of regional soil and water loss. In addition, this method can also be used in other fields such as building detection [35] and illegal mining identification [36].
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Classification of MFCs
#### 3.2.1 Laboratory-Scale MFCs  
Laboratory-scale or in-lab MFCs are smaller and are usually in the order of milliliters in volume [99; 100; 101]. These lab-scale MFCs are the most investigated ones and have been extensively utilized to identify fundamental conditions like anode-materials [102; 103] and configurations [104; 105], improved catalyst/cathode configurations [106; 107] as well as for the study of microbial communities [101].  
For scaling up operations, it is also conventional to implement a lab-scale MFC and achieve an optimal output, mimicking the desired outdoor conditions [108; 109; 110] and then transfer it to the external environment for pilot studies [99; 111]. However, this methodology poses huge challenges since complex configurations for a large scale are difficult to implement [100] and increase expense. Bio-fouling and clogging become more severe in the case of in situ long-term MFC operations [99]. Thus, in multiple cases it has been found that volumetric power densities reduce with increasing MFC reactor size [112; 113].
# Noise Source Identification in Training Facilities and Gyms
## 3 Identification of the Main Noise-Generating Exercises  
Most of the noise and vibration problems in fitness centers and CrossFit boxes have an impact type nature. Heavy weights, such as loaded barbells, impact the flooring as a natural result of the exercise, i.e., Olympic lifts are often dropped from the racked or locked position directly to the ground. Another common cause of heavy floor impact is the dropping of external weight in the form of dumbbells, kettlebells, or barbells due to an improperly executed movement pattern [27] or sloppiness of the trainee. The latter case is quite common during group workouts, where the number of repetitions adds a competitive factor, shifting the focus to the repetitions rather than to proper movement patterns. Combining the above with the presence of multiple club members attending the workout simultaneously, it is common to observe high noise levels, especially during group workouts.  
Barbell pulls and lifts as well as plyo box exercises were mentioned by several gym owners and multiple trainees as especially loud exercises. In general terms, the mass and velocity will define the amount of energy that needs to be dissipated during impact, whether it is the external load or the weight of the trainee. A good representation of noise generated during common barbell exercises is the drop of the loaded barbell from the overhead locked position onto the ground. The second investigated case is the drop of the loaded barbell from the overhead locked position onto the plywood jerk blocks presented in Figure 5. Another investigated case is the common drop of a loaded barbell from the locked position of a dead lift onto the floor.  
Weights applied to the barbell during measurements were chosen as a representation of the commonly used average weight used during group workouts: 90 kg for deadlifts and 60 kg in the case of other Olympic lifts. Most fitness centers equipped with Olympic barbells use bumper plates similar to the ones presented in Figure 5. Bumper plates have a steel core disc vulcanized in rubber or polyurethane, which increases the durability as well as the impact resistance and presumably reduces the force during impact. The second set of measurements was conducted during box jumps done on a plywood pyjometric box, which were identified by users as a noisy exercise.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surface Name** & **Material Type** & **Surface NRC Values** \\ \hline Floor & 0.02 m thick hard rubber panels & 0.03 \\ \hline Ceiling & Plywood, 5 mm 50 mm airspace filled with & \\  & glass wool & 0.15 \\ \hline Walls & Gypsum panels on studs & 0.08 \\ \hline Doors & Wood hollow core door & 0.15 \\ \hline Windows & Large panes, plate glass & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material type noise reduction coefficients (NRCs) defined in the calculations.
## 4 Experimental Results
### Simulation Results
#### 4.1.2 Multiple Vehicles  
In the third experiment, we illustrate the solve performance of the designed sensing waveform with different signal processing methods. And we consider that there are two vehicles in one range cell with similar velocities of 31 km/h and 32 km/h. Figure 13 is the range-velocity map with the DFT-based method, wherein it can be seen from the velocity slice that neither sensing waveform can solve the two vehicles. The reason for this is that the velocity resolution \(\Delta v\) of the DFT-based method is 2.08 km/h. Figure 14 shows the range-velocity map using the IAA and OMP methods, and it can been seen that the IAA method can solve the two vehicles; otherwise, the OMP method cannot do it. In short, the IAA method has the best solve performance compared with the DFT-based method and the OMP method; otherwise, it has the highest computing complexity. Fortunately, in real common traffic scenarios, the case of two vehicles with similar range and velocity rarely occurs, and even if it occurs, it will not last for a long time. Hence, the designed waveform with the DFT-based method can satisfy the common traffic scenarios, and the experiments of real traffic scenarios in the next section will verify it.
## 3 Rheological Models  
For the purpose of understanding and describing of the material viscoelastic behavior, different mathematical models have been developed and are available in the literature. Since the experimental characterization of the viscoelasticity of the compounds is particularly important to properly feed the procedures of so many different application areas, the question arises about which could be the analytical model that is able to reproduce the viscoelastic behavior in an optimal and robust way employing the minimum number of parameters. In other words, the aim is to investigate the simplest constitutive model able to reproduce the experimental viscoelastic behavior in the widest range of operating frequencies.  
The simple Maxwell or Kelvin models fail in representing the actual response of viscoelastic materials at low and high frequencies, respectively, while the generalized models are able to provide more reliable results. As this paper is focused on viscoelastic solids, the GM model and FDGM model are considered and refer to a spring in parallel with (respectively) Maxwell cells and fractional Maxwell cells as defined by [PERSON] (2016). Maxwell cells are composed of a spring and a dashpot arranged in series, while the fractional Maxwell element is obtained by replacing the dashpot elements with the so-called spring-pot elements. Figure 2 depicts the rheological model considered.  
In the following chapters, the GM and FDGM models are mathematically described. Starting from the models' definition in the time domain, the frequency domain expressions are derived by means of the Fourier transform to obtain the pole-zero formulation of both the models under analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**POLYMER** & \(\mathbf{T_{g}}\) (C) & \(\mathbf{E_{\infty}}\)**(MPa)** & \(\mathbf{E_{0}}\)**(MPa)** \\ \hline A & \(-27\) & \(1588\) & \(7.9\) \\ B & \(-16\) & \(4057\) & \(22.3\) \\ C & \(-10\) & \(3031\) & \(12.03\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Principal characteristics of the three studied polymers  
Figure 1: Storage modulus (a) and loss tangent (b) of rubber compounds A, B and C at a reference temperature \(T_{\textit{ref}}\)=\(120^{\circ}\).
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 4 Numerical Results
### Experiments with Known Sparsity Level  
In all the experiments reported in this section, ITC-ADL is in possession of the true value of \(s\), which takes even values from 4 to 12. In the first round of experiments, the dictionary \(\mathbf{D}_{\text{true}}\) has \(n_{\text{true}}=128\) atoms. The size \(n_{\text{init}}\) of the initial dictionary takes random values between 80 and 180, in order to test the robustness of the algorithm to initialization. Table 1 reports the average, minimum and maximum size \(n\) computed by ITC-ADL over the 50 runs. Our algorithm is able to find very good estimates of the size, for all considered noise and sparsity level values. An important conclusion is that the size of the initial dictionary has no impact on the results. We evaluate the performance of the dictionaries given by ITC-ADL on 1000 test signals generated similar to the training ones. For comparison, we compute the RMSE for two dictionaries: (i) \(\mathbf{D}_{\text{true}}\), for which the representations are computed with OMP; and (ii) the dictionary computed by AK-SVD with the true size \(n=n_{\text{true}}\) and the same number of iterations (\(K=200\), in this case). We denote RMSE and RMSEn the RMSE obtained with these dictionaries. Both approaches have an advantage over ours: the first has the true dictionary, so it is in fact an oracle; the second uses the same DL algorithm that we use, but knows the size. Table 2 shows the average value of the ratios RMSE/RMSEt and RMSE/RMSEn. Values below 1 mean that our algorithm is better. Our algorithm always gives worse results than the oracle, which is expected, but the difference is often very small. Compared with AK-SVD, our algorithm is superior in all cases, the advantage growing with the SNR. Another conclusion that could be drawn is that the problem becomes harder as \(s\) and the SNR grow. The first part is natural; for the second part, an explanation can be that, as the SNR grows, there are fewer local minima with values close to the global one; our algorithm appears to be able to find them, while AK-SVD may be trapped in poor local minima; running it with several initializations can improve the results. The results for \(n_{\text{true}}=192\) are only slightly worse, thus we jump to those for \(n_{\text{true}}=256\), shown in Table 3. The size \(n_{\text{init}}\) takes random values between 160 and 360. Now, some of the harder problems with large sparsity level (\(s\geq 10\)) are no longer well solved: some size estimations are wrong. However, the good behavior of the RMSE persists: most results are near-oracle and clearly better than AK-SVD. We can see now some differences between the two ITC: ERNML\({}_{1}\) tends to overestimate \(n\), but hardly ever underestimates it, while EBIC is more prone to underestimation. However, in most setups, both ITC give sizes that are near from the true one. Regarding the RMSE (see Table 4), the situation is somewhat reversed: ERNML\({}_{1}\) is slightly better than EBIC.