esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Volatile Flavor Compounds in Seven Mustard Pastes via HS-SPME-GC-MS
###### Abstract  
To identify the volatile flavor components in Mustard paste (MP), the volatile compounds in seven MPs available on the market were isolated and analyzed by headspace solid-phase microextraction combined with gas chromatography-mass spectrometry. A total of 27 volatile constituents were found by mass spectra and retention index compared to the data obtained from reference compounds or the related literature and databases; these compounds included nine esters, three sulfur-containing compounds, two nitriles, three ketones, three alkenes, and seven other compounds. Of the 27 compounds, 6 compounds came from the turmeric added to MPs. Among the components detected, some compounds derived from AITC were allyl thiocyanate, carbon disulfide, allyl mercaptan, diallyl sulfide, and diallyl disulfide. The results obtained provide a better and comprehensive recognition of the volatile flavor compounds in MPs, and have some reference values for developing and applying isothiamate compounds.  
m m t 2023 2  
chromatography-mass spectrometry (GC-MS) [9]. In addition, most of the volatiles were characterized tentatively by mass spectrometry only, and the relative content was given solely through a normalization method.  
The current study aimed to isolate, identify, and compare the volatile compounds present in commercially available MP products and investigate the ITC flavor compounds and their decomposition products. Therefore, the volatiles in seven commercially available MP products were extracted and identified by HS-SPME combined with GC-MS. The obtained results have the potential to provide a theoretical foundation for developing the flavor compounds and food flavorings with mustard notes.
## 5 Regional Division and Analysis
### Computational Overhead  
In the traditional accounting process, all transactions in the network are verified by the node with the right of accounting alone, which will consume considerable computing power and time for a single node. In this paper, multiple satellite nodes are divided into the same region to share the total computing overhead in the region. When a satellite node receives a transaction, it does not broadcast it but stores it temporarily in the local cache pool. Each region takes turns in bookkeeping. When the region obtains the right of bookkeeping, the master node does not need to verify all transactions in the region alone. Instead, each node in the region first validates the transaction they receive independently. Then, the node forms the transaction tree that passes the verification and calculates the root hash. Finally, the node sends the transaction tree and root hash to the master node. After receiving all transaction trees in the region, the master node forms a larger transaction tree and calculates the final root hash. In this process, each subtree is calculated separately by each satellite node; thus, the calculation in the region is dispersed to each node. Therefore, the master node does not need much calculation, and the partition method reduces the calculation overhead and time of the master node when verifying transactions and calculating hashing.
## 3 Proposed FDTE with Iterative Channel Estimation  
Here, a SIMO IFDTE scheme with low complexity iterative channel estimation is proposed. First, the sparse channel estimation is designed. Second, the proposed IFDTE processes the received symbols in terms of the above estimated CSI. The combination of bidirectional extrinsic information is adopted to accelerate the equalization convergence. The a posteriori soft decision promotes the performance for channel estimation. Finally, the flow chart of the entire scheme with complexity analyses are presented to enhance understanding.  
Traditional turbo equalization mainly exploits the MMSE criterion for independent Gaussian distribution (_i.i.d_ ) symbols. The distribution characteristics are usually ignored, easily resulting in equalization performance being degraded. An EP equalization based on simple Gaussian distribution is proposed to approximate the actual posterior distribution of the transmission symbol through moment matching, so this scheme is used to effectively improve the equalization.  
The entire turbo equalization can be described as follows. First, adaptive channel estimation is adopted to estimate the CSI in terms of training sequences and is fed back to the turbo equalization in the frequency domain. Second, the moment of the a posteriori distribution of the transmitted codeword is approximately matched through the frequency domain EP turbo equalization in terms of the channel state and a priori decoding information \(L_{D}^{a}\). Finally, the a posteriori information calculated by the a posteriori probability is adopted after the self-iteration based on the EP. This information is used to calculate the extrinsic information \(L_{E}^{e}\) of the equalization, and \(L_{E}^{e}\) is fed back to the decoder. If the maximum turbo iteration is not reached, the \(L_{D}^{e}\) of the decoder is outputted. Otherwise, the final results of the codeword is outputted. The block diagram of the FDTE with the EP in a SIMO UWAC system is designed and shown in Figure 2.
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 4 Discussion  
### Uncertainty in Carbon Dynamics in Central Asia  
The Biome-BGC model has been used to assess the responses of carbon dynamics to climate change in Central Asia [14] and China [19]. However, only one climate dataset was used in these studies. The current paper presents the application of multiple input datasets to analyze the carbon dynamics of Central Asian pasturelands, giving uncertainty in modeling.  
Modeling can be an effective tool to address experiments' limitations and to depict the response mechanism of plants to environments; however, researchers demonstrate the need to systematically investigate the uncertainty resulted from modeling. One of the main uncertainties came from forcing datasets themselves, such as from meteorology [6; 27; 28]. Using multiple data sources can give the magnitude and range of carbon flux, which can ultimately help to reduce the errors in future modeling.  
The model simulated that pasturelands in Central Asia released 2.10 \(\pm\) 1.60 Pg C, and the NEE range from three input climate datasets was \(-\)1.53\(\sim\)\(-\)2.35 Pg C in the past 34 years. Our results also show that the annual NEE of Central Asia generally had an increasing trend from 1981-2014, which is in line with some previous studies [13; 14; 29; 30]. Our results indicate that pasturelands in Central Asia play an important role in the global carbon cycle. However, a weak carbon source was observed in this region, which can be attributed to two reasons. Firstly, high grazing intensity had been sustained especially in Kazakhstan [31; 32; 33], although the situation was relieved since the disintegration of the Soviet Union in 1991-1992. Secondly, a significant positive trend in temperature (0.03 \(\pm\) 0.01 \({}^{\circ}\)C year\({}^{-1}\)) and a negative trend in precipitation (2.18 \(\pm\) 2.04 mm year\({}^{-1}\)) may lower the gross primary productivity and enhance the respiration [34; 35; 36; 37].
## 4 The Proposed Mechanisms
### Implementation Details  
With ReCIF and ReCIF+PIF, nodes need to know the number of hops traversed by a packet. The nodes running our three mechanisms also need to be aware of their centrality degree. For these purposes, we added two fields to the interest packet header: FWD_counter and node_ID. Table 1 summarizes the fields added to the interest packet header.  
Each time the packet is forwarded, the FWD_counter field is incremented by one. The purpose of this field is to compute how many retransmissions a given packet has passed through, i.e., the number of hops traversed by the packet. There have been several proposals for neighbor discovery mechanisms in wireless networks [36; 37]. For simplicity, we added a field to the interest packet header for this purpose. The node_ID receives the identifier of the last node that forwarded the interest packet. The node only puts its identification when it forwards the interest. Therefore the other nodes, upon receiving the interest, can count the different node_ID to obtain an estimate of neighboring nodes. The node_ID field is only used for neighbor discovery, not for forwarding purposes. Therefore, we do not violate the NDN paradigm.  
Figure 4 explains how a node calculates its centrality threshold:  
1. After the arrival of the interest packet, the node checks its neighbor list size.
2. If the list of neighbors, called _neighbor.LIST_, is empty, the node adds the tuple node_ID, obtained from the interest packet header, and the interest packet arrival time, _pckt.TIME_ to the list. After that, the neighbors list size _neighbor.LIST.Size_ is incremented.
3. If the list of neighbors is not empty, the node ID extracted from the packet is compared with IDs stored on the _neighbor.LIST_. If the ID is not found in the neighbor list, then the ID and the packet arrival time are added to the list, as previously described. Otherwise, if the ID is found on the list, then the packet arrival time is updated (_pckt.TIME_). A timeout is defined for each entry. If the timeout expires, the tuple is removed from the list, and the list size is updated. The timeout is used to remove nodes from the list that may be off because of some failure or administrator change. The amount of time used to set the expiration time might change to cover the specific network flexibility. For our work, that time was set to the time used in the simulation, since our nodes were fixed and the environment was controlled regarding node failures.
4. The last step is to check if any tuple of the neighbor list has a packet time higher than the expiration time. In such cases, the tuple will be removed, and the neighbor list size will be decremented.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interest Packet Field** & **Description** \\ \hline FWD\_counter & Incremented at each packet forwarding. \\ \hline node\_ID & Receives the identifier of the last forwarding node, \\  & modified at each hop. \\ \hline \hline \end{tabular}
\end{table}
Table 1: New fields added to the interest packet header.
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 4 Conclusions  
This study presents the yearly evolution of bridges, especially PSC I-girder bridges in Korea and investigates analytically and experimentally the performance of the new PSC I-girder bridge to be applied as railway bridges. The following conclusions can be drawn.  
1. Natural frequency analysis for the new PSC I-girder range from a span of 30 to 45 m revealed that their first and second mode occur very close, either in bending or torsion mode alternately.
2. Subsequent moving load analysis was conducted considering 5 types of trains in Korea. The new type of PSC I girder is likely to amplify the dynamic response due to the proximity of the first two modes, but it was found that it satisfied sufficiently all the design limits related to the acceleration, deflection and twist.
3. Experimental verification of natural frequency was carried out on a full-scale specimen with span length of 35 m. The natural frequency (4.2 Hz) of the specimen extracted from the dynamic test results was in good agreement with the value (4.27 Hz) obtained analytically and validated the dynamic analysis model established in this study.
4. The performance of the specimen observed in the static test secured sufficient safety through the comparison of the load and deflection corresponding to the service and ultimate limit states of the design code. Accordingly, the new PSC I-girder presented in this study develops the dynamic performance and load bearing capacity required for its application in railway bridges and can be applied in new constructions under plan in Korea.  
[PERSON] conceived the main idea and prepared this article as the first author; [PERSON] reviewed the dynamic response. [PERSON], [PERSON] and [PERSON] designed and performed the experiment. [PERSON] revised and finalized the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by \"Development of Detection Technology for Internal Void of Tendon Duct of PSC Bridge (20200598-001)\" and \"A Performance Evaluation on the Segmental BH Girder System (20170442-001)\" funded by Korea Institute of Civil engineering and building Technology (KICT), Korea.  
The authors declare no conflict of interest.
## 3 Results
### Verification
#### 3.1.2 Time Independence  
In consideration of convergence of the calculation process, the transient computation was used to approximate the steady state. Therefore, the sample convergence trends were documented for critically important variables. As can be seen in Figure 3, the inlet and outlet flow rate of water under four conditions in Table 1 all converged to a constant after about 150 s, and the air velocity of three air ducts converged to a constant after about 80 s. Accordingly, the analysis in this paper was based on these convergent values.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Air Duct No. 1**} & \multicolumn{2}{c}{**Air Duct No. 2**} & \multicolumn{2}{c}{**Air Duct No. 3**} \\ \hline \multirow{3}{*}{Grid parameter} & \multicolumn{5}{c}{Cell counts: 323 w; 203 w; 72 w} \\  & \multicolumn{5}{c}{Equivalent size _h_: 0.5947; 0.6940; 0.9816} \\ \cline{2-7}  & \multicolumn{5}{c}{\(r_{21}=1.1669;r_{32}=1.4145\)} \\ \cline{2-7} Air velocity (m/s) & 69.12 & 70.37 & 77.97 & 66.54 & 67.53 & 73.31 & 57.78 & 58.37 & 63.36 \\ \hline \(\varphi_{\text{ext}}\) & 67.5259 & & 65.2104 & & 57.2601 & \\ \(e_{\text{a}}\) & 1.81\% & & 1.49\% & & 1.02\% & \\ \(e_{\text{ext}}\) & 2.36\% & & 2.04\% & & 0.91\% & \\ \(\text{GCI}_{\text{fine}}\) & 2.88\% & & 2.50\% & & 1.12\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examination of grid convergence.  
Figure 3: The time independence of documented value: (**a**) flow rate of inlet and outlet; (**b**) air demand.
## 2 Materials and Methods
### Gene Annotation, Differential Expression Analysis, and Evaluation of Enrichment  
The annotations of gene functions were derived from the Gene Ontology (GO) ([[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/)), accessed on 1 July 2022) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) ([[http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)]([http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)), accessed on 30 June 2023) databases.  
The Fastp v0.23.2 tool was utilized for filtering downstream data and producing clean reads. For the de novo assembly of clean reads, Trinity v2.13.2 software was employed using the paired-end approach, with the longest transcript designated as the unigene, while Corset v1.09 software eliminated redundant sequences to yield the final reference sequence. The bowtie2 v2.4.5 program was used to assess gene alignments, and the expression levels of genes or transcripts were quantified with FPKM. The analysis of differential expression between the two datasets was performed using DESeq2 v1.22.2, with \(p\)-values adjusted for multiple comparisons according to the Benjamini and Hochberg method.  
Differentially expressed genes (DEGs) were filtered with the criteria of \(\mid\)log\({}_{2}\)Fold Change \(\mid\)\(\geq\) 1 and FDR \(<\) 0.05. To explore gene expression patterns, hierarchical clustering analysis was performed on the DEGs. Enrichment analyses for GO and KEGG pathways related to DEGs were conducted using R software version 3.6.2 and methods based on hypergeometric distribution.
## 3 Proposed Method
### Multi-Scale Feature Encoding Structure  
In order to extract the essential features of different objects in remote sensing images, we design a feature encoding structure consisting of thirteen convolution layers and five pooling layers, as shown in Figure 3. The convolution kernels of the convolution layers conv1_1, conv1_2 to conv5_3 have the kernel size of \(3\times 3\). The number of convolution operation channels is shown in Figure 3. In the first convolution layer, the input color image is convoluted by the convolution kernel, whose number of channels is 64 and kernel size is \(3\times 3\). The shape of the output feature map is \(64\times 256\times 256\). After five stages of convolution and pooling operations, features of different scales can be obtained.  
The activation function converts the linear mapping of the convolution layer into a non-linear mapping. The activation function used in this paper is ReLU. ReLU has the characteristics of simple calculation and stable gradient value, which can effectively solve the problem of too small gradient or gradient explosion.  
There are five pooling layers in the encoding structure, such as MaxPool1, MaxPool2 to MaxPool5. The pooling layer adopts the max-pooling mode, and the size of the pooling kernel is \(2\times 2\). During the pooling process, the maximum index position of each pooling operation is recorded and released during the decoding process, so that the location information of the object can be retained. The pooling layer can compress the feature map, and reduce the number of network parameters, the calculation time and space complexity. Feature compression can extract key information in the image, which can improve classification performance. During the convolution and pooling operations, if the size of the feature map is halved, the number of channels of the convolution kernel is doubled to obtain a richer feature of the image.
# Steady State Response of Linear Time Invariant Systems Modeledby Multibond Graphs
## Appendix A Proof of Lemma 1  
From the second and third lines of (1) with (4) and (5):  
\[\Big{(}\mathbf{I}-\mathbf{S}_{11}^{22}\mathbf{X}_{GY}\Big{)}\underline{I_{GY}}(t )=\mathbf{S}_{11}^{21}\underline{z}(t)+\mathbf{S}_{12}^{21}\underline{L} \underline{D_{in}}(t)+\mathbf{S}_{13}^{21}\underline{u}(t) \tag{11}\]  
\[(\mathbf{I}-\mathbf{S}_{22}\mathbf{L})\underline{D_{in}}(t)=\mathbf{S}_{21}^{11 }\underline{z}(t)+\mathbf{S}_{21}^{22}\mathbf{X}_{GY}\underline{I_{GY}}(t)+ \mathbf{S}_{23}\underline{u}(t) \tag{12}\]  
Substituting (12) into (11):  
\[\Big{(}\mathbf{I}-\mathbf{S}_{11}^{22}\mathbf{X}_{GY}\Big{)} \underline{I_{GY}}(t) = \mathbf{S}_{12}^{21}\mathbf{L}(\mathbf{I}-\mathbf{S}_{22}\mathbf{ L})^{-1}\Big{[}\mathbf{S}_{21}^{11}\underline{z}(t)+\mathbf{S}_{21}^{12} \mathbf{X}_{GY}\underline{I_{GY}}(t)+\mathbf{S}_{23}\underline{u}(t)\Big{]} \tag{13}\] \[+\mathbf{S}_{13}^{21}\underline{u}(t)+\mathbf{S}_{11}^{21} \underline{z}(t)\]  
and substituting (11) into (12) and (12) into (11):  
\[(\mathbf{I}-\mathbf{S}_{22}\mathbf{L})\underline{D_{in}}(t) = \mathbf{S}_{21}^{12}\mathbf{X}_{GY}\Big{(}\mathbf{I}-\mathbf{S}_{ 21}^{22}\mathbf{X}_{GY}\Big{)}^{-1}\Big{[}\mathbf{S}_{11}^{21}\underline{z}(t )+\mathbf{S}_{12}^{21}\underline{L}\underline{D_{in}}(t)+\mathbf{S}_{13}^{21} \underline{u}(t)\Big{]} \tag{14}\] \[+\mathbf{S}_{23}\underline{u}(t)+\mathbf{S}_{21}^{11}\underline{z }(t)\]  
It is assumed that \(\mathbf{M}_{L}\) and \(\mathbf{M}_{X}\) can be obtained. This is true since \(\mathbf{S}_{22}\), \(\mathbf{S}_{11}^{22}\), and \(\mathbf{X}_{GY}\) are skew-symmetric matrices and \(\mathbf{L}\) is a diagonal and constant matrix, then the algebraic sum of an identity matrix and a skew-symmetric matrix is invertible. From (14) and (15), (13) and (14) can be written as:  
\[\underline{I_{GY}}(t) = \Big{(}\mathbf{I}-\mathbf{S}_{11}^{22}\mathbf{X}_{GY}-\mathbf{S}_ {12}^{21}\mathbf{M}_{L}\mathbf{S}_{21}^{12}\mathbf{X}_{GY}\Big{)}^{-1}\Big{[} \Big{(}\mathbf{S}_{11}^{21}+\mathbf{S}_{12}^{21}\mathbf{M}_{L}\mathbf{S}_{21} ^{11}\Big{)}\underline{z}(t) \tag{15}\] \[+\Big{(}\mathbf{S}_{13}^{21}+\mathbf{S}_{12}^{21}\mathbf{M}_{L} \mathbf{S}_{23}\Big{)}\underline{u}(t)\Big{]}\]  
\[\underline{D_{in}}(t) = \Big{(}\mathbf{I}-\mathbf{S}_{22}\mathbf{L}-\mathbf{S}_{21}^{12} \mathbf{M}_{X}\mathbf{S}_{12}^{21}\mathbf{L}\Big{)}^{-1}\Big{[}\Big{(} \mathbf{S}_{21}^{11}+\mathbf{S}_{21}^{12}\mathbf{M}_{X}\mathbf{S}_{11}^{21} \Big{)}\underline{z}(t) \tag{16}\] \[+\Big{(}\mathbf{S}_{23}+\mathbf{S}_{21}^{12}\mathbf{M}_{L} \mathbf{S}_{13}^{21}\Big{)}\underline{u}(t)\Big{]}\]  
In the same way, \(\big{(}\mathbf{I}-\mathbf{S}_{11}^{22}\mathbf{X}_{GY}-\mathbf{S}_{12}^{21} \mathbf{M}_{L}\mathbf{S}_{21}^{12}\mathbf{X}_{GY}\big{)}\) and \(\Big{(}\mathbf{I}-\mathbf{S}_{22}\mathbf{L}-\mathbf{S}_{21}^{12}\mathbf{M}_{X} \mathbf{S}_{12}^{21}\mathbf{L}\Big{)}\) can be invertible.
## 4 Automatic Within-Show Diarization Baseline Systems
### Within-Show Baseline Performance  
For each system and data set (Dev and Eval), performance of within-show diarization is given in Table 4. For each system, all parameters and thresholds are optimized to minimize DER on the development set and then used on the evaluation set.  
From this table, it is noticeable that the _ResDS_ system strongly out-performs the _SincDS_ system in terms of within-show DER. This is due to the highest quality of its speaker representations (\(x\)-vectors). Using an automatic segmentation degrades the within-show DER for all  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Segmentation**} & \multicolumn{2}{c}{**Within-Show DER**} \\ \cline{3-4}  & & **Dev** & **Eval** \\ \hline \multirow{3}{*}{_SincDS_} & Ref & 17.77 & 13.38 \\ \cline{2-4}  & VAD & 19.07 & 20.20 \\ \hline \multirow{3}{*}{_ResDS_} & Ref & 14.12 & 10.63 \\ \cline{2-4}  & VAD & 14.97 & 16.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the two baseline within-show diarization systems on both Development (Dev) and Evaluation sets (Eval) when using the reference segmentation (Ref) or an automatic segmentation (VAD). The performance is given as a weighed average of within-show Diarization Error Rate (DER). DER is computed for each show and weighted according to the duration of the shows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Layer Name**} & \multicolumn{1}{c}{**Structure**} & \multicolumn{1}{c}{**Output (\(C\times F\times T\))**} \\ \hline Input & - & \(1\times 80\times T\) \\ Conv2d & \(3\times 3\), stride = 1 & \(32\times 80\times T\) \\ \hline \multirow{3}{*}{ResBlock-1} & \(\begin{bmatrix}3\times 3,\ 32\\ 3\times 3,\ 32\\ SE Layer \\ \end{bmatrix}\times 3\), stride = 1 & \(32\times 80\times T\) \\ \hline \multirow{3}{*}{ResBlock-2} & \(\begin{bmatrix}3\times 3,\ 64\\ 3\times 3,\ 64\\ SE Layer \\ \end{bmatrix}\times 4\), stride = 2 & \(64\times 40\times T/2\) \\ \hline \multirow{3}{*}{ResBlock-3} & \(\begin{bmatrix}3\times 3,\ 128\\ 3\times 3,\ 128\\ SE Layer \\ \end{bmatrix}\times 6\), stride = 2 & \(128\times 20\times T/4\) \\ \hline \multirow{3}{*}{ResBlock-4} & \(\begin{bmatrix}3\times 3,\ 256\\ 3\times 3,\ 256\\ SE Layer \\ \end{bmatrix}\times 3\), stride = 2 & \(256\times 10\times T/8\) \\ \hline \multirow{3}{*}{Flatten} & - & \multirow{3}{*}{-} & \multirow{3}{*}{5120} \\ \cline{1-1} \cline{3-3}  & & & \\ \cline{1-1}  
systems but this degradation is more important on the evaluation set than on the development set. For instance, _ResDS_ within-show DER increases by a relative 6% on the Dev set but 57% on the Eval set. We observe that our two baseline systems perform better on _Eval_ than on _Dev_ when using the Reference segmentation but that they face strong degradation when using an automatic segmentation. This can be due to an over optimization of the system hyper parameters on the _Dev_ set when using an automatic segmentation or to a mismatch between _Dev_ and _Eval_ sets but we do not have any clue of that.