esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Volatile Compounds
#### 3.2.2 Fermentation Compounds
The harvesting time had a significant effect (Table 2) on most non-phenolic (11 out of 13) and some phenolic (two out of five) fermentation compounds included in Table 4. Most of the non-phenolic compounds (all except 2-methylbutanal and 2-methylpropanoic acid) were affected by the harvesting time (Table 2). These compounds tended to fluctuate from week to week, although a general increase in ethanol, 2-methylpropanal and 3-methylbutanal was found. For most compounds the highest values tended to appear in weeks 3 and 4. The increase in ethanol over the harvesting season matches a rise in this compound found throughout fruit ripening [13]. This compound, which arises from fruit sugar fermentation and has been proposed as a marker of oil deterioration [13], has been related to the winey-vinegary defect [31]. Likewise, the branched aldehydes increased over time (Table 4). Although no increase was found in Hoijblanca and Picual oils [13], an increase in these undesirable compounds during fruit storage has been reported [4]. These compounds generally possess low odour-thresholds [3] and, in fact, they and their corresponding alcohols and acids are related to the fusty defect [10]. With regard to the volatile phenols included in Table 4, only 2-phenylethanol and 2-ethylphenol were affected according to the the ANOVA results (Table 2), with significant fluctuations over the six weeks consisting of an increase in week 3 and a subsequent decrease (Table 4). To our knowledge no information about the effect of either the harvesting time or fruit ripening on the volatile phenols of olive oil is available. As mentioned above, volatile phenols are related to oil degradation [4]. It was suggested that its formation may depend on the resistance of olives to microbial decay, and they have been related to the free acidity values [35]. In our study there was not a clear change in the quality parameters over time (Table 1) and the only parameter affected (PV) also showed fluctuations instead of a steady increase. Therefore, results for the volatile phenols (Table 4), which fit those for the quality parameters, could confirm that there was not a clear quality loss as the harvesting season elapsed.
## 2 Results  
### Characterization of UK114 from Capra hircus (Gont)  
Like most members of the Rid protein family, UK114 is a small protein of 137 amino acids (predicted mass 14,298.44) that assembles to form a homotrimer [42]. Remarkably, it lacks tryptophan and histidine residues. The only cysteine residue present is not engaged in the formation of disulfide bonds. UK114 shares the highest homology with the RidA proteins from sheep (_Ovis aries_) and from the domestic cow _Bos taurus_ (99% and 98% identity, respectively), _Homo sapiens_ (85%), _Rattus norvegicus_ (82%), _Mus musculus_ (81%), and rabbit (_Oryctolagus cuniculus_, 81%). The amino acid identity decreases to 76%, 65%, and 51% with the homologs from zebrafish (_Danio rerio_), _Caenorhabditis elegans_, and _Drosophila melanogaster_, respectively. The amino acid identity with the RidA protein homolog from the model plant _Arabidopsis thaliana_ is 42% and with Der f 34 from the house dust mite _D Dermatophozoites furinine_ is 47%. YIL051c/_MMF1_ and YER057c/_HMF1_, the two paralogs from the baker's yeast _Saccharomyces cerevisiae_ share a slightly lower but significant identity with UK114 (42% and 38%, respectively). Because of the sequencing of many genomes, hundreds of sequences of Rid proteins from Eubacteria and Archaea are available and the extent of amino acid identity varies. The identity between UK114 and the RidA of _S. typhimurium_ or of _E. coli_ (TdcF), taken as the reference of the bacterial RidA, is 41%. The multiple alignment in Figure 2 shows the blocks of the most conserved amino acids in UK114 and other representatives of the RidA protein family. Extra N-terminal sequences are present in the mitochondrial RidA encoded by the yeast homolog YIL051c/_MMF1_, and in the RidA from _Arabidopsis thaliana_, which is a nuclear-encoded chloroplast enzyme, in agreement with the presence of import signals into the mitochondrion and into the chloroplast, respectively. A recent evolutionary analysis based on genomic data identified the signature pattern of conserved amino acids that are shared by the members of the RidA subfamily, whose reference protein is _E. coli_ TdcF [6]. As shown in Figure 2, the Tyr17, Ser30, Asn88, Arg105, and Glu120 residues found in the active site of _E. coli_ TdcF have an equivalent counterpart in residues Tyr21, Ser34, Asn93, Arg107, and Glu122 of UK114. TdcF Cys107, which is not conserved in all of the homologs, is replaced by Ala109 in UK114. Furthermore, amino acid residues that prevent the oligomeric assembly of monomeric UK114 in trimeric structures are the conserved residues Pro105, Arg107, Gly35, and Tyr21 of UK114 [19; 43].
## 4 Discussion
To the best of our knowledge, most existing works generally switched among different algorithms rather than fusing them. Our rich experimental results demonstrate the effectiveness of the AFM in improving the heading accuracy. For magnetic disturbance detection, most existing works introduced a binary detector (i.e., yes or no), while a detector that recognizes magnetic fields of different qualities was proposed in our framework to improve the robustness of the magnetic-field-based algorithm. Moreover, our proposed framework can easily integrate different heading error constraint modules by changing the heading error measurement instead of constructing a complicated measurement transition matrix. Thus, it is more flexible than previous methods.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 4 Joint Iterative Detection and SCL Decoding Receiver
### Updated a Priori Information of MPA Detector
\right.. \tag{13}\]  
From Figure 7, the messages of each node are updated on a PE by the following recursive formulas:  
\[L(i,j)=f(L(2i,j+1)+R(i+N_{m}/2,j),L(2i-1,j+1)), \tag{14}\]  
\[L(i+N_{m}/2,j)=L(2i,j+1)+f(R(i,j),L(2i-1,j+1)), \tag{15}\]  
Figure 7: Factor graph and processing element. (**a**) Factor graph; (**b**) processing element. \[R(2i-1,j+1)=f(L(2i,j+1)+R(i+N_{m}/2,j),R(i,j)), \tag{16}\] \[R(2i,j+1)=L(i+N_{m}/2,j)+f(R(i,j),L(2i-1,j+1)), \tag{17}\]  
where \(f(\cdot)\) is defined as  
\[f(a,b)=2\text{arctanh}\left[\tanh\left(\frac{a}{2}\right)\cdot\tanh\left(\frac{ b}{2}\right)\right]. \tag{18}\]  
For the details of the SCAN algorithm, we refer to [13]. Then, following [17], after one iteration of SCAN decoding, the extrinsic messages of mother code can be expressed as  
\[L_{x,k}^{v,j}=R(i,n+1)+\alpha\frac{\overline{R}_{n+1}}{\overline{L}_{n+1}}L(i, n+1), \tag{19}\]  
where \(\overline{R}_{n+1}\) and \(\overline{L}_{n+1}\) are the average values of \(R(i,n+1)\) and \(L(i,n+1)\), respectively. \(\alpha\) is the weight factor. Finally, by the coding rules of PIC PCCA-polar code, the extrinsic messages of mother code \(L_{x,k}^{v}\) is punctured or shortened to \(L_{e,k}^{v}=\left\{L_{e,k}^{v,1},\ldots,L_{e,k}^{v,N}\right\}\). The flowchart of hybrid construction algorithm is given by Figure 8.  
Therefore, the extrinsic message of user \(v\) can be represent by \(L_{e}^{v}=\left\{L_{e,1}^{v},\ldots,L_{e,k}^{v},\ldots L_{e,l_{e}}^{v}\right\}\). Then, \(L_{e}^{v}\) is interleaved to \(L_{a}^{v}=\left\{L_{a,1}^{v},\ldots,L_{a,k}^{v},\ldots,L_{a,l_{e}}^{v}\right\}\) by \(L_{a,k}^{v}=\Pi\left(L_{e,k}^{v}\right)\) and transformed into the information of probability domain. Finally, it is re-mapped to the symbol message as the a priori information of the MPA detector. \[P\left(s_{v}^{l}\right)=\prod_{m=1}^{Q}\frac{\left(L_{a}^{v,(l-1)Q+m}\right)^ {1-q_{v}^{m}}}{1+L_{a}^{v,(l-1)Q+m}}, \tag{20}\]  
where \(q_{v}^{m}\in\{0,1\}\).
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 2 Methods  
### Overview of Regional Geology  
The selected logging interval is from the Early Triassic Jialingjiang Formation in the Moxi area of the Sichuan Basin, People's Republic of China. The Sichuan Basin is one of China's largest hydrocarbon-bearing basins, having undergone three major tectonic evolutionary stages. As shown in Figure 1, from the Ediacaran to the Late Triassic period, it was characterized by a marine carbonate platform [16]. The basin is bordered by the Longmen Mountains to the west, the Daba Mountains to the north, and the Kangdian Uplift to the south. The Moxi Gas Field is located in the southern part of the gentle Zhongchang uplift structural belt in the central Sichuan Basin. Geographically, it lies approximately 30 km south of Suining City in Sichuan Province, situated in the southern section of the central Sichuan oil and gas region. During the Early Triassic Jialingjiang period, the Upper Yangtze Craton marine basin was influenced by the uplift of surrounding paleo-uplifts and fluctuations in sea level. Due to the constraints of surrounding paleo-uplifts and periodic global climate changes, the Sichuan Sea underwent three cycles of regression-stagnant water evaporative salt basin-transgression during the second substage of the Jialingjiang period. Under the overall regressive background of this stage, the environment evolved into a restricted marine setting [17].  
The Moxi Gas Field is a typical strongly heterogeneous carbonate gas reservoir, with proven natural gas reserves exceeding 326.59 billion cubic meters. As shown in Table 1 and Figure 1, the Jialingjiang Formation is subdivided into five members from bottom to top (Tlj1 to Tlj5). Members Tlj4 and Tlj5 are primarily composed of anhydrite and dolomite, while Tlj1 and Tlj3 consist mainly of limestone. During the Tlj2 depositional period, the sedimentary system was a carbonate platform on an epiciric sea, characterized by restricted-evaporative platform deposits. The reservoir types are predominantly porous with locally developed fractures and display significant secondary alterations, diverse reservoir types, and strong heterogeneity. Therefore, further analysis of the porosity in carbonate reservoirs with complex pore structures serves as an important reference for evaluating this gas reservoir [18; 19].  
\begin{table}
\begin{tabular}{c c c} \hline
**Fm.** & **Member** & **Lithological Characteristics** \\ \hline Tlj5 & / & Upper part: anhydrite rock, lower part: dolomite \\ Tlj4 & / & Anhydrite rock interbedded with halite and dolomite \\ Tlj3 & / & Limestone \\  & Tlj23 & Limestone, dolomite, and anhydrite rock \\  & Tlj22C & Upper part: anhydrite rock, middle and lower parts: dolomite \\ Tlj2 & Tlj22B & Upper part: anhydrite rock, middle part: dolomite, lower part: limestone \\  & Tlj22A & Mudstone and shale interbedded with limestone \\  & Tlj21 & Upper part: anhydrite rock, lower part: limestone and dolomite \\ Tlj1 & / & Limestone \\ \hline \end{tabular}
\end{table}
Table 1: Abridged stratigraphic table of the Jialingjiang Formation in the Moxi Structure.
## 6 Limitations  
Despite the promising results demonstrated in this study, several limitations must be addressed to verify the system's applicability and performance in real-world urban environments. Firstly, the small-scale deployment, with only three sensor nodes and two relay nodes, is implemented as a proof of concept, and larger urban setups are needed, such as increased net-work traffic and interference, to confirm the system performance. The system's scalability and robustness under more complex environmental conditions have to be tested. Secondly, battery life limits long-term monitoring, with relay and sensor nodes operating for only 8.5 h and 15 days, respectively. Better power management and alternative energy sources, like solar panels, are needed to extend monitoring duration. Thirdly, work should include sensor calibration to ensure reliable data. Additionally, while no packet loss occurred during a 15-minute test, longer-term testing under various environmental conditions is necessary to assess network stability, as factors like weather and interference could affect performance. Lastly, the classification system primarily identifies the dominant sound type, potentially treating low-power valuable information as noise and disregarding it. Incorporating multi-label classification and estimating the contributions of different sound sources to the total SPL from mixed audio segments would enhance the system's practical applicability. Future work will address the above challenges to enhance the system's scalability and robustness in larger urban environments.
###### Abstract  
In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emotional recognition based on EEG have arisen. In this paper, we propose a novel deep learning model-based emotion recognition method. First, the EEG signal is spatially filtered by using the common spatial pattern (CSP), and the filtered signal is converted into a time-frequency map by continuous wavelet transform (CWT). This is used as the input data of the network; then the feature extraction and classification are performed by the deep learning model. We called this model CNN-BiLSTM-MHSA, which consists of a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and multi-head self-attention (MHSA). This network is capable of learning the time series and spatial information of EEG emotion signals in depth, smoothing EEG signals and extracting deep features with CNN, learning emotion information of future and past time series with BiLSTM, and improving recognition accuracy with MHSA by reassigning weights to emotion features. Finally, we conducted experiments on the DEAP dataset for sentiment classification, and the experimental results showed that the method has better results than the existing classification. The accuracy of high and low valence, arousal, dominance, and liking state recognition is 98.10%, and the accuracy of four classifications of high and low valence-arousal recognition is 89.33%.  
EEG; emotion recognition; CNN; BiLSTM; multi-head self-attention; time-frequency map 2022
## 1 Introduction
(2) The relationship between the PH curve and the prescribed geometrical constraint is analyzed, and used to calculate the PH curve. (3) By using different motion laws in different motion phases, the stability and smoothness of motion are improved. The relationship between the PH curve and constraint is also used to improve the efficiency of the method, and the trajectory symmetry is used to generate part of the trajectory directly through a symmetric operation. In this way, the real-time performance is improved. The proposed method can realize the real-time PPO of the robot, which is beneficial to improving the industrial production efficiency and affecting the global economy. The prescribed geometrical constraint in the proposed method helps to make full use of the robot's workspace and avoid collisions. Pick-and-place is known as one of the key operations in manufacturing and modern industrial system and the proposed method is flexible and widely applicable, so the proposed method can not only be applied on Delta robots, but also can be applied to various engineering areas such as raped sorting, palletizing, grabbing, warehousing, packaging, circuit board testing, automatic production lines system of food, medicine, and electronic products. The paper is organized as follows. Section 2 introduces the kinematics and dynamics of the Delta robot, which lays the foundation for the following sections. Section 3 presents a trajectory planning approach on the basis of the quintic Pythagorean-Hodograph curve with a prescribed geometrical constraint, including PPO path, trajectory planning. The results of simulation and experiment are presented in Section 4. Discussion and conclusions are given in Sections 5 and 6 separately.
# Structural Assessment of Endodontic Files via Finite Element Analysis
## 1 Introduction
Within the literature, studies concerning finite element modeling of cyclic fatigue are limited. [PERSON] et al. conducted cyclic fatigue tests on ProTaper Universal endodontic instruments as well as simplified finite element simulations [15]. Ultimately, this study compares failure locations of the files in the tests with peak stress locations derived from the finite element analysis. Similarly, a study by [PERSON] et al. [16] devised a testing apparatus and conducted durability tests on ProTaper Next files and developed a numerical model which related calculated principal stresses with the measured fatigue life of the file set. In summary, while these studies demonstrate a satisfactory correlation between simulation results and test data regarding the fatigue behavior of endodontic files, they do not report comprehensive modeling details for the test apparatus, file fixation conditions, simulation settings, and contact definitions. The main goal of this study was to create a comprehensive simulation approach for the bending and torsional tests defined in the ISO 3630-1 standard, as well as for the cyclic fatigue tests introduced by [PERSON] [5]. This approach defines required boundary and contact conditions. To enhance computational efficiency and ensure convergence robustness, idealized versions of the simulation models that feature simplified depictions of the testing apparatus were also developed and studied. ProTaper Universal F1 and F2 endodontic files were chosen for this effort, based on the availability of data existing literature. This study aims to demonstrate the suitability of the developed simulation method for evaluating the structural assessment of endodontic instruments. Additionally, it seeks to establish that hysteresis energy can serve as a reliable parameter for quantifying the fatigue behavior of Nickel-Titanium endodontic files.
## 4 Efficient Data Distribution and Storage Method
### I/O Buffering  
In the I/O buffering step, the secondary queue is created for the block group that is processing I/Os, thereby collating I/Os in the queue rather than the standby of I/O request for the same basic block group. Figure 7 shows the processing procedure of the I/O buffering step.  
In the I/O buffering process, it is first checked whether the secondary queue is present. If it is not present, the secondary queue is created, and the I/Os in the queue are added to the secondary queue. If it is present, the requested I/Os are added to the already created secondary queue. Once added, the secondary queue setting is finally turned off. Since standby I/Os in the primary queue can be minimized through I/O buffering, it can prevent the performance degradation of the system. Figure 8 shows the processing method of the I/O buffering step.  
The I/O requests for the same file are not standby, while the first I/O of the file is performed in the I/O buffering step, in which other workers can process those requests. That is, while master 1 performs registration to conduct write 1 in file 1, master 2 fetches write 2 in file 1 and processes it. Here, master 2 also registers that it is performing I/O buffering of the file.  
Since master 2 verified that master 1 was processing write 1 in file 1 through the file information in process, the secondary queue in relation to the file is created, and write 2 is  
Figure 8: Method in the I/O buffering step.  
Figure 7: I/O buffering step.  
registered in the secondary queue. Next, the above process is iterated, showing that master 2 fetches write 5. Thus, Figure 8 shows the accumulation of write 2, write 3, and write 4 on file 1 in the secondary queue.
# Machine Learning for Predicting Neutron Effective Dose
## 2 Methodology
### ML Model Selection  
Extreme gradient boosting (XGB) and random forest models for tabulated data are preferred over artificial neural networks (ANNs) [19; 20; 21]. Three models were selected for fitting the pilot data: XGB, gradient boosting (GB), and the random forest-based Extra Trees regressor (EXTR). The detailed parameter options can be found in [22]. Default parameters were used, with a fixed number of estimators (n-estimator = 100), and the data (5168 \(\times\) 38 after dropping the desired fitting target) were divided into a 75% sample for training and a 25% sample for testing.
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
###### Abstract  
Due to the rapid development of quantum computing technology, encryption systems based on computational complexity are facing serious threats. Based on the fundamental theorem of quantum mechanics, continuous-variable quantum key distribution (CVQKD) has the property of physical absolute security and can effectively overcome the dependence of the current encryption system on the computational complexity. In this paper, we construct the spatially coupled (SC)-low-density parity-check (LDPC) codes and quasi-cyclic (QC)-LDPC codes by adopting the parity-check matrices of LDPC codes in the Advanced Television Systems Committee (ATSC) 3.0 standard as base matrices and introduce these codes for information reconciliation in the CVQKD system in order to improve the performance of reconciliation efficiency, and then make further improvements to final secret key rate and transmission distance. Simulation results show that the proposed LDPC codes can achieve reconciliation efficiency of higher than 0.96. Moreover, we can obtain a high final secret key rate and a long transmission distance through using our proposed LDPC codes for information reconciliation.  
continuous-variable quantum key distribution; reconciliation efficiency; low-density parity-check codes +
Footnote †: journal:  
_Article_
###### Abstract  
Wireless networks, including IEEE 802.11-based or Wi-Fi networks, are inexpensive and easy to install and therefore serve as useful connectivity alternatives in areas lacking wired-network infrastructure. However, IEEE 802.11 networks may not always provide the seamless connectivity and minimal throughput required for Industry 4.0 communications because of their susceptibility to interference from other devices operating in the unlicensed \"Industrial, Scientific, and Medical\" frequency band. Here we analyzed how a wireless audio transmitter operating on this band influences the throughput of an IEEE 802.11 b/g/n network under laboratory conditions. Wireless audio transmission reduced mean throughput by 85%, rendering the IEEE 802.11 b/g/n network nearly unusable. Our analysis suggests that in order for IEEE 802.11 wireless networks to support Industrial 4.0 applications, attention should be paid to the physical layer as well as the data or upper layers, and critical services should not transmit on the 2.4 GHz band. These findings may contribute to understanding and managing IEEE 802.11 wireless networks in various Industry 4.0 contexts.  
IEEE 802.11; Industry 4.0; communication requirements; non-Wi-Fi interference; wireless network; throughput measurement; practical implications