esa-sceva/satcom-chunk-collection huggingface dataset:
# Functional Characterization of _Saccharomyces_ Yeasts from Cider Produced in Hardanger
## 2 Materials and Methods
### Determination of Volatile Compounds Using HS-SPME-GC-MS  
Selected aroma compounds were determined in the ciders using an automated robotic system for Solid Phase Micro-Extraction (SPME) in head space (HD) and injected on a gas chromatograph coupled with a mass spectrometric detector (GC-MS). Esters, C6 alcohols, and volatile phenols were analyzed using a method adapted from a previously published protocol [25]. Samples were extracted by headspace Solid Phase Micro-Extraction (HS-SPME) using an SPME fiber assembly (50/30 \(\upmu\)m DVB/CAR/PDMS, Stableflex, 24 Ga, Autosampler, Gray (Supelco, St. Louis, MO, USA)). To a 20 mL SPME vial, 3 mL of the cider sample was added with 2 g NaCl and 3 mL deionized water, and 20 \(\upmu\)L solution of internal deuterated standards (ethyl butyrate-4,4,4 d3, ethyl d5 hexanoate, ethyl octanoate d15, ethyl trans-cinamate d5) was added. The solution was then homogenized using a vortex mixer and the samples were loaded into a Gerstel MPS Robotic Autosampler. The program consisted of introducing the fiber into the SPME Arrow Conditioning Module for 2 min at 270 \({}^{\circ}\)C. The fiber was then introduced into the headspace of a sample vial for 30 min at 40 \({}^{\circ}\)C while simultaneously vortexing the sample with the agitator at 250 rpm. The fiber was then transferred to the injector for desorption at 250 \({}^{\circ}\)C for 15 min. The time for sample injection into the GC column was set to 30 s, followed by cleaning of the fibers in the SPME Arrow Conditioning Module at 270 \({}^{\circ}\)C for 10 min.
## 4 Discussion
Online viscometers have been tested in pilot mills [15; 16]; however, there are no such devices that can be installed permanently in the mills [17]. Optical methods, such as refractometry and NIRS, can be used at the pulp mills for the online analysis of constituents in the kraft process. However, the probes of the optical devices require continuous cleaning because of fouling. In contrast to the optical methods, TD-NMR is non-fouling, as the sample flows through the pipe and does not have any direct contact with the probe. The online TD-NMR system has previously been demonstrated to be useful in liquid fuel analysis [19; 30]. Online measurements of the R\({}_{2}\) have shown the same order of accuracy as the R\({}_{2}\) measured conventionally in NMR tubes. The system was also used to perform online measurementsof water during heavy metal precipitation in a real mine [20], where the environmental conditions were rather harsh. To the best of the authors' knowledge, the implementation of the TD-NMR in online measurements of black liquor at a pulp mill was carried out for the first time. TD-NMR and compact NMR spectroscopy are being considered for use as industrial sensors in the near future, owing to their many advantages, such as their non-fouling, non-destructive, rapid, and robust properties. However, some challenges still need to be overcome, including the pre-polarization time, cost of implementation, and low sensitivity [31; 32]. The results prove that TD-NMR may be used as a sensor in industrial processes, which is highly in demand given the current trends in digitalization and implementation of the Industrial Internet of Things [33]. The proposed system can be considered as an intelligent measurement system that can help to more efficiently control the evaporation of black liquor and the pulping process.
## 3 Results and Discussion
### Simulations for Chain-Based Network
We track how the three different fusion schemes are able to track the varying parameter under different communication error rates as shown in Figure 10.  
As it can be observed from results in Figure 10, all considered fusion methods follow the sinusoidal variation as the real system state for under low communication errors. That means for BER of 0.0001, there is less difference between the network mean estimates of different fusion techniques as mean estimates almost overlap on each other. However, when BER is 0.05, it can be observed a deviation among the mean estimates three different fusion techniques. EBDKF and AVG fusion methods seem to have a higher deviation from the actual system state than the estimates from the proposed method. To prove the preceding argument, we calculated the network MSE for two different BER values for three fusion techniques as shown in Table 1.  
Figure 9: The total retransmission attempts used by AVG and EBDKF fusion methods in a retransmission allowed chain IoT platform. As it is evident from the results summarized in Table 1, network MSE performance for dynamic parameter is such that EBDKF has highest accuracy for low BER and vice versa. Average method has least accuracy under low BER and moderate accuracy under high BER. The proposed method has moderate accuracy under low BER and highest accuracy under high BER. Therefore, the performance of the three considered fusion techniques in terms of accuracy for dynamic system parameter is similar to that of deterministic parameter. Therefore, the argument that proposed method outperforms both AVG and EBDKF under high communication errors can be derived for dynamic system parameter situation also. The reason for overlapping of estimates when BER of 0.0001 and the deviation when BER of 0.05 which was observed in Figure 10 can be explained using network MSE values summarized in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Function** & **MSE for BER = 0.00001** & **MSE for BER = 0.05** \\ \hline EBDKF & 0.0428 & 266.5 \\ AVG & 0.1162 & 2.0107 \\ Proposed method & 0.0702 & 0.3019 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network MSE for a dynamic system parameter for different fusion functions under different BER. Figure 10: The performance comparison among the proposed statistical, AVG and EBDKF for a dynamic system parameter having communication link with (**a**) BER of 0.0001 and (**b**) BER of 0.05. Table 1. The MSE values of the three fusion techniques are very low and there is less difference between them when BER is 0.0001 and vice versa when BER is 0.05 is the reason for preceding observation.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 3 Method
### Foreground Depth Prediction  
Due to depth variations being nonlinear in outdoor scenes, foreground segments \(f_{s_{i}}\) are used as clues to improve foreground depth estimation in order to improve the depth prediction of object surfaces. The architecture of the foreground depth prediction model is shown in Figure 3. The \(f_{a_{i}}\) and \(f_{s_{i}}\) are concatenated together and a multi-scale depth decoding scheme is used to predict the foreground depth map \(d_{f}\) (where \(f=1\) to 5). The foreground depth prediction loss function in [68] can enhance foreground depth prediction by penalizing depth errors in foreground object surfaces. The loss function is shown below:  
\[L_{fg}=\lambda_{f}\times E_{fg}+(1-\lambda_{f})\times E_{bg}, \tag{3}\]  
where \(E_{fg}\) and \(E_{bg}\) are the foreground and background depth root mean square errors (RMSEs), respectively, and \(\lambda_{f}\) is a coefficient. As in [68], we set \(\lambda_{f}\) as 0.8.
## 4 Experimental Setup  
The light source used in the experiment was SLD (EXS210090-01, EXALOS), which had a central wavelength of 840 nm, a spectral bandwidth of 50 nm, and an output power of 12 mW. The light beam from the light source was directed to a \(2\times 2\) fiber optic coupler that acts as fiber-optic Michelson interferometer and is split into a reference and sample arm. The sample arm consists of a fiber-optic collimator (F810 APC-842, THORLABS), an achromatic doublet lens (AC254-040-B-ML, THORLABS) and a 2-axis Galvano-scanner to perform lateral scanning over a sample. The same devices, except for the 2-axis Galvano-scanner, were used in the reference arm to match the dispersion balance. The reflected light beams from both arms were recoupled to the fiber-optic collimator and generated interference signals depending on the OPD between two arms. The interference signal is propagated through single-mode fiber to a spectrometer, where the interference signal of 2048 data points was recorded by a high-speed frame grabber (NI PCI-e 1433, NATIONAL INSTRUMENTS) as a function of the spectral components of the SLD. After removing background noise and applying the Hanning window, the spectral signal was subjected to k-linearization to convert it as a function of wavenumber. For k-linearization, a wavenumber mapping table for each wavelength was prepared prior to the experiment using five fiber Bragg gratings. The interference signal after k-linearization was subjected to a discrete Fourier transform. OCT images were reconstructed by mapping Fourier transformed 1024 data on a grayscale. A program developed using LABVIEW was used for the system operation and signal processing.  
Figure 6: The modulation transfer function (MTF) curve for (**a**) the ADL and (**b**) the TFL at three incident angles of 0 degrees, \(-3\) degrees, and 4 degrees corresponding to three wavelengths of 815 nm, 840 nm, 865 nm, respectively.
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.2.5 Fishing Grounds Grade Classification  
Tertiles provide some indication of the dispersion of the overall data distribution and are widely used in statistical studies and fishing grounds grade classification [40]. The growth habit and migration route of tuna are closely related to seasonal changes, resulting in significant differences in CPUE from month to month. Therefore, the data corresponding to the zero CPUE value were excluded from this study. The data for each month that were below the first CPUE tertiles (<33.3%) were classified as \"low-yield\" fishing grounds according to the tertiles, and those that were above the first CPUE tertiles (>33.3%) were classified as \"high-yield\" fishing grounds.
## 3 Results and Discussion
### Simulations
#### 3.1.1 Effect of the Perturbation  
We investigated how the entity of a perturbation affects the tomographic reconstruction by changing the heterogeneity absorption coefficient \(\Delta\mu_{a}\) of 0.04, 0.017, 0.01, and 0.005 mm\({}^{-1}\) at the depth of 15 mm, which is a depth where a brain activation is mostly expected. The tomographic reconstructions for \(x\)-\(y\) images at specific \(z\) values are shown in Figure 3. Specifically, for the variations in \(\Delta\mu_{a}\) at 15 mm of depth, the system is able to identify the perturbation down to 0.017 mm\({}^{-1}\), close to the expected \(x\)-\(y\) region.  
Figure 2: Reference system: in this representation of the upper surface of the medium, the zero coordinate of all the axes is the top left corner of the medium itself. Optodes scheme: the filled black dots represent the eight detectors, while the central one the source.  
Probably, lower \(\Delta\mu_{a}\) gives rise to signal variations that are too low to be efficiently detected by this system, which could explain the very pale and quite negligible darker shadows in the 2D representations for \(\Delta\mu_{a}=0.01\) mm\({}^{-1}\). However, for all of the inclusions, depth is underestimated by about 5 mm, since the darker regions appear at shallower depths with respect to slices where a white circle is depicted, highlighting slices where the inclusion is expected. Furthermore, artifacts always affect the superficial layers, showing the disposition of the optodes banana shapes.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
###### Abstract
Despite its many advantages, the deployment of MALDI-TOF MS in the food sector presents certain challenges, including the need for robust and curated spectral databases that reflect the diversity of microorganisms encountered in food environments, the difficulty in analyzing mixed or complex matrices, and the current dependence on cultured isolates. Addressing these limitations through ongoing technological advances and efforts to standardize procedures will be crucial for realizing the full potential of MALDI-TOF MS in food safety [34; 35; 28; 15; 36]. This review aims to provide a comprehensive overview of the principles, current applications, advantages, and limitations of MALDI-TOF MS in the diagnosis of microbiological contamination in food. Special emphasis is placed on the detection and identification of foodborne pathogens, microbial spoilage organisms, and the technique's expanding role and traceability assessments. In doing so, the review also outlines future directions for research and technological development, emphasizing the integration of MALDI-TOF MS into modern food safety management systems.
## 5 Conclusions  
In conclusion, we have performed an analysis of narrow-line MOT of Sr atoms both for earth gravity and microgravity environments by Monte Carlo simulation. This analysis suggests that the single-frequency, red MOT based on the narrow-line transition \((5s^{2})^{1}S_{0}-(5s5p)^{3}P_{1}\) in Sr atoms can be substantially influenced by gravity. With the favor of gravity, the atoms in MOT on the ground can be cooled down in a manner of damped oscillation near the bottom of the box-like MOT. However, if the same MOT is in a \(\mu\)-gE, atoms will fly back and forth due to the bouncing at the edge of the MOT. We herein propose a possible solution to realize a dense and cold enough red MOT in a \(\mu\)-gE, i.e., utilizing a dual-frequency laser for the MOT instead of single-frequency laser, where one frequency is detuned a small amount to form a strong damping trapping at the center of the MOT, and the other frequency is greatly detuned to form an outer shell with enough volume to allow obtaining a high transfer efficiency from the previous stage. This study paves a way for the space-borne scientific experiments, such as optical lattice clocks, BEC, and atom interferometry, based on the alkaline earth ultracold atoms.  
Conceptualization, H.L. and H.C.; formal analysis, J.R. and H.L.; funding acquisition, J.R. and H.C.; investigation, J.R. and H.L.; methodology, H.L.; project administration, H.C.; resources, H.C.; software, J.R. and H.L.; supervision, H.L. and H.C.; validation, X.L.; visualization, J.R.; writing--original draft, J.R. and H.L.; writing--review and editing, J.R., H.L., and H.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (grant numbers 61775220 and 11903042), the National Key R&D Program of China (grant number 2016 YFF0200201), the Key Research Project of Frontier Science of the Chinese Academy of Sciences (grant number QYZDB-SSW-JSC004), and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDB21030100). The authors declare no conflict of interest.
# Elemental Profiles of Legumes and Seeds in View of Chemometric Approach
## 3 Results and Discussion  
The analyzed elements concentrations in legumes and seeds of oleic plants were characterized by the arithmetic mean value, the corresponding standard deviation (SD), and ranges for wet weight (w.w.) basis in Table 2. Lead concentration in all the analyzed samples was below the LOQ (30 ug/100 g), thus, it was eliminated from the data analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Element**} & **Declared Value** & **Obtained Value** & \multirow{2}{*}{**Recovery (\%)**} & **RSD (\%)** \\  & **(ug/g)** & **(ug/g)** & & \\ \hline P \({}^{1}\) & 2840 & 2900 \(\pm\) 121 & 102 & 4.26 \\ \hline P \({}^{2}\) & 5180 & 5340 \(\pm\) 94.8 & 103 & 1.83 \\ \hline K \({}^{1}\) & 16,600 \(\pm\) 1200 & 14,780 \(\pm\) 750 & 89.0 & 4.51 \\ \hline K \({}^{2}\) & 32,500 & 27,940 \(\pm\) 932 & 86.0 & 2.87 \\ \hline Na \({}^{1}\) & 44 \(\pm\) 6 & 39.8 \(\pm\) 0.16 & 90.5 & 0.36 \\ \hline Na \({}^{2i}\) & 580 & 522 \(\pm\) 23 & 90.0 & 3.97 \\ \hline Mg \({}^{1}\) & 1700 \(\pm\) 200 & 1740 \(\pm\) 81.9 & 102 & 4.82 \\ \hline Mg \({}^{2}\) & 2160 & 2110 \(\pm\) 35.6 & 97.7 & 1.65 \\ \hline Ca \({}^{1}\) & 4300 \(\pm\) 400 & 3820 \(\pm\) 37 & 88.8 & 0.86 \\ \hline Ca \({}^{2i}\) & 18,500 & 17,450 \(\pm\) 78 & 94.3 & 0.42 \\ \hline Zn \({}^{1}\) & 26.3 \(\pm\) 2.0 & 24.2 \(\pm\) 0.4 & 92.0 & 1.52 \\ \hline Zn \({}^{2}\) & 38.6 & 35.3 \(\pm\) 1.78 & 91.4 & 4.61 \\ \hline Fe \({}^{1}\) & 264 \(\pm\) 15 & 239 \(\pm\) 4.96 & 90.5 & 1.88 \\ \hline Fe \({}^{2}\) & 148 & 137 \(\pm\) 7.9 & 92.6 & 5.34 \\ \hline Cu \({}^{1}\) & 17.3 \(\pm\) 1.8 & 16.7 \(\pm\) 0.82 & 95.5 & 4.74 \\ \hline Cu \({}^{2}\) & 5.67 & 5.45 \(\pm\) 0.14 & 96.1 & 2.47 \\ \hline Mn \({}^{1}\) & 1240 \(\pm\) 70 & 1150 \(\pm\) 54.0 & 92
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Evaluation of the Models on the Test Set of the Primary Dataset
The evaluation comprises four important metrics: accuracy, precision, recall, and F1_score, which provide insight into the models' specific performance features. Inception achieves a 96.96% accuracy, nearly 97% precision and recall, and harmonic balance with a 96.77% F1_score. While Xception trails in accuracy somewhat (96.1%), it maintains a strong  
Figure 13: Comparative analysis of detection abilities of different models on test dataset. balance, with an F1_score of 95.87%. VGG16 performs brilliantly, with a 95.67% accuracy and an F1_score of 95.29%. AlexNet, on the other hand, displays a considerable drop in accuracy at 80.51%, as well as degraded precision and a lower F1_score, indicating potential constraints in precision-focused tasks. Yolo v6 and v7 also performed satisfactorily well, with above 90% accuracy. Yolo V8, on the other hand, is the standout performer, displaying astounding brilliance across all metrics. To summarize, selecting an ideal model should not be based just on accuracy, but rather on a thorough examination of the sensitive trade-off between precision and recall, particularly in cases where misclassifications have major effects. Figure 14: Confusion matrices of the performance of the models on the test dataset. Figure 15: Comparative analysis of detection capabilities of different models on secondary dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1\_Score** \\ \hline Inception & 96.96 & 96.89 & 97 & 96.77 \\ \hline Xception & 96.10 & 96.42 & 96.1 & 95.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance measures of different models on the test dataset.
## 5 Conclusions  
Most SAR studies focus on SAR image application. However, it is difficult for researchers to maintain their own SAR systems, and purchasing SAR images is cost-prohibitive. For commercial data, it costs more than 2,000 USD for a standard strip-map image. If researchers have their own SAR systems, they can easily obtain data for the region of interest. Although system development requires significant time and cost, it is more efficient to have in-house development technology and study the results. We have spent many years conducting research with the help of the national project. L-band ground-based SAR, which has a bandwidth of 20 MHz at 1.27 GHz, was developed and tested in [28; 29]. A UAV with onboard X-band SAR was investigated and validated in this study.  
The range resolution of SAR is determined by the bandwidth of the waveform. We generated a wide bandwidth waveform using an improved MDDS and demonstrated that the proposed method can decrease phase error and improve spectrum quality. We developed a waveform generator in accordance with the proposed structure. Simulation results and hardware outputs were presented for the procedure and compared to verify the developed system. The phase error produced in an ideal waveform was divided into 1-\(\upmu\)s units. We selected specific sections that have large phase errors for the pre-distortion. However, there was a limit to dividing the number of sections because of a trade-off between compensation gain and hardware efficiency. For future work, we will increase the compensation gain and eliminate the vibration of the hardware to increase spectrum quality.  
Conceptualization, J.-H.K.; Data curation, K.-R.K.; Formal analysis, K.-R.K.; Funding acquisition, J.-H.K.; Investigation, K.-R.K.; Methodology, K.-R.K.; Project administration, J.-H.K.; Resources, J.-H.K.; Software, K.-R.K.; Supervision, J.-H.K.; Validation, K.-R.K.; Visualizing, K.-R.K.; Writing--original draft, K.-R.K.; Writing--review and editing, J.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and ICT, Korea, under the ITRC support program (2020-2018-0-01424) supervised by the IITP.  
The authors declare no conflict of interest.
## 4 Experimental Results and Analysis  
### Data  
To verify the effectiveness of the proposed method, we selected two datasets with relatively complete water use statistics in Xiuzhou District of Jiaxing City and Zhuji City for experimental analysis. The statistical information on the water use of enterprises in these regions comes from the water works of each city, and the obtained statistical information of the water use of enterprises includes the names of enterprises and the categories and sub-categories of enterprises' national economic industries. We obtained water use statistics for 3115 enterprises in Xiuzhou District of Jiaxing City and 2140 enterprises in Zhuji City in January 2024. Table 1 shows the details of the two datasets.  
Table 2 shows some enterprise data from the two datasets. We conducted data cleaning on the original enterprise data, removing enterprises with a sample size of less than three in each industry minor category to ensure that each category has sufficient training samples. After data cleaning, the enterprises in Xiuzhou District of Jiaxing City were divided into three major categories and 31 minor categories according to the classification of national economic industries, among which the textile industry and the textile and garment industry are the main types of enterprises, accounting for 31.7% of the total. The enterprises in Zhuji City are divided into 13 major categories and 38 minor categories according to the classification of national economic industries, of which the wholesale industry, retail industry, real estate industry, and textile and clothing industry are the main types of enterprises, accounting for 43.25% of the total. It can be seen from the data statistics that the industrial structure of the two regions is quite different, and there are many types of enterprises in Zhuji City that are difficult to classify. During the experiment, 80% of the total number of enterprises were randomly selected as the training data set, 10% as the test data set, and 10% as the verification data set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\# of Enterprise** & **\# of Major Categories** & **\# of Minor Categories** \\ \hline Xiuzhou & 3115 & 3 & 31 \\ Zhuji & 2140 & 13 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enterprise Name** & **Region** & **Major Categories** & **Minor Categories** \\ \hline Wanglianging Greenland Flower and Tree & & & 21 \\ Horticultural Field, Jiaxing City, China & & & \\ Zhejiang Nantong hosiery Co., Ltd., & & & \\ Nantong, China & & & \\ \hline Jiaxing Xincheng Oil Factory, & & & 13 \\ Jiaxing, China & & & \\ Zhejiang Canal Bay Agricultural & & & \\ Technology Co., Ltd., Jiaxing, China & & & \\ \hline Jiaxing Dingcheng Ecological Agriculture & & & \\ Co., Ltd., Jiaxing, China & & & \\ \hline Jiaxing Gas Group Co., Ltd., Jiaxing, China & & & \\ \hline Jiaxing Xiuzhou District Lotus Sewage & & & \\ Treatment Plant, Jiaxing, China & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Partial enterprise data.