esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Rotundone as an Important Contributor to the Flavor of Oak-Aged Spirits
## 2 Results and Discussion  
### Identification of An Unknown \"Spicy, Woody, Increase\" Odorant in Oak Wood  
A total of 30 odorants were detected by gas chromatography-olfactometry (GC-O) of the oak wood extract (Table 1). All identified compounds agreed with previous studies on volatiles of oak wood [16; 17; 18; 19; 20]. Amongst the compounds detected, one unknown (the target compound) odorant was described as _spicy, woody, incense-like_. To obtain an interpretable electron-impact mass spectrum (EI-M) of the target compound, the RI range was cut to both an odor detection port/flame ionization detector (ODP/FID) and to a mass selective detector (MSD). During the cut to the ODP, the target compound was marked, and then overlaid with the cut to the MSD. This was repeated using three configurations of different polarity columns. Only certain mass ions were consistently present on all marked spots of every configuration; in particular, _m/z_ 218 was an indication of the molecular weight of the target compound. As a result of the characteristic odor description and mass of the target compound, an investigation of different wood oils, tobacco, hops, dried herbs, and roots was undertaken to determine if the _spicy, woody, incense-like_ target compound was present in another source material. Extracts from Agarwood oil, white peppercors, and the root from Cyperus rotundus contained an intense _spicy, woody, incense_ peak at the same retention times as the target compound. Further analysis of Cyperus rotundus root enabled the identification of the target compound as 5-isopropyl-3,8-dimethyl-3,4,5,6,7,8-hexa-hydro-1(2H)-azulenoeno (rotundone). The identification was confirmed by comparison to the authentic standard. Rotundone went largely went unnoticed as a potent flavor compound, except for being used in perfumery. Most recently, however, it was noted as potent odorant in grapes, as well as the reason behind the black pepper note in wine [7]. It was also identified in a number of products including black pepper, marjoram, geranium, roscamy, saltbush, basil, thyme, and oregano [21] and as chicory coffee [10]. Its potency was confirmed by determination of its odor detection threshold of only 8 ng/L in water [7]. The discovery of rotundone in oak wood prompted subsequent investigations in oak-aged products.
# Bridge Crack Detection Based on SSENets
###### Abstract  
Bridge crack detection is essential to prevent transportation accidents. However, the surrounding environment has great interference with the detection of cracks, which makes it difficult to ensure the accuracy of the detection. In order to accurately detect bridge cracks, we proposed an end-to-end model named Skip-Squeeze-and-Excitation Networks (SSENets). It is mainly composed of the Skip-Squeeze-Excitation (SSE) module and the Atrous Spatial Pyramid Pooling (ASPP) module. The SSE module uses skip-connection strategy to enhance the gradient correlation between the shallow network and deeper network, alleviating the vanishing gradient caused by the deepening of the network. The ASPP module can extract multi-scale contextual information of images, while the depthwise separable convolution reduces computational complexity. In order to avoid destroying the topology of crack, we used atrous convolution instead of the pooling layer. The proposed SSENets achieved a detection accuracy of 97.77%, which performed better than the models we compared it with. The designed SSE module which used skip-connection strategy can be embedded in other convolutional neural networks (CNNs) to improve their performance.  
deep learning; image classification; bridge crack detection; skip-connection; SSENets +
Footnote †: journal: Applied Sciences
# Dynamic Impedance Estimation: Challenges and Considerations
## 2 Transducers Online Equalization
### Where to Go  
With the advent of synchronized measurements in power systems, applications using the harmonic phasor tend to multiply. However, the CTs and VTs scattered throughout the system do not have an adequate frequency response for these applications. The replacement of these transducers with others with a better response is not economically viable. Thus, online and adaptive equalization seems to be a promising alternative.  
The approach shown in this section served only to open the range of issues to be investigated in the coming years, among which are:  
1. What is the influence of the estimation error of the transfer function of the \(H_{p,q}(z)\) system on the design of the equalizer? This point is essential, as it is known that the transfer function obtained from design data can present significant variations from the real system. The ideal would be to estimate these transfer functions by online procedure, but this falls into the process of recursive error (tail-chasing);
2. On which bus, or buses, of the system should the reference meters be installed? This question seems similar to another one already addressed in the literature, which concerns the points of the network for PQ meters allocation;
3. In telecommunications systems, it is common to use the blind equalization strategy [23]. The question is whether it is possible to use the same strategy for equalizing transducers.
# Assessment of Student Music Performances Using Deep Neural Networks
## 3 Material and Methods
### Deep Neural Network Architectures
#### 3.4.2 Convolutional Recurrent Model with Mel Spectrogram  
A music performance evolves with time and, hence, it is essential to use architectures that are capable of learning the temporal dependencies in data. Deep networks combining convolutional layers with RNNs (referred to as CRNNs) are designed to learn both the temporal and local feature representations. CRNNs were first successfully proposed for sentiment analysis in documents [61] and, since then, have been adopted for other tasks like image classification [62], music transcription [44] and music classification [25].  
The M-CRNN uses the MEL input representation and is essentially a CNN with its last layer replaced with an RNN. Using a simple time-frequency representation as an input as compared to the  
Figure 4: Model architecture diagrams. (**a**) Fully Convolutional Pitch Contour Model (PC-FCN); (**b**) Convolutional Recurrent Model with Mel Spectrogram (M-CRNN).  
high level PC allows the network to learn features that span across multiple dimensions instead of just pitch. The architecture details are shown in Figure 4b. The three convolutional layers consist of four components each: (i) a 2D convolution layer, (ii) a 2D batch normalization layer, (iii) an exponential linear unit (ELU) activation, and (iv) a 2D max pooling layer. The hyperparameters for each of the layers are shown in Figure 4b. The RNN layer is comprised of a single gated recurrent unit (GRU) layer with a hidden state of dimension 200. GRUs are used as they are simpler in implementation but are equally well-suited to capture long-term dependencies compared to other RNN units such as long short-term memory (LSTMs) (see [63; 64] for empirical studies). The hidden state of the last GRU is passed to a fully connected linear layer (with an output dimension of 1) followed by a ReLU activation to obtain the model output.
## 4 GNN-Based Digital Twins for Next-Generation Networks
### Existing Studies
#### 4.2.2 Network Slicing Management  
We examine the application of a GNN-based digital twin for network slicing as proposed by [PERSON] et al. [122]. The developed digital twin focuses on network slicing management, utilizing GNNs operating on a multilayered network constructed from slicing configurations. It is capable of performing the prediction of per-slice End-to-end (E2E) metrics, in which the latency was considered as an example of the target metrics. Moreover, three potential what-if scenarios were considered as the showcases for the leverage of the digital twin.  
In 5G networks, network slicing can leverage the virtualized and software-defined nature of the network infrastructure to propose a customized slicing configuration that aligns with the specific usage requirements. This approach brings significant enhancements in terms of QoS. However, achieving this goal is a challenging task primarily due to the difficulty of measuring E2E metrics across multiple network slices spanning multiple network domains and involving different network nodes. Moreover, a digital twin targeting the capability of performing what-if scenarios may require the accurate modeling of the network behaviors given different configurations. To fulfill these requirements, the authors made use of GraphSAGE [85], a GNN to build a data-driven network modeling capable of predicting with precision the E2E latency of multiple network slices given the slicing configurations and substrate topology.  
To train the GraphNDT, the authors reused the data from [106]. The dataset was initially generated from OMNeT++ for routing scenarios which consist of three different topologies, routing configurations, traffic matrices, and captured network metrics such as delay, latency, and jitter. However, by converting the pair-wise source destination traffic matrices into network slices, that dataset was used to stimulate the network slicing management scenario, and the ground-truth network metrics represent the E2E network slicing metrics. The experimental results demonstrate the accuracy of the proposed framework where the predicted latency has less than a 5% error rate under all three network topologies. Furthermore, three what-if scenarios are introduced to showcase the advantages of an NDT. Firstly, it can simulate an increase in traffic intensity to observe potential SLA violations, thus providing network operators with insights into the need for network slice reconfiguration. Secondly, it can simulate link failures and evaluate the monitoring system's adaptive capabilities by observing changes in network metrics. Lastly, it can assess the performance of different optimization algorithms by comparing network metrics with the required SLA thresholds. Overall, the combination of the repurposed dataset and the GraphNDT enables accurate simulation and evaluation of network slicing scenarios, allowing for effective network optimization and management.
## 3 Results and Discussion
### CSO Yield  
The predicted yield of the center point (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(>\) 1.4 mm) standards (STDs) 9, 10, and 11 was 40.25%, whereas the actual yields were 45.99, 47.93, and 32.40%, respectively. Another predicted yield of the center point (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(<\) 1.4 mm) STDs 20, 21, and 22 was 40.95%, but the actual yields were 36.48, 39.62, and 40.84%, respectively. The highest yield was observed in STD 6 (restriction die: 0.8 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(>\)1.4 mm) with an actual value of 53.68% and a predicted value of 39.34% (Table 2). The analysis of the variance of the 2 FI model for the CSO yield is presented in Table 3.  
The 2 FI model for CSO extraction was significant (\(p<0.0472\)) with adjusted R\({}^{2}\) (0.3835), predicted R\({}^{2}\) (0.2255), and nonsignificant lack of fit (\(p=0.9877\)) values. The results indicated that the model equation was acceptable for predicting CSO extraction from cocoa seeds. The CCD-generated 2 FI equation (Equations (5) and (6)) for CSO yield (%) was as follows:  
\[\text{CSO Yield (\%)}=43.2155+19.4588\text{A}-0.0038\text{B}-0.4004\text{AB (For sieve size: >1.4 mm)} \tag{5}\]  
\[\text{CSO Yield (\%)}=14.6360+33.6030\text{A}+0.3428\text{B}-0.4004\text{AB (For sieve size: <1.4 mm)} \tag{6}\]  
where A = restriction die (0.4-0.8 cm) and B = temperature (40-80 \({}^{\circ}\)C).  
A previous report stated that processing temperature affected the yield of virgin rapeseed oil and its sensory and aroma profile. A higher temperature showed more favorable effects in terms of the nutritional values of the oil [34]. Temperature plays a major role in cold-press oil extraction, i.e., increased roasting temperature produces nutritionally rich _Pistacia terebint thus_ oil. Similarly, temperature was the influencing factor in the extraction of almond, walnut, peanut, and grape seed oils [35].  
In this study, as the temperature increased, the yield of CSO decreased when we used a larger size restriction die. However, the yield was not affected at high temperatures when we used a bigger die size. The results showed that the size of the restriction die has an effect on yield depending on the extraction temperature (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**STD**} & \multicolumn{2}{c}{**Yield (\%)**} & \multicolumn{2}{c}{**Acid Value**} & \multicolumn{2}{c}{**Peroxide Value**} & \multicolumn{2}{c}{**Total Phenolic Acid**} & \multicolumn{2}{c}{**Time (Min)**} \\  & \multicolumn{2}{c}{**(mg KOH/g of Oil)**} & \multicolumn{2}{c}{**(mEq/Kg of Oil)**} & \multicolumn{2}{c}{**(ug GAE/g of Oil)**} & \multicolumn{2}{c}{**(mEq/Kg of Oil)**} & \multicolumn{2}{c}{**(mEq/Kg of Oil)**} \\ \cline{2-10}  & **Observed** & **Predicted** & **Observed** & **Predicted** & **Observed** & **Predicted** & **Observed** & **Predicted** \\ \hline
1 & 44.98 & 44.44 & 1.34 & 2.68 & 1.66 & 1.37 & 9.99 & 9.78 & 40.00 & 38.65 \\
2 & 46.10 & 45.82 & 1.52 & 1.75 & 1.86 & 1.70 & 12.32 & 10.29 & 35.00 & 33.02 \\
3 & 38
## 4 Distributed Clustering Algorithm for the UAV Swarm
### Clustering Model Based on Coalition Game  
In the coalition game clustering approach, coalitions are equivalent to clusters, while each cluster corresponds to one coalition. The UAV swarm will eventually form multiple disjoint coalitions, called coalition partition (or coalition structure). Definition 2 gives the concept of coalition partition.  
**Definition 2**.: _A coalition structure is defined as \(\Pi=\{C_{1},C_{2},\cdots,C_{K}\}\), which consists of \(K\) disjoint coalitions with \(\bigcup_{k=1}^{K}C_{k}=\mathcal{N}\) and \(C_{k}\cap C_{q}=\emptyset,\forall k\
eq q\)._  
Then, we establish a coalition game model \(\mathcal{F}(\mathcal{N},u)\) for the clustering problem of the UAV swarm. \(\mathcal{N}\) is the set of game participants. The coalition revenue \(u\) is:  
\[u(C_{k})=\begin{cases}f(C_{k}),&\forall C_{k}\in\Pi,\ \ \ if\ n_{k}\leqslant n_{\max}, \omega_{k}\leqslant\omega_{\max}\\ 0,&\forall C_{k}\in\Pi,\ \ \ otherwise\end{cases} \tag{9}\]  
Since the coalition revenue \(u\) only depends on the cluster members, the proposed coalition formation game is with transferable utility (TU).  
The optimization objective of the coalition game clustering problem is:  
\[\max_{\widetilde{H}}\sum_{C_{k}\in\widetilde{I}I}u(C_{k}) \tag{10}\]  
\(\widetilde{I}\widetilde{I}\) is the best coalition partition.  
According to the coalition revenue, the coalition game of the clustering problem exhibits different characteristics in static and dynamic networks. For a static network, the communication link correlation between two neighboring UAVs is 1. Thus, the adaptive weight \(\alpha_{c}\) is 1. Then we can verify that:  
\[u(C_{k}\cup C_{l})>u(C_{k})+u(C_{l}),\forall C_{k},C_{l}\in\Pi,k\
eq l \tag{11}\]  
It shows that the designed coalition game is superadditive. Furthermore, the designed game is convex, which tends to the formation of large-size coalitions. However, the cluster constraints limit the formation of grand coalitions.  
For the dynamic network, the coalition revenue is related to the average communication link correlation of the cluster. Therefore, the coalition revenue of the clustering problem for the mobile network is not monotonic. The coalition game is not convex and does not have superadditivity. However, UAVs in the same group or having high correlation will merge to form stable clusters.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 2 Dataset Validation and Preprocessing
### Hypothesis Testing  
To determine if the outcome of a dataset is statistically significant, statistical hypothesis testing is carried out. The probability that the difference in conversion rates between a particular variation and the baseline is not the result of random chance is known as statistical significance. This is shown by the _p_-value, which is the degree of marginal significance in a statistical hypothesis test and represents the likelihood that a particular event will occur. To assess if there is a significant difference between the means of the two groups, which may be connected in certain attributes, the _t_-test is a common sort of inferential statistic [11]. The two groups in this case are the population and the sample for Q-factor, SNR and BER. The null hypothesis (H\({}_{0}\)) is that the average of the sample set is the same as the population set. On the other hand, the alternative hypothesis (H\({}_{\text{A}}\)) is that both averages can be differentiated. The hypothesis can be denoted as:  
\[\text{H}_{0}:\text{Mean}(\text{F}[i]_{\text{Sample}})=\text{Mean}(\text{F}[i ]_{\text{Population}}) \tag{1}\]  
\[\text{H}_{\text{A}}:\text{Mean}(\text{F}[i]_{\text{Sample}})\
eq\text{Mean}( \text{F}[i]_{\text{Population}}) \tag{2}\]  
where F represents the features and i = 1, 2, 3 which represents the Q-factor, SNR and BER. The _t_-test reveals _p_-value of 0.97, 0.98 and 0.98 for Q-factor, SNR and BER, respectively.  
A _p_-value greater than 0.05 provides strong support for the null hypothesis, but is not statistically significant. As a result, there is no statistical significance, and the null hypothesis cannot be rejected. The population's distribution matches that of the sample. As a result, the test demonstrates the predictor dataset's reliability.
## 1 Introduction
In addition, embedding non-toxic metal-oxide particles such as nano-zinc-oxide (nZnO) for its ability to enhance the thermal, mechanical [17], and vapor transmission rate properties in wound dressings [18; 19; 20] and packaging material applications has attracted a great deal of attention and has been investigated in several publications [21; 22; 23]. Incorporating the nZnO into wound dressing sheets supports the antibacterial features [24; 25] and increases healing rates [26]. Additionally, ZnO is acknowledged as safe, and according to the Food and Drug Administration (FDA) [27], it is essential for human health and physiological activity that each individual takes in 10 mg of zinc per day [28], and the cytotoxicity analysis of the nZnO particles has proved that a concentration of 40 mg/mL or less could be considered non-toxic [29]. Although a few research groups have used nZnO particles in different applications such as bioplastics, wound dressings, and food packaging [17; 18; 19; 20; 21; 22; 23], based on our best knowledge, the effects of adding nZnO particles on functional starch/PVA/glycerol-based wound dressings have not been studied. Therefore, in this work, the nZnO particles were inserted into a starch/PVA/CA/glycerol film (SPCG) to prepare biodegradable and antibacterial wound dressing materials. To establish the proper wound environment for the ideal wound-healing procedure, the material water vapor transmission rate (WVTR), swelling index, antibacterial characteristics, biodegradability, gel fraction, solubility, mechanical properties, and antibacterial activity were assessed and validated. Then, the interactions between the nZnO particles and starch-based blends, which lead to UV and visible light transmittance, were evaluated using the UV/Vis spectrometry device. The wound dressing material morphology properties were also examined utilizing SEM imaging.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### LOS Direction Evaluation
#### 5.4.2 Refining the Estimated LOS Direction Using Residuals  
A statistical process is further used to refine the estimation of \(\theta_{los}\): linear regressions are performed between lidar-measured \(V_{LOS}\) and the reference speed \(V_{hor}\) projected using angles \(\theta_{proj}\)--e.g., \(\sim\)20 values centered around \(\theta_{0}\) with an increment of 0.1\({}^{\circ}\). Each linear regression yields one Residual Sum of Squares (RSS) value which is then plotted against \(\theta_{proj}\). A 2 nd order polynomial is fitted to the curve (Figure 8). \(\theta_{los}\) is taken at the minimum of the parabola. The last step assumes that a minimum of residuals (\(\sum\left(V_{LOS}-V_{ref}\right)^{2}\)) is obtained when \(V_{hor}\) is projected onto the correct LOS direction. Figure 8 displays the RSS process results.  
The final estimations of the LOS direction are: \(\theta_{los,5B}=286.03^{\circ}\) and \(\theta_{los,ZDM}=287.44^{\circ}\). We typically observed a difference of \(\sim\)0.3\({}^{\circ}\)-0.7\({}^{\circ}\) between the LOS direction \(\theta_{los}\) provided by the RSS analysis and \(\theta_{0}\) values provided by the cosine fitting. Two plausible explanations affecting the cosine fitting are: lower quality of lidar \(V_{LOS}\) measurements for wind directions orthogonal to the LOS; asymmetry of the sonic anemometer's geometry causing biases in measurements outside the preferred direction sector.  
The sonic anemometer is aligned to the absolute North, with a mounting uncertainty of \(\approx\)2\({}^{\circ}\). Using GPS coordinates, \(\theta_{los}\) was estimated to \(\approx\)285\({}^{\circ}\) and \(\approx\)286\({}^{\circ}\) for 5B and ZDM respectively, which is compatible with the results of the LOS direction evaluation.
# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 2 Method
### Sparse Similarity Calculation  
Before recognizing previously visited places, a measure of similarity between the images in required. Ideally, the features in an image \(I_{t}\) would be compared against all the other images, thus obtaining a similarity matrix \(S\). Each entry \((i,j)\) in this matrix contains the similarity between the node \(i\) and \(j\). However, the process of directly comparing the features from every image is computationally expensive. An alternative is to calculate an approximation \(\tilde{S}\) to that similarity matrix \(S\) as shown in Figure 3.  
The similarity matrix is updated every time a new node is added to the graph. Let \(V_{L}\) be the last added node to the graph and \(V_{0:L-\delta}\) the set of all nodes from \(0\) to \(L-\delta\) with \(\delta\) the number of ignored nodes before the last one (this is to avoid recognizing recently added images since they are likely to be similar to the one currently being added). We initialize a vector of votes with \(L-\delta\) zeros. Then, for each descriptor \(z_{L,r}\in Z_{L}\), the most similar descriptor \(z_{M,q_{1}}\) and the second most similar descriptor \(z_{N,q_{2}}\) from all the features in the set \(V_{0:L-\delta}\) are obtained, being \(M\) and \(N\) the images where these features appear. If \(\frac{\|z_{L,r}-z_{M,q_{1}}\|}{\|z_{L,r}-z_{M,q_{2}}\|}\) is less than a threshold \(\tau\) we sum \(1\) to the \(M\)th entry in \(v\). To find the two most similar descriptors we utilized FLANN.  
After the vector of votes has been calculated, it is normalized in the range \([0,1]\). Then, it is added as the last row in the matrix \(\tilde{S}\). We add the necessary zeros into the matrix to keep a consistent dimension since every newly stacked vector of votes is larger than the previous one. An example of a similarity matrix obtained with this voting scheme is shown in Figure 3. Despite the noisy entries with high similarity values, the diagonals resultant from the sequences of similar images can still be observed. In the next section, we present a method to deal with these noisy entries by looking for predefined sets of lines with high similarity.