esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microbiological Quantification  
The microbiological counts of burgers packed in MAP during chilled storage are shown in Figure 1. Initial counts were higher than in burgers made with other animal species due to the type of evisceration and manage of carcass, which is performed in the field instead in a slaughterhouse [26]. In this sense, initial counts around 4.0-5.1 log CFU/g of TVC, 3.0-4.5 log CFU/g of psychotrophic, and 2.0-4.5 log CFU/g of LAB were found in pork burgers [23].  
Freshly made burgers showed a TVC of 4.8-5.2 log CFU/g, but a significant increase (\(p<0.001\)) was observed during storage in all samples. In general, wine lees addition did not affect TVC (\(p>0.05\)). In this sense, other natural extracts (chestnut and seaweed) did not influenced TVC throughout storage, while tea and grape extracts and tomato powder led to a decrease in pork burgers [23; 27]. Furthermore, it was observed that chilled storage caused a progressive increase (\(p<0.001\)) in psychotrophic aerobic bacteria counts, especially in control burgers, reaching values of 8 log CFU/g, while burgers with lees kept the initial counts during the first eight days of storage (5.7-6.5 log CFU/g). _Enterobacteriaceae  
Figure 1: Modifications in microbial counts of deer burgers packed in MAP during chilled storage. Symbols: — C; — Lva; — Lvb; — Lpa; — Lpb. C: sodium ascorbate control; Lva: 2.5% “verdejo” lees; Lvb: 5% “verdejo” lees; Lpa: 2.5% “palomino” lees; Lpb: 5% “palomino” lees.  
counts exhibited a significant increase (\(p<0.001\)) over time in control burgers reaching 3.3 log CFU/g at the end of storage. Burgers with 2.5% wine lees (Lva and Lpa) showed this growth pattern but lower counts (2.6-2.9 log CFU/g). No growth was observed in burgers with 5% lees (Lvb and Lpb) maintaining 2.4-2.6 log CFU/g during storage.  
The inhibition of psychotrophic and _Enterobacteriaceae_ growth was also observed in other studies where the ability of wine pomace to extend the shelf life was tested [13; 15].  
It is known that LAB constitute a wide part of the natural microbiota of meat packed in MAP [28]. A significant increase (\(p<0.001\)) in LAB counts was observed during chilled storage in all samples, however no significant effect of wine lees addition was observed after 12 days of storage.  
Similarly, yeast counts increased with storage time, although, in the case of burgers with 2.5% lees (Lva and Lpa), the final yeast counts were significantly lower (\(p<0.001\)) than control.
###### Abstract
This technique depends on the direct extraction of the energy associated with the signal oscillations, and its operation provides a finite number of intrinsic mode functions (IMFs). Secondly, the IMFs are analyzed in the spectral-domain by using TV-AR spectral-based analysis, which allows tracking the time-varying frequency components involved in each IMF, independently. Based on this feature extraction methodology, this work focuses on studying the CTG classification performance by considering the complete FHR signal, deceleration episodes, and resting periods (between contractions) separately. Considering that recent research has shown that a separate analysis of these FHR signal segments can improve the detection of fetal distress [19], we propose to extract information from those segments independently and determine their contribution in CTG analysis evaluated by a support vector machine (SVM) based classification. The main hypothesis is that the feature extraction approach based on CEEMDAN and TV-AR modeling, applied separately to the complete signal, deceleration episodes, and resting periods, improves the CTG classification performance compared with the traditional analysis based only on the complete signal. The rest of the paper first presents a brief overview of related works in the field of FHR signal feature extraction (Section 2). Then, Section 3 explains the methodology and describes, in detail, the signal processing techniques used for the CTG signal analysis. Continuing, Section 4 presents a comprehensive analysis and foundations behind the feature extraction operation and the strategy used for their evaluation. Finally, Section 5 concludes this paper and discuses perspectives for future work.
## 4 Experiments
### Quantitative Evaluation
#### 4.4.3 Parameter Sensitivity Analysis  
In order to demonstrate the robustness of our proposed model, we also construct an experiment of parameter sensitivity analysis. The quantitative comparison under four metrics is shown in Table 3. Our experiments show a deviation within 1-3% on the evaluation of the four metrics, which indicates that the parameters of our model have little influence to salient segmentation results.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT  
This article delves into the significant role of blockchain technology within IoT applications in agriculture and animal husbandry and its importance for data processing and control, supply chain management, efficient resource allocation, building trust frameworks, and ensuring system security and privacy. First, we introduce how blockchain can improve data processing and control, enhance data transparency, immutability, and traceability, and provide a reliable data management platform for agriculture and animal husbandry. Next, the article examines how blockchain technology can improve supply chain management, focusing on its capability to improve both transparency and efficiency via a decentralized record-keeping mechanism.  
Subsequently, we discuss how blockchain can promote efficient resource allocation, automate the execution of agricultural contracts and transactions through smart contracts, optimize resource distribution, and reduce operating costs. In addition, this article also explains how blockchain can help build a trust framework by providing an immutable record and verification mechanism to enhance trust among participants.  
Finally, we focus on discussing the strategies and technologies of blockchain in ensuring system security and privacy, exploring how to use blockchain encryption and access control functions to protect agriculture and animal husbandry IoT data from unauthorized access and the risk of leakage. IoT in agriculture and animal husbandry can be enhanced through this series of analyses and how it can drive the industry towards a more efficient, transparent, and secure direction.
## 3 Uncertainty-Controlled Remaining Useful Life Prediction with a Data Augmentation Strategy
### Data Augmentation Based on Degradation Modeling and Sobol Sampling
If there are M run-to-failure data, M sets of parameters of \(a,b\), and \(e\) can be acquired after the model fitting on the M run-to-failure datasets. Among these sets of parameters, the maximum and minimum values should be chosen. Ranges of each parameter can be recorded as \([a_{min},a_{max}]\), \([b_{min},b_{max}]\) and \([e_{min},e_{max}]\). 3. The Sobol sequence sampling is used to get different combinations of parameters. Random sampling algorithms are quasi-random and limited to one period. When the cycle is exceeded the period, they are repeated and are no longer mutually independent random numbers. Sobol sequences sampling method focus on producing uniform distributions in the probability space compared with the random sampling method. Localized clustering can be avoided in this way. As one of the low deviation sequences, Sobol sequence sampling is superior to other low deviation sequences. The random numbers generated afterward will be distributed to the areas that were not previously sampled. A set of independent parameters can be acquired after one Sobol sequence sampling among the ranges of \([a_{min},a_{max}]\), \([b_{min},b_{max}]\) and \([e_{min},e_{max}]\). The algorithm of Sobol sequence sampling is as follows. Consider \(i\) random data \(Z_{i}\) are generated in the range of \([a_{min},a_{max}]\). \(Z_{i}\) follows the normal distribution. A non-integrable polynomial \(f(Z)\) can be constructed as \[f(Z)=Z^{n}+m_{1}Z^{n-1}+m_{2}Z^{n-2}+\ldots\ldots+m_{n-1}Z+1,\] (8) where the \(n\) is the highest order. The \(m_{1},m_{2},m_{2},  
where the \(b_{1},b_{2},b_{3},....\) is the binary representation of \(i\). After the Sobol sequence sampling operation is implemented on the ranges of parameters \(a,b\), and \(e\). \(i\) sets of parameters of the degradation model can be acquired. 4. Now, \(i\) sets of parameters sampled in the last step are substitute to the degradation model function in Equation 6 again without \(\varepsilon(t)\) to get \(i\) new run-to-failure data as \[X_{t}^{i}=a\exp(-\frac{e^{2}}{2}+b(t-\gamma)),\] (11) the rand noise \(\varepsilon(t-\gamma)\) is added in the new sample to imitate the random shock in the working situation in the last step. The \(i\) run-to-failure data are prepared and can be sent to the Wiener-LSTM model for the training process.
# Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM
## 5 Conclusions  
In order to recognize the welding quality automatically during high-power disk laser welding, the SVM method was employed to build the classifier. A feature selection method based on the SFFS algorithm was applied to detect the optimal feature subset for SVM. The SVM classifier generated by the ten selected features can reach an accuracy of 98.11% by 10-fold cross validation. Experiment results show that this method is satisfactory, and has the potential to be applied in the real-time monitoring of high-power laser welding.  
The images of the metal vapor plume and spatters captured during high-power disk laser welding contain a lot of information associated with the welding quality. The fifteen extracted features, especially the centroid and height of the plume, the area, perimeter, centroid, average grayscale value and quantity of the spatters play very important roles in recognizing the welding quality.  
Despite the many advantages of the proposed method, some limitations remain. The most essential one is that the testing phase of cross validation was not conducted during the welding, but using the data collected from the welding and simulating in computer. The computer used is a Lenovo ideapad 300-14 IBR (CPU Intel Celeron N3150 1.60 GHz; RAM 8.00 GB). In MATLAB R2010b, the processing time, including image processing, feature extraction and classification, of 1 testing fold (550 samples) is 26.71 s for SVM and 24.92 s for BP, using the optimal feature subsets. However, many viable methods exist to reduce the processing time, such as using more advanced equipment and programming with C. Other than these, using the unsupervised mode for classification can be another solution. More experiments are in planning, and the aforementioned methods will be investigated.  
This work was financially supported by the National Natural Science Foundation of China (51505158,11674106), and the Natural Science Foundation of Guangdong Province (2014A030310153). Many thanks are given to Katayama Laboratory of Joining and Welding Research Institute of Osaka University, for the technical assistance of laser welding experiments.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
## 4 Interference Analysis  
This section presents the different parameters and formulas used in our study. The first subsection introduces some parameter definitions and relevant relations used in the study. The second subsection introduces the formulas that will help evaluate the impact of the total interference on the signal.
# Response to Sulfur Dioxide Addition by Two Commercial _Saccharomyces cerevisiae_ Strains
## 1 Introduction
Furthermore, many SO\({}_{2}\) studies used differences in gene expression as the sole method of inferring differences in yeast resistance mechanisms, but changes in gene expression are not always correlated with changes in protein translation or metabolic activity [21; 22; 23; 24]. Additionally, few studies have investigated the effects of SO\({}_{2}\) addition on the production of volatile secondary metabolites by _S. cerevisiae_ strains [25; 26; 27]. Here, we investigate both the potential sulfite resistance responses of two commercial strains of _S. cerevisiae_, as well as their production of volatile compounds that are important to the sensory profile of wines. The objective of this study was to observe the responses of two commercial _S. cerevisiae_ strains to the presence (50 mg/L) or absence (0 mg/L) of SO\({}_{2}\) in fermentations of pinot gris juice conducted under laboratory conditions. Specifically, fermentation kinetics, yeast abundance, SO\({}_{2}\) concentration, H\({}_{2}\)S production, acetaldehyde production, and secondary metabolite composition as a result of SO\({}_{2}\) addition were compared between the two strains. The two yeast strains selected for comparison were Lalvin(r) QA23 and Lalvin(r) BRL97, both of which are commonly used to inoculate fermentations at commercial wineries. QA23 was originally isolated in Portugal and is recommended for white wine production, while BRL97 was isolated from Italy and is recommended for red wine production. However, BRL97 was recently shown to be genetically equivalent to Lalvin(r) ICV-D47 via SNP analysis [28]. Interestingly, ICV-D47 is recommended for white wine production, and the abundance of ICV-D47 was previously found to increase in a dose-dependent manner with increasing SO\({}_{2}\) concentration in uninoculated commercial fermentations [29]. Both QA23 and BRL97/ICV-D47 are killer-active [30], and possess unique enological properties, including \(\beta\)-glucosidase production (QA23) and colour stabilization (BRL97) (www.lallemandbrewing.com). Therefore, we predicted that these strains would differ in both their susceptibility to SO\({}_{2}\) and in their response mechanisms to SO\({}_{2}\). We further predicted that these differences in yeast behaviour would also result in differences in their production of volatile secondary metabolites, both between strains and between SO\({}_{2}\) treatments within one strain.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 1 Introduction  
Task allocation is a general form of an assignment problem belonging to the combinatorial optimization problem (COP) in mathematics [1]. In the field of radio and television transmission, the problem involves the assignment of multiple transmission tasks to multiple pieces of transmission equipment at the same time. Because the equipment has different effects when performing different tasks, the comprehensive implementation effect of the static allocation of the whole task group depends on the task allocation arrangement scheme. Transmission task static allocation (TTSA) is a typical NP-hard problem.  
Typical COPs include the traveling salesman problem (TSP), vehicle routing problem (VRP), and job shop scheduling problem (JSSP). There are two methods for solving a COP: an approximation algorithm and an exact algorithm. The solution time of the exact algorithm increases with the size of the problem, and an optimal solution cannot be calculated within the effective timescale for large-scale problems. It is an effective way to solve COPs by using an intelligence algorithm for the approximation algorithm, but for specific problems, the adaptive improvement of the intelligence algorithm can obtain better calculation results [2; 3; 4].
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 3 The Proposed Dimming Control Scheme
### Motivation of the ECWCS Scheme  
In order to realize dimming control for VLC systems with constant transmission efficiency, paper [14] proposed a scheme based on block coding via the bitwise AND operation. The error performance and spectral efficiency still have room for improvement. Motivated by the works in [12; 13], we propose a dimming control scheme for VLC systems with constant transmission efficiency. In the proposed scheme, the code weight of the codewords in a set are all odd or all even. Therefore, the minimum Hamming distance of a codeword set is 2, which can improve the error performance to a certain extent. Meanwhile, the extensional constant weight codeword set scheme can achieve arbitrary dimming control within a wide range.
## 4 Results
### Speed Dependency  
The second study examines the influence of speed dependencies. As presented, the data are collected at a constant speed with seven different rotational steps. In the real application, a constant speed at defined values is not possible. Therefore, the influence of the speed on the classifier accuracy should be investigated.  
Two investigations are performed. In the first investigation, the classifier is trained with rotational speeds of 700, 1700, 2700 and 4200 rpm and tested with the other three speeds. In the second investigation, the classifier is trained with rotational speeds of 1200, 2100 and 3400 rpm. The following equations provide an overview of the investigations, where \(D\) is the whole dataset:  
\[\text{Investigation 1: }D_{\text{train}}=D\setminus D_{\text{test}},\quad D_{ \text{test}}=\{1200,2100,3400\}\text{ rpm}, \tag{8}\]  
\[\text{Investigation 2: }D_{\text{train}}=D\setminus D_{\text{test}},\quad D_{ \text{test}}=\{700,1700,2700,4200\}\text{ rpm}. \tag{9}\]  
Again, all investigations are performed using tenfold cross validation. The results are shown in Table 5. The highlighted data show the results of the tested data, which means the trained classifier does not know those rotational speeds. First of all, the BAC is decreasing for samples, if the present rotational speed is not used in the training process. In the worst case, data samples collected at 700 rpm only reach an averaged BAC of 0.20, and the worst iteration only reaches 0.06. Therefore, the classifier becomes worse than random prediction. Secondly, it can be derived from the results that, compared among rotational speeds not used in the training process, the intermediate rotational speeds can achieve the best classification accuracy. Therefore, the data collected at 2100 rpm still reach an averaged BAC of 0.83 in the first investigation, although this speed is not trained. The second and third best results are reached at 3400 and 1700 rpm. Also, it can be noted that the results from the first investigation achieve higher BAC. The main difference to the second investigation is that only intermediate speeds are tested and wrapped by trained rotational speeds. In addition, with the worst results of the lowest and highest rotational speeds and the best results for the middle rotational speeds, it can be derived that for a good classification of unknown rotational speeds, a suitable coverage of rotational speeds  
Figure 4: Confusion matrix for the worst iteration of the classifier differentiating seven classes.  
during training is essential. Therefore, the rotational speeds in the training set should cover the whole range and be evenly distributed since the investigation shows that data of rotational speeds, which are wrapped by trained rotational speeds, result in good accuracy.  
It can, therefore, be assumed that the ranges in between the known rotational speeds from the present measurements are also classifiable.
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
## 3 Experimental Tests
### Interpolation and Anomaly Detection with Real-World Personal Data
#### 3.3.2 Application of Interpolation and Anomaly Detection with Real-World Personal Dataset Two  
This dataset was collected for 8 days between 25 January 2019 and 1 February 2019. The length of the dataset is 62,878, having 74 missing datapoints in total. Dataset 1 used in the previous experimental test was very stable, because the distribution of PM data was mostly less than 100 \(\upmu\)g/m\({}^{3}\) during the data acquisition period. However, the PM data in Dataset 2 (Figure 6) reached up to 2000 to 8000 \(\upmu\)g/m\({}^{3}\) with 10-s intervals and showed a more dynamic change in the distribution of PM data, implying that the subject had various activities or was exposed to many different environmental conditions containing all the data-distribution patterns of rising, falling, and stable PM\({}_{2.5}\) concentrations.  
Figure 5: Anomalies are shown in red.  
Figure 6: This is Dataset 2, collected for 8 days between 25 January 2019 and 1 February 2019. The length of the dataset is 62,878, having 74 missing datapoints in total.  
The same bandwidth estimation steps were also carried out as described previously. Figure 7 shows the chosen bandwidth of 20 estimated for the flat part of the data between 33,000 and 33,600 in the time index. The interpolated missing data are shown in red in Figure 8, overlaid on the entire dataset.  
Figure 8 can be seen as two sets of data with different natures; in the front part, the data fluctuates heavily, but in the back part, the data are relatively stable. In particular, the front data were generated between 14:00 and 18:00, probably by a wide variety of movements. Unlike the previous Dataset 1,  
Figure 8: Missing data is interpolated and shown in red.  
Figure 7: Optimal bandwidth for this dataset is chosen as being 19.  
the data loss is relatively small, even under the condition of dynamic movements; so, a very stable performance sensor may be used. In the case of the latter part of the data, it appears that more data loss occurs than from the front part, but not much data are actually lost. Overall, a fairly stable interpolation result can be observed.  
Finally, anomalies are also shown in red, in Figure 9. Overall, eight anomalies seem to be detected by visual inspection. If you look at the enlarged part of the data (between 2750 and 4000 out of the whole dataset), you can see that the detected outliers can be accepted as real outliers, because the corresponding PM concentration significantly changes within a 10-s period. In this experiment set, the threshold value 1000 was used as the criterion to detect outliers, and the threshold was also empirically chosen, analogously to the approach described in Section 3.3.1.
## 3 Example Analysis
### Data Feature Selection  
The visualization of logging data is shown in Figure 7.  
Various logging parameters exhibit different correlations with Vs. The R\({}^{2}\) between logging data and Vs is shown in Figure 8; in the figure DTM_S refers to the shear wave velocity.  
Figure 7: The visualization of logging data.  
The strong correlation of logging data directly affects the accuracy of Vs prediction. The R\({}^{2}\) between Vs and the other parameters, in descending order, are as follows: P-wave (0.8653), density (0.3601), neutron porosity (0.0243), caliper (0.0103), gamma ray (GR) (0.0096), resistivity (LLD) (0.0041), lithology (Lith3) (0.0038), and SP (0.0020). Based on the order of R\({}^{2}\), three less relevant aspects (SP, lithology, and resistivity) are excluded from the experiment. Excluding less relevant parameters can reduce overfitting, enhance model generalization, and extract important features to improve model accuracy and generalizability. Caliper data measure the diameter of the wellbore, providing information on wellbore size [43]. Density data quantify the mass per unit volume of rock, aiding in rock-type identification and stratigraphic analysis [44]. Gamma-ray surveys detect radioactive elements within formations, providing insights into rock composition and hydrocarbon potential [45]. Neutron porosity measurements assess water content and porosity in rock formations, aiding in aquifer identification [46]. P-wave data measure the speed of seismic waves through rock, facilitating the rock-type identification and stratigraphic analysis [47].  
Figure 8: The scatter plots illustrating the correlation between logging parameters and Vs.