esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Retention of the Aroma Compounds during the Different Encapsulation Process
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{2}{l}{**Retention (\%)**} & \multirow{2}{*}{
\begin{tabular}{l} **SD-UN** \\ \end{tabular} } \\ \cline{2-2} \cline{4-5}  & **SFD** & & & **FD** & **SD-2 FN** & **SD-UN** \\ \hline \(\alpha\)-pinene & 42.42 \(\pm\) 0.37 \({}^{\text{a}}\) & 57.64 \(\pm\) 1.24 \({}^{\text{b}}\) & 90.31 \(\pm\) 2.19 \({}^{\text{c}}\) & 104.06 \(\pm\) 3.47 \({}^{\text{d}}\) \\ Sabinene & 8.90 \(\pm\) 0.29 \({}^{\text{a}}\) & 11.75 \(\pm\) 0.32 \({}^{\text{b}}\) & 39.58 \(\pm\) 1.27 \({}^{\text{c}}\) & 46.07 \(\pm\) 1.49 \({}^{\text{d}}\) \\ \(\beta\)-Myrcene & 43.14 \(\pm\) 0.39 \({}^{\text{a}}\) & 96.30 \(\pm\) 3.51 \({}^{\text{b}}\) & 44.19 \(\pm\) 1.34 \({}^{\text{a}}\) & 45.14 \(\pm\) 0.89 \({}^{\text{a}}\) \\ (Z)-\(\beta\)-Ocimene & 5.87 \(\pm\) 0.21 \({}^{\text{a}}\) & 14.08 \(\pm\) 0.90 \({}^{\text{b}}\) & 61.13 \(\pm\) 0.57 \({}^{\text{c}}\) & 31.95 \(\pm\) 0.41 \({}^{\text{d}}\) \\ Ocimene & 5.32 \(\pm\) 0.12 \({}^{\text{a}}\) & 13.84 \(\pm\) 0.76 \({}^{\text{b}}\) & 52.58 \(\pm\) 0.45 \({}^{\text{c}}\) & 82.28 \(\pm\) 1.14 \({}^{\text{d}}\) \\ Linalool & 13.80 \(\pm\) 0.46 \({}^{\text{a}}\) & 22.17 \(\pm\) 0.66 \({}^{\text{b}}\) & 97.32 \(\pm\) 0.8 \({}^{\text{c}}\) & 99.71 \(\pm\) 1.86 \({}^{\text{d}}\) \\ Total & 14.16 \(\pm\) 0.13 \({}^{\text{a}}\) & 24.52 \(\pm\) 0.17 \({}^{\text{b}}\) & 62.28 \(\pm\) 0.1 \({}^{\text{c}}\) & 73.52 \(\pm\) 0.63 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} \({}^{\text{a}}\)Red Means within a row with different letters are significantly different (\(p<0.05\)).  
\end{table} 
Table 5: Aroma retention percentages of selected aroma compounds in terephthal fruit oil by different encapsulation methods. highest (96.30%) in FD when it was almost at the same level (43-45%) for all other methods. Similarly, a study conducted to encapsulate the lemongrass essential oil by freeze-drying displayed that the highest retention obtained was 81.6 \(\pm\) 5.8% for \(\beta-\)myrrene with the slow-tested freezing protocol and a mixture of 50% GA, 40% MD, and 10% XG (volume) [69]. Additionally, [PERSON] et al. [70] reported that the encapsulation efficiency of spray drying using gum arabic was 74-88% for \(\beta-\)myrcene and 44-64% for linalool. The authors displayed that steric factors and chemical functionality (diffusion through the forming matrix and solubility) of volatile compounds are the factors influencing the retention of volatile monoterpenes in gum arabic capsules.
## 5 Results and Analysis
### Test Performance
#### 5.1.2 Program Size
This dispatching rule preferred to select the operation with smaller processing times (PT) and larger weights (W). It is interesting that this dispatching rule preferred operation with smaller work remaining (WKR) based on the first section of \(r_{1}\) while it tended to select the operation with larger WKR, based partially on the second section of \(r_{1}\). This indicates that although we can interpret the rules to some extent, it is still hard to completely understand the behavior of rules. We will continue to work on this topic in the future. The rules evolved using the IGEP and GEP algorithms can be simplified, as shown in Equations (17) and (18), respectively. It can be seen that the rule structure of both \(r_{2}\) and \(r_{3}\) was more complex than that of FS-IGEP, even after simplification. In addition, \(r_{1}\) contained more of the meaningful building blocks, such as PT/W and WINQ/W, that have been established to yield favorable outcomes in the context of minimizing the mean weighted tardiness in DJSS. In general, the results of the analysis performed on the FS-IGEP evolved rules showed that the selection of important terminals led to a reduction in the length of the rules and a  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Test Sets** & **WEDD** & **WESD** & **WSPT** & **W(CR + SPT)** & **WCOVERT** & **GEP** & **IGEP** & **FS-IGEP** \\ \hline
2, 5, 85\% & 30.96 & 4.86 & 0.06 & 0.53 & 0.58 & \(-\)10.23 \(\pm\) 2.88 & \(-\)11.35 \(\pm\) 1.67 & \(\mathbf{-13.68\pm 1.26}\) \\
2, 5, 90\% & 37.78 & 6.76 & 0.37 & 0.32 & 0.27 & \(-\)5.87 \(\pm\) 2.57 & \(-\)6.63 \(\pm\) 0.85 & \(\mathbf{-10.52\pm 1.17}\) \\
2, 5, 95\% & 39.23 & 11.25 & 0.18 & 0.03 & \(-\)0.57 & \(-\)6.01 \(\pm\) 1.31 & \(-\)6.46 \(\pm\) 0.75 & \(\mathbf{-9.32\pm 1.06}\) \\
2, 10, 85\% & 34.69 & 8.89 & 0.07 & 0.36 & 0.08 & \(-\)3.27 \(\pm\) 1.42 & \(-\)4.13 \(\pm\) 0.69 & \(\mathbf{-9.12\pm 0.93}\) \\
2, 10, 90\% & 31.95 & 10.52 & 0.25 & \(-\)0.12 & 0.02 & \(-\)4.38 \(\pm\) 0.95 & \(-\)5.74 \(\pm\) 0.81 & \(-\)6.73 \(\pm\) **0.78** \\
2, 10, 95\% & 33.26 & 9.56 & \(-\)0.15 & 0.28 & 0.93 & \(-\)4.86 \(\pm\) 0.91 & \(-\)5.12 \(\pm\) 0.79 & \(\mathbf{-8.58\pm 0.85}\) \\  
mathbf{-10.34\pm 0.27}\) \\
8, 10, 95\% & 31.25 & 5.88 & 0.23 & \(-\)0.  
more concise structure. This, in turn, enhanced the interpretability of the dispatching rules and facilitated their application in practical production scenarios by decision makers. \[r_{1}=\frac{\max(PT,NPT)+WKR}{W}+\max\left(\frac{PT}{W},\frac{WINQ}{W}+NOR+WKR\right) \tag{16}\]  
\[ \begin{array}{l}r_{2}=(WIQ+NPT+FDD)\times\frac{NOR}{W}+\max(AT,PT\times NPT) \\ +NOPS+2 DD\times\min(\frac{SI^{2}}{NOR+PT})-MRT\\ \begin{array}{l}r_{3}=\frac{NOIQ+MRT}{W}-\left(W\times NOR-\max\left(\frac{ NOIQ+MRT}{W}\right)\right)\\ -\min((NOIQ,FDD)+(PT+NOINQ))\\ +ORT\times\frac{WKR}{W^{2}}-WIQ\end{array}\end{array}  \tag{18}\]
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 1 Introduction
### Contributions  
In this paper, we propose an effective PSCCH searching algorithm for 5G-NR V2X sidelink communications in the Mode 2. The proposed algorithm can be applied:  
* At the transmitting UE, when it performs a resource allocation based on a sensing-based algorithm;
* At the receiving UE, when it is trying to decode the PSCCH to obtain the desired data contained in the transport block of PSSCH.  
To the best of our knowledge, PSCCH searching algorithms, beyond the Blind algorithm as in [15], specifically designed for 5G-NR V2X sidelink communications have not been addressed in the literature, yet. The main contributions of this paper are the following:  
* A correlation analysis is performed to verify the peak values of the correlation of the received signal with the PSCCH DMRS, and the predominance of the desired peak over the others;
* Based on the previous correlation analysis, an efficient algorithm to search/decode the first stage SCI in 5G-NR V2X sidelink communication systems is designed;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm Group** & **Algorithm Description** & **Reference** \\ \hline \multirow{4}{*}{Platooning based} & Subchannel selection by a platoon leader which & [19] \\  & learns from the communication environment. & \\  & Subchannel selection information distributed by a & \\  & platoon leader which grants the resources by & [20] \\  & the network. & \\  & When a UE is located within a certain zone, this one & \\  & uses the subchannel allocated to the zone. & \\  & clustering based & The resources are divided into different resource & \\  & sets, and each cluster head selects its resource set. & \\  & Clustering-based resource management based on an & \\  & optimization of the total average sum rate of & \\  & Subchannel selection to minimize the interference & \\  & between UEs. & \\  & Subchannel selection based on reinforcement & \\  & learning to prioritize UEs with urgent needs. & \\  & Subchannel selection based on the density & \\  & information and channel and queuing & \\  & state information. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Finally, a theoretical runtime analysis is performed to compare the runtime of the proposed algorithm with the Blind algorithm, because it is the one that guarantees the best overall system performance in terms of PSCCH detection.  
The simulation results show that the Proposed algorithm achieves the same performance as Blind algorithm in terms of PSCCH detection, SCI Block Error Ratio (BLER) and throughput, but with a much shorter runtime.  
The remainder of this paper is organized as follows: Section 2 describes the system model considered in this work. Section 3 presents the design of the proposed algorithm. Finally, the main performance results are shown in Section 4 and the conclusions in Section 5.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 1 Introduction
This article strives to answer the following research questions (RQ):  
* RQ1: What sensing modalities are used to measure engagement? * RQ2: Which application domains are and will benefit from human engagement estimation? * RQ3: What are the current challenges confronting widespread deployment of engagement estimation systems? This review presents a retrospective and broad comparative analysis of current techniques and algorithms of human engagement estimation. We focus attention on the fundamental element of this digital world--sensors--and how they measure engagement, their accuracy and limitations are in real-world scenarios, as well as challenges and opportunities of deploying sensor-empowered engagement estimators in various application domains. Our key findings include:  
* A comprehensive comparison of commonly used sensors for engagement estimation in terms of accuracy, practicality, and main applications. Results demonstrate disparate sensor requirements among use cases. * Strengths and weaknesses of existing analytic and data-driven engagement estimation algorithms are reviewed highlighting a trend of exploiting Deep Learning for better accuracy and generalization. * Our review of existing engagement metrics reveals a gap in the knowledge of how to express engagement to be useful across different applications and to cater for individual needs. * We predict a prevalence of human engagement estimation in digital engagement, driver assistance, human-robot interaction, and Physiological Feedback and Training; however, reliability, generalization, and user acceptance will remain major issues to address. The remainder of this paper is structured as follows: Section 2 elucidates the context and scope of the review; Section 3 introduces different sensors and common datasets; Section 4 expands on engagement estimation methods; Section 5 reviews engagement-related metrics; Section 6 gives examples of application domains where human engagement estimation has great impact; Section 7 presents an in-depth discussion on the existing work of human engagement estimation with a focus on identifying accuracy, privacy, and other real-world applicability problems; Section 8 concludes this review by sharing insights in future trends of human-engagement-enabled applications.
## 7 Discussion and Analysis
### Feature Fusion Models Leverage the Complementary Benefits of Multimodal Data  
Based on the results presented in Table 8, the expression and speech feature fusion model demonstrates superior recognition performance against the single-modal data model, which proves that the complementary advantages of multimodal data overcome the limitations of single-modal data.  
The empirical results of our multimodal feature fusion model (90.73% accuracy) validate the theoretical framework of multimodal emotion recognition discussed in the Related Works Section. The substantial performance improvement over single-modal approaches (facial expression: 79.12%; speech: 83.47%) empirically demonstrates the complementary nature of these information sources when applied specifically to children with autism. This performance enhancement is particularly significant given the documented challenges in emotion recognition for this population, where traditional single-modal approaches may fail to capture the full spectrum of emotional expressions.  
Expression and speech, two distinct sources of emotional information, provide complementary data. Expression mainly conveys non-verbal emotional information, whereas signals such as intonation and rhythm in speech also contain emotion, which cannot be fully accessed in a single modality. Furthermore, a single modality has constraints when it comes to addressing nuanced alterations in emotional displays. Multimodal fusion has the ability to decrease errors or ambiguities that arise from a single source of information, thus enhancing the accuracy and robustness of the model. In special groups such as children with emotional interaction disorders, the change of facial expressions may be less discernible, thus incorporating speech signals can enhance the model's comprehension of the individual's emotional state.  
The advantages of multimodal data were also verified by [PERSON] et al. [34]. They used auditory and visual clues from children with autism to predict children's positive, negative, and neutral emotional states in real-world game therapy scenarios, with a recognition accuracy of 72.40% [34]. We use the CvT network to extract the facial expression and speech features of children with autism. By using a multi-scale channel attention module, the model enables the fusion of multimodal data. The accuracy of multimodal emotion recognition is 90.73%, which is higher than the accuracy of 79.12% for the facial expression modality and 83.47% for the speech modality. This effectively enhances the model's performance and makes use of a higher value between the data for both modalities.
## 2 Results and Discussion
### Volatile Composition of Investigated Fruits and Vegetables
#### 2.1.7 Spinach  
Regarding the characterization of the volatile profile of spinach, 57 volatile metabolites were identified, mainly 14 esters, 13 terpenic compounds, 8 alcohols and 7 aldehydes. Higher alcohols are the most dominant chemical group, accounting for 43.5% of the total volatile fraction, followed by esters (25.6%), aldehydes (16.5%) and terpenic compounds (10.9%).  
C6 alcohols and aldehydes, with an odor similar to cut grass, are typical compounds produced by green leafy vegetables, such as spinach and esters [62]. The major compounds identified in this vegetable were (_E_)-2-hexen-1-ol (148), 2-hexenal (76) and 1-hexanol (126), which together account for 56.8% of the total volatile fraction. These volatile compounds are derived from C18 unsaturated fatty acids such as linoleic acid and/or \(\alpha\)-inolenic via the lipoxygenase/ hydroperoxide lyase pathways [63]. Once deoxygenated by lipoxygenase (LOX), the resulting hydroperoxide fatty acids are metabolized by several enzymes, including hydroperoxide lyase (HPL), to produce volatile compounds (Figure 5).  
C6 aldehydes responsible for the 'herbaceous' aroma are formed from 13-hydroperoxides and include hexanal and (_Z_)-3-hexenal. The latter is an unstable compound and is rapidly isomerized to (_E_)-2-hexenal by (_3Z_):(_2E_)-enal isomerase [62]. These aldehydes, in turn, can be transformed into the corresponding alcohols and esters through the activity of alcohol dehydrogenase (ADH) and alcohol acyltransferase (AAT). According to [PERSON] et al. [64], these volatile compounds have bacterial antiproliferative activity, and unsaturated (_E_)-2-hexenal and (_E_)-2-hexen-1-ol volatiles have higher activity than hexanal saturated volatiles and 1-hexanol.
## 2 Related Work
### DL-Based Scheme  
As deep learning becomes more and more intelligent, its influence has been expanding, and it has a complete theoretical framework and some practical experience base. It has been applied very effectively in various fields, such as target detection, speech recognition, video recommendation, text data analysis, medical diagnosis, treatment, etc. The applications of deep learning methods in communication are the spectrum sensing of signals, classification of signal modulation methods, etc.  
There are also a few experiments combined with deep learning in the study of SNR evaluation. The DL-based algorithm proposed by [PERSON] et al. [22] indirectly performs SNR estimation by estimating the target signal amplitude using a neural network structure containing five convolution layers for one-dimensional signal data. First, the received signal is segmented according to the input dimension of the deep neural network. Then, the trained deep neural network is used to estimate the signal amplitude of each segment. Next, the estimated amplitude of the target signal is calculated based on the estimated amplitude of each segment. Finally, calculate the SNR of the received signal. This method can be applied to more modulation types, and the effective range of SNR evaluation is broader and more robust to adapt to phase and frequency shifts. The idea of deep learning also inspires research on this topic; two-dimensional images of the signal are used as the input of the neural network, then improve the neural network model to make the SNR ratio evaluation more accurate.
# Biodegradation of Picolinic Acid by _Rhodococcus_ sp. PA18
## 3 Results
### Effects of Initial PA Concentration and Inoculum of the Strain on PA Biodegradation  
To investigate the effect of the environmental factors on PA biodegradation, the effects of the initial PA concentration and the inoculum of the strain on the PA degradation rate were investigated. As shown in Figure 3A, the PA degradation efficiencies were 10.4%, 28.6%, 68.0%, and 95.9% after 6-h of incubation, from a starting OD\({}_{600}\) of 0.05%, 0.20%, 0.50%, and 1.00%, respectively, when the initial PA concentration was 100 mg/L at 30\({}^{\circ}\)C, pH 7.0. Furthermore, as shown in Figure 3B, when the initial concentration of PA was increased from 50 mg/L to 300 mg/L, the time required for complete PA removal increased from 18 to 48 h, which increased to 84 h when the initial concentration of PA was increased to 400 mg/L. However, the PA could not be degraded when the concentration was 500 mg/L. One possible reason was that the excessive concentration of PA was toxic for the strain. The delay period was extended as the initial PA concentration increased. Compared with other PA-degrading strains previously reported, such as, _Achromobacter sp._ JS18 [11], _Acaligenes faecalis_ JQ135 [12], _Arthobacter picolinophilus_ DSM 20665 [13], _Burkholderia_ sp. ZD1 [3], and _Streptomyces_ sp. Z2 [14], strain PA18 possessed a very high degradation efficiency.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 4 Data Analysis
### Architectures and Applications
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & **Count** & **ENOB** & \begin{tabular}{c} **\(f_{s}\)** \\ **[MHz]** \\ \end{tabular} & \
\begin{tabular}{c} **FOM\({}_{W}\)** \\ **[p/Level]** \\ \end{tabular} &
\begin{tabular}{c} \(\overline{FOM}_{TID}\) \\ **[Levels/mg]** \\ \end{tabular} \\ \hline \(\Delta\Sigma\) & 5 & [16; 20.3] & [0.001; 0.125] & [1; 37.8] & 6 & 2320 \\ SAR & 19 & [8; 16] & [0.025; 500] & [0.1; 78125] & 34 & 83,313 \\ PIP & 21 & [7.4; 12.9] & [3; 3200] & [0.8; 24.8] & 109 & 2,139,462 \\ FIF & 8 & [6.8; 9.4] & [1000; 2200] & [1.4; 26.5] & 79 & 303 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Operation ranges within the _selection_ segregated by architecture.
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Packets Forwarding Scheme (UIWPF)
#### 3.4.1 Efficient Neighboring Discovery Process  
In this phase, initial neighboring information tables are constructed, which are used in the routing phase where different data paths are selected in the final routing tables based on the priority of data-forwarding nodes. The neighboring discovery process finds and maintains the updated neighboring ASNs information for each ASN in UWSNs. Initially, a remote user initiates the neighbors finding process by forwarding a network initialization \(\left(\mathit{ini\_msg}\right)\) message to the sea surface sink. Upon receiving the \(\mathit{ini\_msg}\) message, the sea surface sink broadcasts neighboring discovery \(\left(\mathit{neg\_msg}\right)\) message to randomly deploy acoustic nodes in its communication range with the following values: the sink identity, level number and location information with the angle of packet departure in UWSNs. An acoustic node closest to the sink receives the \(\mathit{neg\_msg}\) message successfully, sets the layer number to maximum \(n\), which infers that it has a direct contact with the sea surface sink. Then, each ASN creates a new record entry for neighboring sink and computes the distance to the sea surface sink of itself and stores this information in the neighboring information table. The ASNs directly received the \(\mathit{neg\_msg}\) messages from the sea surface sink are marked as level \(n\). This level is periodically decreasing in the downward direction for the nodes located in the lower layers such as \(n-1\), \(n-2\), etc., and reaches to 0 as shown in Figure 5a. After updating the sink record, these ASNs rebroadcasts the \(\mathit{neg\_msg}\) message to their neighbors within their transmission radius with the following values: the sender identity, current channel, remaining energy, level number, position, angle of departure and distance to the sink. Upon receiving the \(\mathit{neg\_msg}\) message correctly, each ASN creates a record entry for new neighboring ASNs then computes the distance to the sender of itself, angle of packets arrival, level number, and stores the entire received information in the neighboring information table. This procedure is repeated until each ASN has its neighbors information stored in the routing table. During each message exchange process, the receiver acoustic node sends an acknowledgment message to the sender using the CSMA mechanism to ensure the guaranteed delivery. Thus, the communication links between acoustic nodes are bidirectional tested in UWSNs. At this stage, each acoustic node is aware of its neighbors deployed for events monitoring in UWSNs.