esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This makes it difficult to extract conclusions on the nature and significance of aroma-saliva-wine matrix interactions  
Additionally, recent research using aromatized solutions showed that besides the well-known dilution or salting out effects [20], saliva can act on aroma compounds at very different levels, binding different types of odorants (hydrogen, hydrophobic interactions) [21; 22] and/or metabolizing them [23; 24; 25; 26; 27; 28], which also might give rise to new odorant metabolites [25; 27; 28]. Whether these effects can be relevant in wine, and if wine matrix components might also modulate the effect exerted by saliva on aroma compounds, are some questions that need to be addressed. Moreover, the role of other oral physiological components on wine aroma volatility is unknown. Although it has been shown that wine odorants can be adsorbed to oral mucosa [14], it is not clear if they can be directly adsorbed onto oral epithelial cells [29] or if they can bind to polyphenols from the food matrix already adsorbed to the oral epithelial cells. Actually, saliva can increase the stickiness of polyphenols to the surface of the oral mucosa [30], which might also affect the interaction with odorant compounds. [PERSON] et al. [31] also showed that some polyphenols can directly bind to oral epithelial cells. However, as far as the authors know, there are no previous works considering the effect of saliva and buccal epithelial cells on wine aroma volatility in the same study. Therefore, the aim of this study was to determine the relative effect of the oral physiological components (saliva, buccal epithelial cells) on wine aroma volatility, while also considering the presence of different types of wine matrix compounds (phenolic compounds). To do so, three types of wines that consisted of one synthetic wine spiked with centrifuged human saliva (containing low molecular weight proteins), artificial saliva with mucin (a glycosylate high molecular weight protein), and buccal epithelial cells were prepared. Each wine type was independently assessed with four aroma compounds of different physicochemical characteristics (guaiacol, \(\beta\)-phenyl ethanol, ethyl hexanoate, and \(\beta\)-ionone). Additionally, the effect of four types of phenolic compounds (gallic acid, catechin, grape seed extract, and a red wine extract) on aroma volatility using wines with different types of oral components was also tested. A static headspace approach at equilibrium using Solid Phase Microextraction (SPME)-GC/MS with short sampling times was followed to determine the effect of each oral and wine matrix component on aroma volatility.
## 5 Conclusions  
In conclusion, we have demonstrated a compact, high pulse energy, and narrow linewidth 6.45 \(\upmu\)m laser, based on BGSe-OPO and pumped by a 1064 nm oscillator. A maximum output pulse energy of 1.23 mJ, with the linewidth of 6.8 nm was obtained, which is the highest energy with narrow linewidth at 6.45 \(\upmu\)m, to the best of our knowledge. The pulse duration was measured to be about \(\sim\)24.3 ns. Meanwhile, the output idler energy versus input pump energy and optical-optical conversion efficiency versus the BGSe length  
Figure 6: Measured and simulated spectra of the pump, signal, and idler beams in BGSe-OPO. (**a**) Simulation of signal and idler laser wavelength and linewidth. The inset at the top: the measured pump spectrum at 1064 nm. (**b**) Measured spectrum of output idler beam.  
were simulated. The calculated results were in close agreement with the experimental data. This 6.45 \(\upmu\)m OPO system is precise and appropriate in applications. Such a high pulse energy can meet the tissue cutting threshold requirements (~1 mJ) [2]. In addition, pulses with narrow bandwidth are profitable for improving tissue ablation accuracy.  
Conceptualization, Y.S. and N.W.; methodology, Y.S.; software, N.W.; validation, N.Z., J.Y., F.Y., H.G., Y.B., Q.P. and D.C.; formal analysis, Y.S. and N.W.; investigation, J.C. and Y.W.; resources, C.L., J.Y. and W.L.; data curation, Y.S., N.W. and N.Z.; writing--original draft preparation, Y.S., N.W. and Y.W.; writing--review and editing, N.W. and D.C.; visualization, D.C.; supervision, N.Z., J.Y., H.G., Y.B. and Q.P.; project administration, Y.S., F.Y. and J.Y; funding acquisition, Y.S., F.Y. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation Programs of China (No. 11504389, 61875208, and 22175190).  
Not applicable.  
The data presented in this paper can be obtained by contacting the corresponding author.  
We thank the Key Lab of Functional Crystal and Laser Technology, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences. The authors also acknowledge the help from Chengdu Dien Photoelectric Technology Co., Ltd. (DIEN TECH). We also sincerely thank the reviewers for their valuable comments and insightful suggestions.  
The authors declare no conflict of interest.
## 4 Conclusions  
In this study, cell-plastics were fabricated using the unicellular green alga _C. reinhardtii_ as an ingredient and starches as fillers. By using starches as fillers, these starch cell-plastics could stably maintain their structures even when composed of a 9:1 cells to starch ratio. SEM evaluation revealed that all starch cell-plastics had rough structures formed by the cells on the surface. The mechanical characteristics of starch cell-plastics evaluated via tensile testing, showed that starch cell-plastics composed of 50% cells and 50% starches (1.5 oxidized starch: 1 raw starch) possessed a higher Young's modulus value than that of PBS cell-plastics. Additionally, these starch cell-plastics were biodegradable even in phosphate buffered saline, which boosted the degradation by \(\alpha\)-amylase. This is the first paper to evaluate starch cell-plastics right from their fabrication to biodegradation.  
Conceptualization, A.N., K.I. and Y.S.; methodology, A.N., K.I., M.W., A.M., A.T., S.S. and A.O.; validation, A.N. and K.I; formal analysis, A.N., K.I, A.M., A.T., S.S. and A.O.; investigation, A.N., K.I., A.M., A.T., S.S. and A.O.; resources, A.M. and A.T.; writing--original draft preparation, A.N. and K.I.; writing--review and editing, A.N. and K.I.; visualization, A.N. and K.I.; supervision, A.N.; project administration, A.N. and K.I.; funding acquisition, A.N. and K.I. All authors have read and agreed to the published version of the manuscript.  
Feasibility Study Program of the New Energy and Industrial Technology Development Organization (NEDO).  
Not applicable.  
Data Availability StatementData are contained within the article.  
Figure 6: Evaluation of biodegradation of starch cell-plastics with amylase, displaying (**a**) visualized images of starch cell-plastics and starch after 24 h treatment, and (**b**) degrading activities for starch cell-plastics. Values are the averages of three replicated experiments, \(\pm\)SD.  
**Acknowledgments:** The authors would like to thank [PERSON] for technical support. Raw starch and oxidized starch were kindly provided by Matsutani Chemical Industry Co., Ltd. This work was supported by the Feasibility Study Program of the New Energy and Industrial Technology Development Organization (NEDO).  
**Conflicts of Interest:** The authors declare they have no competing interests.  
**Abbreviations**  
BSA: bovine serum albumin; LDPE: low-density polyethylene; PBS: polybutylene succinate; SEM: scanning electron microscopy; S-S curve: stress-strain curve.
## 6 Conclusions  
This paper examined the advantages of the vertical vibration compaction method in comparison to the static compression test. Building on this, interrelationships between the volume of voids, voids filled with asphalt, voids in mineral aggregates, and mechanical properties of VVCM asphalt mixtures were scrutinized. A procedural standard for volume parameters, predicated on the vertical vibration compaction method, for LSAM-50 asphalt mixtures was introduced. The key conclusions of this research are outlined below:  
The correlation between the mechanical properties of VVCM specimens and field core samples was up to 90% or more. The compressive strength and splitting strength of VVCM specimens were 1.23 and 1.13 times higher than those of PCT specimens, respectively. The gradation change of VVCM specimens after compaction was less than that of PCT specimens. These results indicate that VVCM is closer to the actual compaction effect at the construction site than PCT. VVCM is better than PCT for asphalt mixture design.  
There is a good correlation between the LSAM-50 asphalt mixture's mechanical properties and VV and VFA. Therefore, the recommended values of VV and VFA design for LSAM-50 asphalt mixture are 3.5% to 4.8% and 49.7% to 52.9%, respectively. Within this range, the mechanical properties of the asphalt mixture achieve their peak values. The correlation between LSAM-50 asphalt mixture and VMA is limited. It is merely used as a recommended design reference. Based on the standard's minimum value criteria, a VMA greater than 7.5% is recommended for LSAM-50 asphalt mixture.  
The practical engineering application has demonstrated that the road base layer formed from the LSAM-50 asphalt mixture, guided by the volume parameter design standards proposed in this paper, meets the requirements of the specified standard indicators.  
Research ideas, Y.J.; experiment design, M.C. and S.L.; experiment operation, M.C., H.S. and C.B.; writing--original draft preparation, M.C., Y.Z. and Y.Y.; writing--review and editing, Y.J. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Innovation Capability Support Plan of Shaanxi Province (No. 2022 TD-06), Shaanxi Province Transportation Science and Technology Project (No. 20-02 K), and the Fundamental Research Funds for the Central Universities, CHD (No. 300102218205). The authors gratefully acknowledge all the financial support.  
Not applicable.  
Data Availability StatementData will be made available on request. The data are not publicly available due to Unified management of research group data.  
The authors declare no conflict of interest.
## 3 SEM-RCNN-Based EM Detection Method
### Feature Extraction Step  
The feature extraction step is the basis for deep learning to perform all tasks. Therefore, whether a suitable feature extraction network is selected or not directly affects final detection results. After analyzing and comparing the existing networks, we finally chose the deep residual network (ResNet) [91] combined with a squeeze-and-excitation network (SENet) [92] as the basic backbone, together with the feature pyramid network(FPN) [93]. ResNet can solve the degradation problem occurring in the training process of CNN. SENet can effectively enhance the needed feature information while suppressing less useful feature information. FPN can achieve the accurate detection of multi-scale objects by making good use of shallow feature information and deep feature information.
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion
### Device-to-Device (D2D) Communication (Two-Way Channel)  
In addition to ISAC multi-access systems, the authors of [68] also studied the related two-way channel, i.e., device-to-device (D2D) communication. The D2D setup is illustrated in Figure 15 and is similarly defined to the MAC, except that message \(W_{1}\) has to be decoded at Tx 2 and message \(W_{2}\) at Tx 1. There is thus no receiver terminal. The capacity-distortion region \(\mathcal{CD}\) is defined in analogy to the MAC setup. The capacity region for D2D data communication (without a sensing task), and thus the optimal coding scheme, is still open in general. Various inner and outer bounds on the capacity region have been proposed. [PERSON] [109] and [PERSON] [110] proposed schemes that correlate the inputs of the two terminals in a block-fashion, while for [PERSON]'s coding scheme the correlation ensures a stationary distribution of the inputs and outputs across the blocks and thus still allows for single-letter rate-expressions, [PERSON] has to resort to multi-letter rate expressions based on directed mutual information. An interesting outer bound on the capacity region was proposed by [PERSON] and [PERSON] [102] again based on the dependence-balance idea, similar to the MAC with feedback. The authors of [68] proposed two coding schemes for the ISAC D2D problem. The idea of the first scheme is to extend [PERSON]'s D2D coding scheme similar to the way the authors of [68] extended [PERSON] scheme for the MAC. That means, the two terminals generate compression information, which they convey to the other Tx as part of the indices  
Figure 14: Sum-rate distortion tradeoff in Example 2 achieved without and with collaborative sensing, for given channel parameters \(p_{s}=0.9\), \(t_{0}=0.3\), \(t_{1}=0.1\) and \(t_{2}=0.1\). Figure 15: State-dependent discrete memoryless two-way channel with sensing at the terminals. sent in the data communication scheme. A second, more advanced coding scheme based on joint source-channel coding, was also proposed in [68]. In this second scheme, the compression information is not just transmitted by means of indices sent instead of data but also by correlating the channel inputs with the sensing information (i.e., the compression codewords), as is typically done in hybrid coding [28]. This allows the two terminals to directly transfer the correlation of the sensing information to the channel inputs, which often allows for improved decoding performances at the two Txs. **Theorem 10** (Inner Bound via Joint Source-Channel Coding).
## 6 Movement Track Positioning Model
### Comparative Test  
To verify the advantages of the proposed method, we apply both the proposed method and the Kalman filter to a moving target, and the average value of the RMSE of the proposed method and the Kalman filter is shown in Table 7. Moreover, to verify the efficiency of the proposed method, the UWB positioning system in this paper and the Kalman filter were coded in Matlab (2020a) and run on a PC (Intel Core i5-11500, 4.60 GHz; Memory, 16.00 G). The operation time of the proposed method is shown in Table 7.  
Table 7 shows that the RMSE of the proposed method is 3.6471 lower than that of the Kalman filter. This means that, compared to the Kalman filter, the localization accuracy performance of the proposed method is improved by 100%\(-\)92% = 8%. The running time of the proposed method is about the same as the Kalman filter.  
The full details of the Program addendum is available in the Supplementary File.
## 2 Control Schema and System Description  
Figure 1 illustrates the model of the two-finger gripper used to apply the optimal fuzzy impedance controller. The FSR is fitted below the finger pads to provide gripping force value. The gripper is attached to the FTS fixed on the end effector of the Hexa robot. The FTS measures the weight of the object, which is gripped and picked up.  
Figure 2 is the architecture of the optimal fuzzy impedance controller, including three blocks: position control, force calculation, and optimal fuzzy impedance control.  
Figure 1: The model of the two-finger gripper in SimMechanics (MATLAB).  
The position control block, using a common PID controller, is responsible for controlling the position \(X_{c}\) of the fingers to the desired position \(X_{d}\). The value \(X_{d}\) is determined by the initial reference position \(X_{r}\) and the position compensation \(\Delta X\). For picking up objects of unknown shape and size, the value \(X_{r}\) is set relative to the gripper's fully closed position.  
The force calculation block includes FSR and FTS. The gripping force \(F_{o}\) is measured by the FSR. The object weight \(F_{w}\) is measured by the FTS. \(F_{r}\) is the initial gripping force defined by the user. At the beginning of the gripping process, the desired gripping force \(F_{d}\) is assigned by the value of \(F_{r}\). When the gripper starts to pick the object up, based on the value \(F_{w}\), it will calculate an appropriate value \(F_{d}\) to prevent the object from sliding down. The calculation \(F_{d}\) is presented in Section 3.4.  
The optimal fuzzy impedance control block has three sub-blocks: impedance controller, ILC, and fuzzy controller. The impedance controller calculates the position compensation value \(\Delta X\) for the position control block based on the force error \(F_{c}\), the initial reference position of fingers \(X_{r}\), and the current position of fingers \(X_{c}\). It is the key control, which ensures the gripping force is always kept at a sufficient force. The ILC is derived from the gradient descent algorithm to find optimal parameters \(B\) and \(K\) of the impedance control in unknown environments. This is the initial learning process. For each kind of object (with various hardness) and various closing speed, the ILC is performed to find an optimal data of impedance parameters. By changing the object material, closing speed, and repeating the ILC, an optimal dataset of impedance control is built for further creating a rule base for the fuzzy controller. Based on this rule base, the fuzzy controller calculates the best impedance parameters simultaneously when the gripper fingers touch on the object surface. If the fuzzy controller cannot match any rule in the rule base, the ILC is recalled to calculate new appropriate parameters of the impedance control. If these parameters make the control reach the desired state, they will be analyzed to create new rules and added to the rule base.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
### Main Contributions  
To address this issue, this paper constructs a linear programming-based model for optimizing the sequence of vehicles passing through intersections. A rolling optimizationalgorithm is designed to solve the model and enhance its solving efficiency. Therefore, the main contributions of this paper are as follows:  
(1) The traffic control of the unsignalized intersection is abstracted as a vehicle sequence optimization problem. On this basis, a linear programming model is developed.  
(2) An efficient algorithm is designed to ensure the accuracy and speed of the solution based on moving horizon optimization.  
(3) A numerical experiment is conducted to analyze the impact of critical parameters on the control effect.
## 3 Results and Discussion
### Headspace Volatile Analysis Using SPME-GC-MS of Mallab Samples (Roasted/Unroasted)
#### 3.4.2 Lactones  
Lactones constituted the second most abundant volatile class in mahlab seeds, with WE and WG yielding the highest levels (ca. 33.9 and 14.2%, respectively), and they increased upon roasting, being detected in RE at 6.8% to reach 24.2% in RRE seeds. Such an increase is likely due to the dehydration impact of roasting to enhance the intramolecular lactation of some compounds hydroxy fatty acids producing \(\gamma\) and \(\delta\)-lactone compounds [34]. WE also encompassed, e.g., the highest fatty acid level, indicating that lactones originated from fatty acid cyclization. Among the identified lactones, \(\gamma\)-caprolactone (peak 114), isomintlactone (peak 118) and \(\gamma\)-nonalactone (peak 119) showed increases from 2.14, 1.19 and 0.88% to 6.58, 8.31 and 4.20%, respectively, upon roasting in RE samples. Such observations can explain why the traditional use of mahlab seeds as flavoring agents in confections implies a short heating process to improve its sensory attributes, as evidenced by the increased lactone-derived aroma. \(\gamma\)-Caprolactone has sweet herbaceous notes [35], while isomintlactone and \(\gamma\)-nonalactone are characterized by mint-like [36] and coconut flavors [37], respectively.  
Coumarin (peak 122) along with herniarin (7-methoxycoumarin) (peak 123) were found to be enriched in WE samples, at 19.75 and 1.37%, respectively, compared to the other samples. Coumarin is well-known for its pleasant vanilla-like odor and slightly bitter taste and was previously reported in most parts of the _P. mahaleb_ tree, i.e., leaves, fruits, seeds and wood, as well as in other plants, e.g., _Melitotus alba_ and _Cinnamoun verum_[38]. Aside from their strong aroma, coumarins are reported to exhibit a wide range of biological actions, viz. antioxidant, anticancer, anti-inflammatory and antimicrobial actions [39]. However, hepatic damage and carcinogenic effects were also reported in experimental animals, limiting their potential use in humans. Accordingly, a tolerable daily intake of 0.1 mg/kg of body weight has been estimated based on human data experiments and recommended by the Scientific Panel on Food Additives, Flavorings, Processing Aids and Materials in Contact with Food (AFC) to guard against any hepatotoxicity [40]. Absolute quantification of coumarins in _P. mahaleb_ should now follow, using standards in order ensure conclusive results.
## 4 Fault Monitoring Using KPCA-KDE-SSGLR
### Online Monitoring  
1. Attain trial sample \(\hat{\mathbb{S}}_{tt}\) and stabilize utilizing the mean and standard deviation standards exercised in step 1 of the offline stage.
2. Calculate the kernel vector of the trial sample utilizing the below equation: \[[k_{tt}]=k(\hat{\mathbb{S}}_{i}\,\hat{\mathbb{S}}_{tt})\] (18)
3. Center the kernel vector as per the equation given below: \[\mathbb{K}_{ctl}=\mathbb{K}_{tt}-u_{1}\mathbb{K}-\mathbb{K}_{tt}U+u_{1} \mathbb{K}U\] (19)
4. Obtain the principal component of the test sample from by utilizing the equation: \[z_{tt,k}=\sum_{i=1}^{n}\alpha_{k,i}[\mathbb{K}_{ctl}]_{i}\] (20)
5. Attain the T\({}^{2}\) and Q of the test sample with their individual control limits acquired in the model improvement phase.
6. Analyze the charting statistics \(\hat{R}_{k,m,l}\).
7. If \(\hat{R}_{k,m,l}>\) UCL\({}_{\mathbb{R}}\) then it specifies an unstable condition occurring in the procedure and the expert must instantaneously find out the root causes and resolve it.
8. The process is in-control if both T\({}^{2}\) and Q are smaller than their scrutinizing values. If either T\({}^{2}\) or Q surpasses its threshold, the procedure is out-of-control and consequently, fault identification is conceded to distinguish the basis of the fault.  
To appropriately assess the outcomes [12; 40] the fault detection rate (FDR) and false alarm rate (FAR) are described as follows:  
\[FDR=\frac{AN}{TAN}\ \text{and}\ FAR=\frac{NN}{TNN}\]  
Whenever the procedure is normal, NN is the number of defective instances which are assessed. The cumulative number of normal trials is TNN, the amount of faulty data is AN when the procedure is faulty, and the total number of anomalous samples is TAN.