esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Experimental and Numerical Evaluation of Natural Frequencies and Modes of Vibration of the Bridge  
The experimental and numerical evaluation of the dynamic characteristics of the bridge were presented in detail in the previous authors' work [51]. The locations of the measurement points on the half of the bridge are presented in Figure 7. Each point was equipped with three-axial piezoelectric accelerometers of a frequency range from 0.1 to 2000 Hz.  
The most important methods used in this work to solve and verify the eigenproblem are presented below.  
Firstly, the preliminary analysis of the FE model of the bridge was carried out, and the natural frequencies and modes of vibration were obtained numerically. The eigenvalues determination was limited to 15 Hz and covered four frequencies and modes of vibration since higher mode shapes were very complex, concurrently exhibiting both bending and torsional features. This fact resulted from the structural complexity of the bridge, with the longest span suspended by hangers to the arch (see Figure 2). Secondly, the experimental modal analysis of the bridge was conducted using the Operational Modal Analysis (OMA) techniques [52] and the Subspace Identification (SSI) algorithm [53; 54; 55] to estimate the natural frequencies of the bridge. The OMA and the SSI techniques served to assess the eigenfrequencies on the basis of the output data of the bridge's response to road traffic only [56]. Then, the Time Domain Decomposition (TDD) method [56; 57] was implemented to determine modes of vibration corresponding to the estimated frequencies. Finally, verification with the Modal Assurance Criterion (MAC) [58] proved the high degree of consistency between the modes obtained numerically and experimentally.  
The natural frequencies determined numerically and experimentally are compared in Table 2. The basic three modes of vibration are presented in Figure 8.  
For such a level of structural complexity, the similarity between numerical and experimental results may be considered reasonable since the differences are lower than 7% [59], with the exception of the second natural frequency accompanying the transverse eigenform. The reason for the discrepancy in this direction far above the limit lies in the FE model, with assumed frictionless sliding in bearings. Such a simplification is not appropriate since, due to the very low level of ambient vibration used with the OMA techniques, sliding in the pot bearings was prevented by frictional forces. However, as it was proved in the work [51], tuning the FE model is not required in case of excitation by a strong mining tremor, which is strong enough to defeat frictional forces in the bearings immediately; thus, the numerically obtained dynamic performance of the structure is not affected by the assumption of frictionless sliding at bearings.  
Figure 7: Layout of measurement points for evaluation of modal properties of the bridge.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Improved Pheromone Update Rule and Mathematical Analysis of Convergence
#### 3.4.5 Conclusions of Mathematical Analysis  
Through mathematical derivation, it can be demonstrated that the improved pheromone update rule provides significant convergence advantages over the original ant colony optimization algorithm:  
1. Increased pheromone concentration difference: this improvement enhances the directional guidance in path selection.  
2. Faster reduction in expected path length: the algorithm achieves the optimal solution with fewer iterations.  
The above derivation illustrates that the improved ant colony algorithm not only effectively avoids the trap of local optima but also significantly accelerates convergence. This is accomplished by considering the differences between the best and worst paths during the pheromone update process, which enhances the optimal path while suppressing the influence of suboptimal paths.
###### Abstract  
In this paper, the static and dynamic characteristics of the MXene-based IDE capacitive humidity sensor are investigated through theoretical modeling and simulation. It is found that the capacitance increases according to the thickness of the sensing film within a certain range while stopping increases along with the growth of the thickness when the thickness is over a threshold. When the thickness is at a tiny level, a larger thickness does not lead to a significant increase in the response time due to the diffusion mechanism of water molecules. When the thickness increases to certain extent, there is an evident relationship between the response time and the change of thickness. For the humidity-sensitive film, under the same relative humidity conditions, the capacitance has a positive correlation with temperature, and the response time shows the opposite trend. Subsequent studies on the sensitive mechanisms of MXene materials explain these phenomena and demonstrate the accuracy of the model. This provides a more accurate method for sensor design. The properties of the MXene capacitive humidity sensor can be optimized by changing its structure and adjusting material parameters.  
simulation; humidity sensor; MXene; static properties; dynamic properties; humidity-sensitive mechanism Article  
Theoretical and Simulation Analysis of Static and Dynamic Properties of MXene-Based Humidity Sensors.
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 2 Algorithm Theory and Method
### Modal Feature Extraction Based on Empirical Mode Decomposition (EEMD)  
Feature extraction is a key step in signal processing. Monitored signals mix multiple modes and contain a lot of invalid information. In order to better solve the mode mixing problem, EEMD is chosen to decompose the monitored signals, which utilizes the white noise could provide uniformly distributed scale in time-frequency space. It is widely used in all kinds of signal analysis fields. EEMD was presented to utilize the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose by WU [19]. EEMD can reduce the mode mixing to some extent depending on the parameters adopted in the EEMD algorithm [11]. In current studies on EEMD, the parameters are generally selected artificially and subjectively.  
The proposed ensemble empirical mode decomposition is developed as follows:  
Step 1: add a white noise series \(n_{m}(t)\) to the targeted data.  
\[x_{m}(t)=x(t)+n_{m}(t) \tag{7}\]  
Step 2: decompose the data with added white noise into intrinsic mode function (IMF).  
Step 3: repeat step 1 and step 2 again and again, but with different white noise series each time.  
Step 4: obtain the ensemble means of corresponding IMFs of the decompositions as the final result.  
\[IMF_{i}=\frac{1}{M}\sum_{m=1}^{M}a_{i,m}\ \ \ \ \ i=1,2,\cdots,I\ \ \ \ \ m=1,2, \cdots,M \tag{8}\]  
M is the number of computing average.  
Then \(x(t)\) can be obtained:  
\[x(t)=\sum_{n=1}^{N}y_{n}(t)+r_{n} \tag{9}\]  
The effects of the decomposition using the EEMD are that the added white noise series cancel each other, and the mean IMFs stay within the natural dyadic filter windows, significantly reducing the chance of mode mixing and preserving the dyadic property.  
Figure 2 shows a time-dependent trend chart and frequency spectrum for raw signal \(y\), and time-dependent chart and frequency spectrums IMFs of signal \(y\) decomposed by EEMD at some points are also presented in Figure 2. Signal \(y\) can be obtained by formula below.  
\[y=2\times\sin(20\times t)+4\times\sin(80\times t)-\sin(10\times t) \tag{10}\]  
As shown in Figure 2, the signal is decomposed into IMFs by analyzing modes, and the frequency spectrums present the amplitude changes of different modes. Different frequency components are separated, so smaller frequency peaks in the raw signal are enlarged in figure above by decomposing. By decomposing the monitored signals, the mode information is more plentiful and modal features are more distinct. Feature extraction can use statistical methods to reduce dimension.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Combining Prediction Intervals  
Combining point forecasts has the tendency to improve accuracy, as shown in [19; 43]. The combined point forecasts can also be improved in terms of their prediction intervals by combining their prediction limits. A comparison of the combined PI and the PIs from constituent models is presented in this section. Lower and upper prediction limits from \(K\) number of forecasting models can be represented as \([L_{t},U_{t}]t=1,\cdots,K\) to denote the resulting \(100(1-\alpha)\%\) PI. The researchers used two prediction interval combination methods, which are the simple averaging and the median method combining PIs, as applied in [31].
## 2 Multi-Chip Interconnection Topology Design
### The Inter-Chip Wireless Communication Topology
From Figure 3 it is observed that the multi-chip system has higher peak achievable bandwidth and low energy for a WI deployment density of 1/16 WIs per core. As each chip has 16 cores, this is the minimum density possible while still ensuring all chips are interconnecting using the wireless links. For higher WI density the system performance degrades due to larger starvation time. While Figure 3 gives an example of the optimization, the optimum WI density in each chip depends on the number of cores in each chip as well as the intra-chip topology adopted for each chip. Optimization through system-level simulation as discussed above requires a priori knowledge of the expected traffic patterns of the multi-chip system. However, these systems are designed for embedded platforms or blade servers and are, therefore expected to cater to a wide variety of applications ranging from multimedia to parallel tasks characterized by benchmark suites like Parsec [43] or SPLASH-II [44]. Therefore, while a priori knowledge of precise traffic interactions between cores in multiple chips can result in better optimization, such knowledge may not always be available at design time, especially for general purpose platforms. On-chip traffic modeling has been done in [45; 46]. However, in the absence of such knowledge about traffic in a multi-chip system, a uniform random traffic pattern can be assumed at design time, which is generic and captures the characteristics of both local as well long-distance traffic. This approach has been adopted in the design of intra-chip WiNoCs [31]. Alternatively, traffic-independent metrics such as average hop-count or zero-load latency, which is the average latency of a packet in the absence of any other packet in the interconnection network, can be adopted. Average zero-load latency is a more precise metric compared to average hop-count, but it does not capture delay due to traffic congestion in absence of traffic in the network. Therefore, the optimization method for the wireless interconnection fabric needs to be adopted depending on inter-dependent factors such as whether knowledge of traffic pattern is available at design-time, the adopted wireless MAC protocol, and the chosen optimization metric. While the above method is suitable for a shared medium wireless interconnect topology, directional antennas with specific radiation patterns might govern the exact location of transmitting and receiving antennas to maximizing antenna transmission gains [47]. The challenging aspect of  
Figure 3: Multichip system performance vs. WI deployment density. that design is a rigorous analysis of wireless transmission and channel characteristics at relevant frequencies to determine optimal locations.
## 3 Results and Discussion
### Mechanical Properties  
The variation in the tensile properties, mean hardness values, and 28] transition temperatures (T28)) estimated from the impact tests are given in Table 2 and Figure 13. In addition, the through-thickness hardness profiles and impact transition curves are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Steel Code** & **Y5 1,** & **UTS 1,** & **El 1, \%** & **Hardness 2,** & **T28] \(C\) & **USE** & **Y5/UTS** & **UTS*El** \\  & **MPa** & **MPa** & **MPa** & **HV** & **T28] \(C\) & **J/cm2** & **Y5/UTS** & **(MPa  
Both Steels II (2.5 Cr) and III (4 Cr) were able to provide the targeted 700 MPa level of yield strength, which is a widely used strength class among high-strength structural steels [20]. Steel I (1 Cr) provided a lower yield strength of 560 MPa with a high elongation to fracture as a result of the formation of PF. The higher impact toughness of Steel I (1 Cr), in respect to higher upper shelf energy and lower T28] temperature compared to Steels II (2.5 Cr) and III (4 Cr) resulted from the presence of PF and GB, which contains martensite-austenite (M/A) islands (see Table 2 and Figure 14). It is well known that the presence of PF enhances the toughness properties [4]. On the other hand, the presence of M/A constituents in the lath-like granular bainite is detrimental to impact toughness [21]. Figure 15 shows that despite a refinement of the effective mean and 90 th percentile grain size, increasing the Cr content was associated with poorer impact toughness, i.e., higher T28]. Presumably this is due to the combined detrimental effects caused by an increase in the fraction of BF and higher yield and tensile strength.  
The adverse effect of the high fraction of PF (see Figure 11a) on the hardness in Steel I can be seen clearly in Figure 14a. As mentioned above, increasing the Cr content enhanced the hardenability, promoted the formation of the low-temperature transformation product BF, and hindered the formation of the high-temperature transformation products PF and GB, thereby enhancing the hardness in the case of Steels II and III (see Table 2) [22]. Moreover, due to the higher hardenability of the high-Cr steels, the hardness was fairly uniform throughout the plate thickness as shown in Figure 14a.  
As can be seen in Figure 16, there is a correlation between the yield strength and T28]. It can be seen that the targeted yield strength could still be achieved with good impact toughness in the  
Figure 14: Through-thickness profile (**a**) and Charpy V impact transition curves (**b**).  
Figure 15: Relationship between effective high-angle grain sizes at the 90 th percentile in the cumulative size distribution (D\({}_{90\%}\)) and the 28-joule transition temperature (T28).  
hot-rolled and direct-quenched plates when the Cr content was in the range of 2.5 wt.%-4 wt.%, but not in the case of the lowest Cr content (1 wt.%) due to the formation of a significant fraction of PF which improved the impact toughness and tensile elongation to fracture, but at the expense of the yield strength.
## 2 Materials and Methods
### Wobble Board Test  
Computerized Balance Board WSP (Well Sport Project, G.S.J. Services S.r.l., Rome, Italy) is a proprioceptive platform; we used the WB model, incorporating a triaxial accelerometer (Phidget Spatial 0/0/3 Basic 1041, Phidgets Inc. 2016, Calgary, AB, Canada). The platform is composed by a circular surface (diameter 40 cm, height 2 cm) and placed on a plastic material semispherical support (diameter 12 cm, height 6 cm). The WB model is connected via USB cable, with a sampling frequency of 200 Hz. The WB tilt (maximal tilt angle = 20\({}^{\circ}\)) angle data is then transmitted to a customized software displaying real time balance performance on a monitor (resolution = 1920 \(\times\) 1080) through a motion marker (MM, diameter = 6 mm). The software user interface showed the MM, represented by a yellow circle, a Target Zone (TZ, diameter = 6.5 cm) displayed by a red circle which represented the stability area (0\({}^{\circ}\) tilt angle), and a countdown of the trial. The boundaries of the TZ and the MM were the same for all subjects during experimental sessions.  
After a detailed explanation of the testing procedures, with a short demonstration and verbal support, subjects were asked to stand barefoot on the WB, which was placed directly on the floor, with a comfortable double leg stance, keeping their hands on their hips and instructed to keep the board flat (0\({}^{\circ}\) tilt) and as still as possible for as long as possible within a recording period of 30 s.  
The test session consisted of a 3-min free practice on the WB followed by three 30-s double leg stance trials and 1-min sitting recovery in between. Subjects were asked to perform two conditions: (1) with VBF, where subjects were asked to keep visual focus on the MM showed on the display and try to keep it inside the TZ as long as they could within 30 s; and (2) without Visual Biofeedback (NVBF), looking at a fixed point on a black board, where subjects were asked to keep visual focus on a marking in front of them and instructed to maintain the WB as flat and still as possible for as long as possible within the recording period of 30 s. During the VBF and NVBF condition, the screen or the black board were positioned 2 m far in front of the WB while standing on it. The order of conditions was randomly assigned.
## 3 Methodology
### Explainability Integration  
XAI refers to a set of techniques designed to make the decisions of ML/DL models transparent, interpretable, and understandable to human users [31; 32; 33]. As ML models, especially deep learning architectures, become increasingly complex, the need for interpretability grows in critical domains like cybersecurity, healthcare, and finance. Overall, XAI ensures that end-users and analysts can trust, validate, and act upon AI-generated decisions [34; 35; 36; 33].  
In this study, SHAP, a leading XAI technique grounded in cooperative game theory, was integrated to enhance the interpretability of the proposed ensemble intrusion detection system. SHAP values were computed for both global and local interpretability. The global SHAP summary plot revealed that the model relied slightly more on the ANN outputs than on SVM predictions when forming its final decisions via the Random Forest meta-classifier. For local explanations, SHAP decision plots and waterfall charts were used to visualize the contribution of each feature to individual predictions, offering transparent reasoning behind classifications. These explainability components not only demystify the internal workings of the hybrid ANN-SVM-RF architecture but also foster greater user trust, accountability, and usability in real-time security-critical environments.
## 5 Data Products, Processing and Distribution
### Data Processing System
#### 5.2.1 GPS and Inertial Data  
GPS-INS data is stored on internal memory in a raw, unprocessed format. Lever arm and other offset coefficients are applied to the L0 data with Oxford's RT Post Process software, which converts the proprietary binary data to an ASCII format. During this step, the inertial data is processed forwards and backwards in time to minimize the effects of GPS data drift. During data acquisition, OmniStar HP differential correction service provides a real-time positioning error of \(<\)15 cm in extended periods of open sky. Increased precision can be achieved where base station GPS data is available.
_Appl. Sci._**2013**, \(3\), 469-489; doi:10.3390/app3020469  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Effect of the Presence of Nonionic Surfactant Brij35 on the Mobility of Metribuzin in Soil**  
**[PERSON] * and [PERSON]**  
Department of Bioresource Engineering, McGill University, Ste. Anne de Bellevue, Quebec H9X 3V9, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-514-463-7799.  
_Received: 31 December 2012; in revised form: 22 March 2013 / Accepted: 25 March 2013 / Published: 24 April 2013_  
**Abstract:** Given the water scarcity becoming endemic to a large portion of the globe, arid region irrigation has resorted to the use of treated, partially treated, or even untreated wastewaters. Such waters contain a number of pollutants, including surfactants. Applied to agricultural lands, these surfactants could affect the fate and transport of other chemicals in the soil, particularly pesticides. A field lysimeter study was undertaken to investigate the effect of nonionic surfactant, Brij35, on the in-soil fate and transport of a commonly used herbicide, metribuzin [4-amino-6-_tert_-butyl-3-(methylthio)-1,2,4-triazin-5(4\(H\))-one]. Nine PVC lysimeters, 1.0 m long \(\times\) 0.45 m diameter, were packed with a sandy soil to a bulk density of 1.35 mg m\({}^{-3}\). Antibiotic-free cattle manure was applied (10 mg ha\({}^{-1}\)) at the surface of the lysimeters. Metribuzin was then applied to the soil surface of all lysimeters at a rate of 1.00 kg a.i. ha\({}^{-1}\). Each of three aqueous Brij35 solutions, 0, 0.5 and 5 mg L\({}^{-1}\) (_i.e._, \"good\", \"poor\" and \"very poor\" quality irrigation water) were each applied to the lysimeters in triplicate. Analysis for metribuzin residues in samples of both soil and leachate, collected over a 90-day period, showed the surfactant Brij35 to have increased the mobility of metribuzin in soil, indicating that continued use of poor quality water could influence pesticide transport in agricultural soils, and increase the risk of groundwater contamination.  
**Keywords:** nonionic surfactant; Brij35; herbicide; mobility; wastewater; sandy soil
## 5 The Application of ETRM for Damage Imaging of Composite Laminates
the damage location can be accurately located under different _weight_ values. These results demonstrate the effectiveness of the ETRM for damage detection in composite laminates. For comparison, Figure 14 a, b and c give the imaging results of the traditional TRM as _weight1_ takes 0.1, 0.2, and 0.3, respectively. It can be seen that the grid with the highest probability value for the presence of damage sits at the sensor sites, rather than the actual damage site. Besides, there are a number of fake peaks at the intersections of different sensing paths, which contaminate the imaging results. It may be possible to obtain accurate results by continuously adjusting the excitation parameters, the _weight1_ value and _weight2_ value. But that requires a large number of experiments, i.e., \(2\times n\times m\times p\times k\) experiments, where \(n\) indicates the number of propagation paths in the sensor array, \(m\), \(p\), and \(k\) represent the number of changes of excitation parameters, _weight1_ and _weight2_, respectively. For example, we have conducted \(2\times 16\times 1\times 3\times 1\) = 96 experiments to obtain these results (Figure 14), which is very time consuming.