esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results  
In this section, we present a comparative analysis for the performance of the SVM algorithm in crop type mapping, using diverse experimental setups. Specifically, in Section 4.1 we have used the actual Sentinel-1 and Sentinel-2 data as derived from the corresponding satellites and we compare the performance of the SVM model using different features as input. Table 3 presents the overall comparison of the different experiments for multiple metrics. The main result of this section is illustrated in the last row of the table and corresponds to the model that utilizes multiple GA runs (more details in Section 4.1.4). Analytic metrics for all crop types of this model can be overviewed in Table 4. Following, in Section 4.2 we have applied artificial cloud masks in the Sentinel-2 time-series to simulate a clouded scenario. Table 5 illustrates the performance of the SVM models trained in the different clouded input feature spaces. In this case, the same model as before (i.e., the GA with multiple runs) yielded the best performance. However, here we observe a much more significant improvement in both individual and overall metrics compared to the Sentinel-2 model.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 5 Bridge Health Monitoring
### Moving Reference Influence Line (MR-IL) Calculation  
Moving reference deflections are the deflections under a moving instrumented axle. They are affected by both bridge and vehicle properties and, as such, are different for every vehicle passage. To provide information about bridge condition, this data needs to be transformed into a form that is independent of the vehicle. The influence line (IL) of a bridge is the response to a unit moving load and is a useful descriptor of structural behaviour, independent of any vehicle property.  
[PERSON] et al. [43] describe a 'Matrix Method' to determine the IL from a measured multi-wheel response. This method is very effective at finding the IL directly from strain measurements. In this section, a method is proposed to find the moving reference influence line (MR-IL) for deflection under the leading axle of the railway carriage. In conventional structural analysis, an influence line describes the response at a fixed point on a bridge. By its nature, MR-IL is more complex as the point of reference is no longer fixed and the load and response may be at different relative locations. The 'general' MR-IL function, \(J^{1}(x,y)\), is defined here as the response at a moving point, \(x\), to a unit load at any point, \(y\)--see Figure 5. A special case is when the load and the measurement are at the same point (\(x=y\)). This response at a moving point, \(x\), to a unit load at that point, is referred to here as the 'fundamental' MR-IL function, \(J^{0}(x)\). It can be seen that the fundamental function is a special case of the general function: \(J^{0}(x)=J^{1}(x,x)\). This section describes a methodology which extends the Matrix Method of [PERSON] et al. [43] to find the fundamental MR-IL directly from moving reference deflection data.
## 2 Materials and Methods
### Weibull Analysis  
To ascertain the reliability of the samples, a Weibull analysis was undertaken. The Weibull distribution is a type of statistical continuous probability distribution; a flexible model that is frequently used to approximate the normal, Rayleigh, or exponential distribution. It can be used to model data exhibiting right-skewed, left-skewed, or symmetric properties.  
This versatility renders it a suitable model for assessing reliability. The Weibull distribution has been demonstrated to be an effective extreme value distribution for the purpose of predicting the occurrence of extreme phenomena. Therefore, it is widely used to describe the probabilistic failure behavior of materials. In general, the Weibull distribution is defined either by its probability density function or cumulative distribution function. The original Weibull model is three-parametric with shape indicating whether the failure rate is increasing, decreasing, or constant over time, scale that stretches or compresses the distribution, and threshold that shifts the distribution along the \(X\)-axis. The latter is optional and neglected in the 2D model. The parameters of the Weibull distribution can be determined analytically, employing either least squares (rank linear or non-linear regression) or maximum likelihood estimation (MLE). In this study, the former approach was used.  
Thanks to linearization procedure, the Weibull method can theoretically work with extremely small samples, even with two or three failures. In order to achieve statistical relevance, it is necessary to increase the size of the sample. Usually, Weibull analysis requires at least 30 samples (the central limit theorem requirement). For a distribution that differs significantly from normal, it may be necessary to use as many as about 30 samples. However, it is important to note that, given the wide array of probability distributions that exist, this criterion may not be universally applicable. Therefore, this rule can be revised under certain prerequisites [37; 38].  
However, relatively small dispersion of the values was noted, so based on [39], it was found acceptable to reduce the number of samples down to 15. The performed Shapiro-Wilk test did not show a significant departure from normality, with parameters \(W(15)=0.9445\), \(p=0.4428\) for bending strength and \(W(15)=0.9447\), \(p=0.4455\) for hardness. Since \(p\)-values in both cases were larger than \(\alpha\), we accepted the H\({}_{0}\) hypothesis, assuming that the data were normally distributed.
# Study of Damage Mechanism and Evolution Model of Concrete under Freeze-Thaw Cycles
## 6 Conclusions  
In this study, we adopted to the ultrasonic tester and nuclear magnetic resonance (NMR) to investigating the effects of F-T cycles on the microscopic and macroscopic mechanical properties of saturated concrete specimens under uniaxial compression conditions. The main research results are summarized as follows:  
The F-T cycles can lead to the deterioration of the internal structure of the samples based on the observation of the T\({}_{2}\) spectrum using the rock structure and moisture analysis test equipment.  
P-wave velocity is negatively correlated with the F-T cycles, while the porosity is positive correlated with the F-T cycles. The peak strength and elastic modulus of the specimens decrease gradually as the F-T cycles increase, and the peak strain shows an obvious increasing trend.  
A microscopic F-T damage model, considering the pore size distribution, was established. The damage evolution law of concrete samples under different F-T cycles was analyzed based on the P-wave velocity, porosity, static elastic modulus, dynamic elastic modulus, total energy, and pore size distribution. The accuracy of the pore size distribution damage model and five other common damage models was tested using the uniaxial compressive strength (UCS) and the peak strain.  
The newly proposed damage factor and damage model, considering the definition of pore structure changes before and after the freeze and thaw cycle, can effectively evaluate the freeze-thaw cycle properties of related concrete materials in cold areas. Without mechanical tests, it can evaluate the evolution process of the freeze-thaw damage of concrete materials, providing a reference for related projects.  
Figure 10: Comparison of predicted and experimental values of peak strain for the concrete samples subjected to different F–T cycles.  
**Author Contributions:** N.Z.: writing--original draft, writing--review and editing, investigation, and methodology; S.L.: conceptualization, validation, data curation, software, and formal analysis. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [National Natural Science Foundation of China] grant number [Grant No. 52274118 and No. 52274194].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available from the corresponding author ([EMAIL_ADDRESS]) upon reasonable request.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest regarding this work.
## 4 Application of the Method  
This study aimed to develop and validate a HPTLC method suitable for the analysis of fat-soluble vitamins in various pharmaceutical preparations. Three different types of pharmaceutical dosage forms (hard gelatine capsules, soft gelatine capsules, and injection-Table 2) were selected to demonstrate the applicability of the method. The results of the quantification study are shown in Table 12. The findings revealed that 97.24% of the label claimed for vitamin D\({}_{2}\) in the S1 sample could be recovered based on three replicate analyses. For samples S2, S3, and S4, the vitamin content analyzed expressed as a percentage of the labelled content was 99.42% for vitamin D\({}_{3}\), 103.55% for vitamin E, and 94.42% for vitamin K\({}_{1}\), respectively (Table 12). All the samples contained different excipients, as listed in Table 2, but none was found to interfere with the quantification, demonstrating the broad applicability of the method.  
The developed method appears more convenient than previous HPTLC-based methods developed for the analysis of fat-soluble vitamins as it only used a simple mobile phase in a single development step with satisfactory accuracy, precision and robustness. Moreover, its sensitivity is also good with LOD and LOQ levels that were found to be similar or lower compared to previously published methods [4, 15, 16, 17, 18]. Moreover, the newly developed method can analyze four fat-soluble vitamins, vitamin D\({}_{2}\), D\({}_{3}\), E and K\({}_{1}\) whereas previously developed HPTLC methods can only identify and quantify a single vitamin, like vitamin D\({}_{3}\) in pharmaceutical preparations, vitamin A and E in human plasma and vitamin K\({}_{1}\) in pharmaceutical preparations, fruits and vegetables [4, 15, 16, 17, 18]. This illustrates the novelty of the newly developed HPTLC method in comparison of previous methods.  
Despite its advantages over previous studies, a limitation of the method is its inability to separate vitamin D\({}_{2}\) and D\({}_{3}\). However, this does not seem to have practical implications as formulations always tend to contain either one or the other but not both types of vitamin D at the same time [3].
## 3 Results
### Concentration of VOCs and Aroma Compounds/Data of Toxic Guidelines  
Table 1 summarizes the aroma compounds collected as vapor samples from the nicotine-free inhalator. A total of 12 aroma compounds were identified through sorbent tube sampling of volatiles based on GC/MS analyses. The concentrations of these compounds ranged from 0.26-7.09 ppm. The most abundant vapor compounds were menthol (5.49 ppm), (\(\pm\))-isomenthone (3.84-7.09 ppm), and limonene (3.39 ppm). Other minor constituents were also found, including neomenthol (1.32 ppm), 3-octanol (1.28 ppm), \(\alpha\)-pinene (0.73 ppm), \(\beta\)-pinene (0.90 ppm), \(\beta\)-myrcene (0.74 ppm), menthyl acetate (0.74 ppm), piperitone (0.26 ppm), m-eugenol (0.25 ppm). All detected compounds in vapor samples were identified as peppermint oil components [18; 19]. Permissible inhalation exposure limits were compared with our sampled data to assess the toxic effects of these compounds. Permissible exposure limits were based on guidelines from the Occupational Safety and Health Administration (OSHA), the derived no-effect level (DNEL), and sensory irritation levels. Menthol concentration was higher than the sensory irritation level but lower than DNEL. Concentration of (+)-isomenthone was more than DNEL (6.35 ppm), but sensory irritation values for (+)-isomenthone were not available. Myrcene and piperitone have not been identified as inhalation toxins by the RD\({}_{50}\) (exposure concentration corresponding to a 50% respiratory rate decrease) and DNEL [20; 21]. Furthermore, other compounds were observed in lower concentrations than sensory irritation and DNEL levels. Figure 2 displays chemical structures of aroma compounds detected from vapor samples from a nicotine-free inhalator, which contained alcohols (1 compound: 3-octanol) and monoterenes  
Figure 1: Plot of response factor (RF) values against carbon number (CN) of 25 standard compounds. The equation derived from these standard species was used to predict RF values of CLASS (compounds lacking authentic standards or surrogates) volatile organic compounds (VOCs). (11 compounds). The most widely present class of hydrocarbons in essential oils was identified as the terpenes. The basic component of diverse terpene compounds is a five-carbon molecule called an isoprene. As a chemical bond forms between two isoprene units, a 10-carbon molecule called a monoterpene can be produced. Monoterpenes are widely distributed in plants and show volatility during use of essential oils [22]. Table 2 summarizes the VOCs present in vapor samples of a nicotine-free inhalator. A total of 29 VOCs were identified by the HPLC-UV and GC/MS methods. Concentrations of all VOCs were estimated to be <0.2 ppm. Aldehydes (nine compounds) and acetone measured by the HPLC-UV method were quantified and ranged from 0.05 ppm (propionaldehyde) to 0.11 ppm (benzaldehyde). A total of 19 VOCs measured by the GC/MS method were quantified as present up to 0.06 ppm. VOCs detected above 0.001 ppm (or 1 ppb) were observed in the following order: toluene (0.0609 ppm), methyl ethyl ketone (0.0280 ppm), total xylene (0.0162 ppm), styrene (0.0030 ppm), and hexanoic
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of Noise Power
#### 4.3.1 Performance of Metric of Single Measurement  
In Figures 3 and 4, we have illustrated the relations between the error bounds and the deviation of the metrics of RSS, AOA and TOF [27]. The increase of the deviation of the metrics of RSS, AOA and TOF consistently decreases the Fisher information and meanwhile increases the error bounds.
# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 3 Experimental Results and Analysis
### Image Information Comparison and Evaluation  
The processes of constructing scale spaces varies between different algorithms, which include building an image pyramid and employing specific downsampling techniques. SIFT and SURF utilize Gaussian blurring and the GaussianLaplace filter for pyramid construction, respectively, and both apply average downsampling. In contrast, BRISK and ORB deviate from traditional Gaussian pyramids by creating scale spaces through direct downsampling, where each layer of the image is formed by simply removing pixels. This section describes the comparative experiments conducted to analyze the methods of Gaussian blurring + average downsampling and nonlinear diffusion filtering + maximum downsampling.  
The above figure shows the boat image boat-1 in the [PERSON] image dataset. Figure 3a adopts the Gaussian filter function + average downsampling method; it can be seen that the blurring effect cannot be controlled, and the edges and textures are not well processed, which will cause greater interference in later feature point extraction. Figure 3b adopts the Gaussian filter function + maximum downsampling method, and it can be seen that the edges and texture are improved compared with the image contour in Figure 3a, but the blurring effect of the overall image cannot be controlled, and the edges and texture are not well processed, which will cause a greater interference in the feature point extraction in the later stage. Figure 3c adopts the Gaussian filter function + average downsampling method, and it can be seen that the blurring effect cannot be controlled, and the edges and texture are not well processed, which will cause a greater interference. Figure 3b adopts the Gaussian filter function + maximum downsampling, from which it can be seen that the edges and textures are improved compared with the image contour in Figure 3a, but the overall image is still blurred. Figure 3c adopts nonlinear diffusion filtering + average downsampling, from which it can be seen that the edges and textures of the image contour are still blurred, but the clarity has been improved compared with that of the image contour in Figure 3a. The clarity of the image contour is improved compared to that in Figure 3a. Figure 3d uses nonlinear diffusion filtering + maximal downsampling, which retains more image details and texture information compared to Gaussian filtering, which retains the image information more effectively. It can be seen that the combination of nonlinear diffusion filtering and maximum downsampling has the best effect.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 2 Literature Review
### Iot Monitoring Systems
The proposed system sends an automatic notification to personnel if the thresholds of monitored parameters are abused, which contributes to taking timely necessary action to mitigate the loss of perishable products. In addition, the proposed system provides support of a scalable web dashboard for remote monitoring and personnel can check the food transport conditions on any device, at anytime, from anywhere.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 3 Experiments and Results
### Region Division Result  
In a hierarchical clustering algorithm, the number of clusters \(L\) will affect the quality of the clustering results and ultimately affect the positioning accuracy. Thus, setting an appropriate number of clusters \(L\) is necessary. Based on the experimental results obtained in Section 3.1, we choose six APs (AP1,..., AP6) as the Wi-Fi RSSI signal APs and use the coverage area of the APs as the experimental area. In addition, we choose 122 reference fingerprint points (red dots) in the experimental area, and the distance between the reference points is 0.8 m. An experimenter holds the standard mobile terminal in front of his/her chest, keeps the same height and direction and collects the Wi-Fi RSSI fingerprint data at each reference fingerprint point at a frequency of 30 s/time for 3 min. We average all the RSSI data collected from each AP to establish an offline fingerprint database. According to the established offline fingerprint database, we set \(L\) as 3, 4, 5, 6, 7 or 8 then introduce the BWP [23] index to judge whether the clustering result is good or bad. The experimental results are shown in Figure 7. We can conclude that when \(L=4\), the result is the best. Therefore, we divide the offline fingerprint database into four regions and calculate the clustering centres of each region. The final results of the regional division in the environment are presented in Figure 8.  
Figure 6: Error reduction ratio for the number of Wi-Fi APs.  
The five-pointed star icons represent the centre position of each cluster. From right to left, the coordinates of the four centre positions are (2.05, 3.85), (2.86, 13.04), (2.00, 21.50) and (8.28, 14.16).
## 3 Methodology
### Proposed Model
The reconstruction loss \(R_{loss}\) for the review \(I_{i}\) can be evaluated in the following way using mean squared error (MSE):  
Figure 2: Overview of the modified LSTM-autoencoder network. \[R_{loss}=O_{i}-I_{i}, \tag{1}\]  
To determine whether a comment is aggressive or not, we calculate the reconstruction loss between the input \(I_{i}\) and the output \(O_{i}\) of the proposed model. We use a simple neural network approach to obtain the threshold. When the reconstruction loss from the auto-encoder is passed through the neural network, each comment is classified as a common aggressive comment (CAG), a common non-aggressive comment (NAG), or common extremely aggressive comment (DAG). For each comment, the neural network returns the group label (CAG, NAG, or DAG). The overall accuracy of the model is then determined by comparing these predicted labels with the actual labels of the comments. **WisdomNet**. WisdomNet is a neural network architecture for reliable learning that can determine what cannot be accurately classified. The WisdomNet is constructed in four phases: (i) a standard neural network is trained and misclassified training samples are identified; (ii) a new network is created based on the trained network; (iii) a new neuron is added to the output layer to produce the label \"unable to classify\"; (iv) the new network is retrained using only the misclassified training samples. Similar to the neuron that returns the class label, the conjugate neuron also produces a rejection or the label unable to classify in this situation. The proposed model takes each comment in parallel through three stacked LSTM encoders (stacked LSTM encoder 1, stacked LSTM encoder 2, and stacked LSTM encoder 3). Each encoder encodes the input and immediately passes it through repeat vectors to reconstruct its shape. Each reshaped encoded output then passes through another stacked LSTM encoder. Finally, the outputs from the three stacked LSTM encoders are fed into a meta-learner to provide a single encoded output. The meta-learner is a simple RNN model that quickly learns from weighted inputs. The single output then passes through a repeat vector to reshape it before feeding into the stacked LSTM decoder. The final outputs of the decoders are fed into a meta-learner, which is a simple neural network designed to provide a single output. This single output is then used for repeat vectors to scale the information, and it is subsequently employed to compute the reconstruction loss. During the final phase of the reconstruction calculation, a neuron called _WisdomNet_ is connected to the simple neural network to learn from the misclassified outputs. We iterate the optimization process using gradient descent until we achieve an error greater than 5 and a misclassification rate higher than 5%, ensuring a highly accurate result.
# Natural Frequencies and Modes of Electrostatically Actuated Curved Bell-Shaped Microplates
###### Abstract  
Configuration-dependent spectral behavior of initially curved circular microplates loaded by a distributed nonlinear electrostatic force is investigated. The structures under consideration are distinguished by two interesting features. The first is that the plates are initially bell-shaped, rather than flat or spherical, and therefore have regions of both positive and negative curvature. Second, the plates are sufficiently curved to exhibit snap-through buckling and bistability. The structure is described in the framework of the nonlinear Foppl [PERSON] shallow plate theory. The influence of the initial curvature and loading on the free vibrations around unloaded and deformed equilibria is investigated. The results of the Galerkin model backed by the finite elements analysis show that the modes of even slightly curved bell-shaped unloaded plates differ significantly from those of the initially flat plates. As a result, when the natural modes of a curved plate are used as the base functions, a significantly better convergence of the RO model is achieved. In the vicinity of the critical snap-through and snap-back configurations, the sensitivity of the natural frequencies to the plate deflection is much higher than in the unloaded state. This high tunability opens new opportunities for the design of better resonant sensors with enhanced performance.  
MES; curved micro-plate; snap-through; bistability; frequency tunability; reduced-order modeling +
Footnote †: journal: Applied Sciences