esa-sceva/satcom-chunk-collection huggingface dataset:
# Targeted Substituted-Phenol Production by Strategic Hydrogenolysis of Sugar-Cane Lignin
## 3 Results and Discussion
### Sugar-Cane Lignin (SCL) Characterization
#### 3.2.9 Effect of Ni and Fe Catalysts  
Depolymerisation of SCL with Ni/Al\({}_{2}\)O\({}_{3}\) and Fe/Al\({}_{2}\)O\({}_{3}\) gave different selectivity plots compared to the noble metals (Figure 4). Both had higher yields towards one specific compound, 4-ethylphenol (2A), followed by 2-methoxyphenol (1) and 4-ethyl-2,6-dimethoxyphenol (11). Other main products for Ni were 2,6-dimethoxyophenol (6A) and 4-propyl-2,6-dimethoxyenol and for Fe 4-ethyl-2-methoxyphenol (3) and 4-(2-hydroxyethyl)-2,6-dimethoxyphenol (10). Overall, according to Figure 4, if the desired reaction was dealkylation, nickel was appropriate, whereas iron would mainly give branched type of products. SCL depolymerisation using nickel showed a consistent level of selectivity, with highest yield for 2,6-dimethoxyphenol and 4-ethylphenol. The catalytic activity of these metals was not a surprise. In the literature, studies with model compounds using nickel and iron gave a variety of products [22; 23], thus, it would be surprising if it were to be different with a real lignin in reaction with these metals. [PERSON] and co-authors demonstrated depolymerisation of a bich derived lignin through alcoholysis followed by hydrogenolysis using a nickel catalyst [23]. An \(\alpha\)-O-4 bond in benzyl phenyl ether was cleaved by hydrolysis without catalyst, however, once a nickel catalyst was added, the predominant route was hydrogenolysis [45]. Interestingly, [PERSON] et al. reported bond cleavage on the Ni surface as the rate determining step in the hydrogenolysis of a 4-O-5 bond [46]. We found that with a real SCL, hydrogenolysis and deoxygenation were also appreciable when using a nickel-based catalyst. For iron [22], rupture of linkages such as \(\alpha\)-O-4 and \(\beta\)-O-4 gave toluene and phenol. These authors [22] also found relevance in the position of -OH group regarding to the \(\beta\)-O-4-type molecule, thus, the groups around the linkages that were cleaved influenced how a reaction proceeds. When [PERSON] et al. investigated a two-step process of phenol production from lignin, they reported that iron oxide favoured catechol and methoxyphenol conversion to molecules like phenols, cresols and alkylphenols [47]. Many reactions like hydrogenolysis, hydrodeoxygenation, hydrogenation, dehydration, decarboxylation, and decarbonylation [43; 48] can occur in lignin depolymerisation and the final product depends upon the type of catalyst used.  
The findings in this work were not divergent to this aspect, iron and nickel had different product distributions, nevertheless, our studies showed that compared to noble metals (Rh and Pt), it was possible to achieve appreciable product yields for the reductive catalytic hydrogenolysis using lower cost catalysts.
# Analysis of Fluid Velocity inside an Agricultural Sprayer Using Generalized Linear Mixed Models
## 1 Introduction  
The velocity of the fluid inside an agricultural sprayer, necessary for the homogeneous mixing of the active matter, is determined by the internal agitation system, which is hydraulic in most cases. It differs according to (a) certain working parameters such as circuit pressure, number of active nozzles, and liquid level in the tank; and (b) the geometry of the tank height, width, section type, presence of barriers and irregularities inside the tank, and the location of the nozzles.  
The combination of these two sets of parameters makes it difficult for the observed fluid velocity data to fit a normal distribution. This requires the use of non-parametric methods when evaluating possible velocity variations based on working and geometry parameters [1]. This analytical approach helps reveal the effect of the parameters separately, masking the possible interactions between them, as well as the weight that each one exerts on the velocity variations.  
Fluid velocities inside spray tanks can be correlated with the efficacy of the agitation. [PERSON] et al. [2] concluded that the efficacy of agitation was improved by increases in both flow rate and working pressure; similar conclusions were obtained by [3]. In turn, it can be concluded thatan increase in fluid velocity will produce an improvement in the efficacy of the agitation system [4]. Different studies have investigated fluid velocities inside spray tanks using Computational Fluid Dynamics (CFD) [5]. The use of this type of model is very useful, but it has the drawback of requiring long calculation times and the need for very precise data on the geometry of the interior of the tank and the location and characteristics of the components of the agitation system [6]. In this sense, Generalized Linear Mixed Models (GLMM) are an alternative to the CFD.  
GLMM extend the set of generalized linear models by adding random effects to the linear estimator [7], allowing the modelling of correlated observations. In addition to generalized linear models [8], the GLMM can be formulated using a three-part sequence [9], on the basis of its general formulation [10; 11] (Equation (1)).  
\[y=X\beta+Zu+\varepsilon \tag{1}\]  
where \(y=n\times 1\) observed data in a column vector; \(X=n\times p\) matrix of the \(p\) predictor variables (all levels of fixed effects); \(\beta=p\times 1\) column vector of the fixed-effects regression coefficients; \(Z=n\times q\) design matrix for the \(q\) random effects; \(u=q\times 1\) vector of the random effects; \(\varepsilon=n\times 1\) unobserved error vector.
## 3 Methodology
### Data Pre-Processing
#### 3.1.2 Data Interpolation  
To accurately reflect real-time behavior within the load time series, missing values were addressed through interpolation. Specifically, the \"Means by Nearby Points\" method was employed, guided by the following equation:  
\[x^{\prime}=\left(x_{1}+x_{2}+x_{3}+x_{4}\right)/4 \tag{1}\]  
where \(x_{1}\) and \(x_{2}\) denote the two preceding values, and \(x_{3}\) and \(x_{4}\) signify the two subsequent values (\(x^{\prime}\)). This approach effectively eliminates irregularities and yields a smoother load time series.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 4 Results and Discussion
### Analysis of Results

Table 3: Descriptive performance observation of W-GUN in terms of energy consumption. The throughput performance of the proposed framework is comparatively studied in the results presented in Figure 11, with varying numbers of sensor nodes in the underwater network environment. The throughput of the proposed framework is significantly higher for the considered underwater relay optimization environment compared to state-of-the-art techniques. The better throughput of the proposal is due to the utilization of stable and lower delay-centric underwater relay nodes. In the framework, an underwater environmental scenario-centric routing path is discovered to have more stable nodes in the delivery path. In other words, the proposed underwater relay node optimization framework selects the more stable underwater routes compared to the considered existing literature with the optimal number of underwater relay nodes. The delivery path consists of stable underwater relay nodes in our proposed framework. Thus, the throughput performance of the proposal is considerably higher compared with existing schemes in the underwater literature. The benefits in terms of the throughput of the proposed framework are analyzed as performance gain in Table 5. It is a comparative investigation between the proposed framework and underwater state-of-the-art techniques. The average throughput performance gain of W-GUN in terms of percentage is 10%, 33%, and 45% for the underwater literature including ES-VBF, HH-VBF and, VBVA respectively. The reason behind the better throughput is the utilization of underwater characteristics for identifying network dynamics. However, W-GUN considers underwater network dynamics, resulting in significant performance gains in throughput. These performance benefits are further evident in Figure 12 in a more scientifically understandable way. Here, the percentage gain and throughput observations are presented in close relation so that they can be analyzed relatively. This is helpful in validating the results shown in Table 5 and Figure 11. Thus, the proposed underwater framework provides higher throughput in underwater environments compared to the state-of-the-art techniques. Figure 11: Throughput versus the number of underwater nodes.
# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 2 Materials and Methods
### Evaluation Index  
This paper selects multiple indicators to evaluate the performance of the model for soybean weed detection, namely accuracy, recall rate, average precision, parameter amount, detection frame rate and parameter amount. The calculation of precision, recall rate and average precision is shown in the following formula:  
\[P=\frac{TP}{TP+FP} \tag{10}\]  
\[R=\frac{TP}{TP+FN} \tag{11}\]  
Here, \(TP\) refers to the number of weeds correctly detected by the model in dags_tcc dataset, \(FP\) refers to the number of weeds incorrectly detected by the model in dados_tcc dataset, and \(FN\) refers to the number of weeds not detected by the model in dados_tcc dataset.  
\[AP=\int_{0}^{1}P(R)dR \tag{12}\]  
\[mAP=\frac{\sum_{i=1}^{C}AP_{i}}{C} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of Categories** & **Narrow-Leaf Weed** & **Broad-Leaf Weed** & **Soybeans** \\ \hline Train & 6732 & 2588 & 2417 & 1727 \\ Val & 1922 & 754 & 664 & 504 \\ Test & 979 & 353 & 356 & 270 \\ Total & 9633 & 3695 & 3437 & 2501 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset category information table.  
Figure 6: There are three types of datasets. (**a**) Soybeans; (**b**) Broad-leaf weeds; (**c**) Narrow-leaf weeds.  
where \(C\) represents the total number of categories in the dataset; \(AP_{i}\) represents the detection accuracy of the \(i\)-th class, and the intersection ratio threshold of \(mAP\) is selected as 0.5 in this paper; For cross-to-union ratios greater than 0.5 and less than 0.95, their \(mAPs\) are calculated separately first, and then the mean value of these \(mAPs\) is calculated, i.e., mAP50:95. FPS is used to evaluate the real-time detection speed of the model. The larger the FPS, the better the real-time detection efficiency.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 3 Performance Evaluation
### Scenario 2--Infrastructure Network  
In this scenario, we consider an infrastructure mode network. We have a stationary access point that is placed at the center of the square field and all stations are moving randomly sending constant traffic of 60 Mb/s to the access point. The number of stations is increased gradually from 5 to 50.  
Figure 2 shows the overall physical throughput received at the access point. Ideal RAA performed better than the other algorithms due to its fast reaction to channel changes, while the two implicit feedback algorithms (Strale and Minstrel-HT) take more time to recover after the channel conditions change. On the other hand, the Strale algorithm performed slightly better than Minstrel-HT, because, instead of decreasing the rate, it tries to find an optimal size of the A-MPDU in which it provides a better throughput at the current rate when compared to stepping to a lower rate.  
Figures 3 and 4 show the FLR based on MPDU losses and A-MPDU losses, respectively. We notice high MPDU FLR values, because all of the stations are in the transmission range of each other. This increases the interference level when the node density increases. A-MPDU FLR is a false-positive rate increase in a fast changing channel. It happens when a transmitter decides to increase the rate for the next transmission, but the channel conditions deteriorate before sending the frame. Note that this condition also applies in the MPDU FLR when the channel conditions deteriorates during the transmission of the packet, this can be seen by inspecting the position of the MPDU losses in the BlockAck. Figure 5 shows the average MCS index values selected by the stations. We notice that the stations using the Ideal RAA selected higher MCS index values with almost the same MPDU FLR and lower A-MPDU FLR which explains the higher throughput achieved. All of these frame losses cause MutFed to have the worst performance among the tested algorithms, each 2 frame losses the rate is lowered, causing more channel occupancy and less data being sent.  
We noticed from Figure 4 that, when the node density increases (>20 for example) causing the interference level to increase, the gap of the A-MPDU FLR between RAAs relying on SINR and other RAAs is increased. This is due to the fact that the SINR values better reflect the channel condition.  
Figure 2: Overall throughput received at the access point.  
Figure 4: A-MPDU Frame Loss Ratio.  
Figure 3: MAC Protocol Data Unit (MPDU) Frame Loss Ratio.
# Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines
###### Abstract  
We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user's role while achieving good segmentation accuracy.  
semi-automatic segmentation; MR spine image; vertebral body; graph-based segmentation; correlation +
Footnote †: journal: Applied Sciences
## 4 Results  
### Volcanic Gas Composition  
Gas emissions at Mount Garet were measured at close proximity to the southeast crater. Therefore, over the non-continuous, 4-h measurement period, the multi-GAS consistently captured a strong plume signal (up to \(\sim\)70 ppm SO\({}_{2}\)). Background air-corrected CO\({}_{2}\) concentrations varied from \(<\)1 to \(\sim\)60 ppm (Figures 3 and 4). Rare plume batches yielded H\({}_{2}\)S concentrations above the 14% cross-sensitivity to SO\({}_{2}\) but occasionally reached in-plume concentrations up to \(\sim\)7 ppm (Figure 3). We recorded H\({}_{2}\) concentrations up to 3 ppm (Figure 3). The volcanic H\({}_{2}\)O signal (up to \(\sim\)3500 ppm; Figures 3 and 4) was intermittently resolvable from background noise caused by high humidity conditions characteristic of tropical environments.  
Derived volatile ratios (in the form of x/SO\({}_{2}\) ratios; where x is either H\({}_{2}\)O, CO\({}_{2}\), H\({}_{2}\)S, or H\({}_{2}\)) for Mount Garet were obtained in measurement windows where strong positive co-variations (R\({}^{2}\geq\) 0.70) were observed between SO\({}_{2}\) and the other volatile species measured (Figure 3). Interference-corrected H\({}_{2}\)S/SO\({}_{2}\) ratio and H\({}_{2}\)SO\({}_{2}\) ratios (0.13 \(\pm\) 0.01 and 0.03 \(\pm\) 0.01, respectively) remained constant throughout the entire measurement period. Likewise, CO\({}_{2}\)/SO\({}_{2}\) ratios varied narrowly between 0.65 and 1.07 (avg. 0.87 \(\pm\) 0.24). Due to variable mixing between atmospheric H\({}_{2}\)O and the magmatic plume, H\({}_{2}\)O/SO\({}_{2}\) measurements were more variable, ranging from \(\sim\)40 to \(\sim\)56 (avg. 47.2 \(\pm\) 5.7; Tables 1 and 2). Similarly, H\({}_{2}\)O/CO\({}_{2}\) ratios also showed wide variations across the entire 4-h window, reaching values as low as \(\sim\)40 and as high as \(\sim\)70. However, the best correlation (R\({}^{2}\) = 0.75) was found at the upper limit of that range, yielding a H\({}_{2}\)O/CO\({}_{2}\) ratio of \(\sim\)69 (Figure 4). The volcanic plume composition of Mount Garet was estimated at 95.9 mol% H\({}_{2}\)O, 1.8 mol% CO\({}_{2}\), 2.0 mol% SO\({}_{2}\), 0.3 mol% H\({}_{2}\)S, and 0.06 mol% H\({}_{2}\) (uncertainties reported in Table 2 are expressed as the standard error of the regression analysis and subsequent error propagation).  
Figure 4: On the left: scatter plot of H\({}_{2}\)O vs. CO\({}_{2}\) mixing ratio in the Mount Garet plume; the least-square regression line is shown as black dashed line on each plot (R\({}^{2}\) = 0.75). On the right: concentration (ppmv) time series of H\({}_{2}\)O (blue) and CO\({}_{2}\) (red) recorded at a frequency of 1 Hz; in black, SO\({}_{2}\) concentration (ppmv) is given as an analog for plume density during our measurement period.
## 3 Results
### Results Obtained at 100 mW (20 dBm) Input Power to the ARCM
#### 3.2.2 Local 10g-SAR  
The maximum local 10g-SAR in the considered exposure scenarios varied from 4.73 W/kg (ARCM on the head) to 6.20 W/kg (ARCM on the forearm) for the APD exposure scenarios and from 3.63 W/kg (ARCM on the chest) to 5.13 W/kg (ARCM on the forearm) for the CWED exposure scenarios (Table 5). The 10g-SAR spatial distributions in various exposure scenarios are presented in Figure 4.
# Fv-AD: F-AnoGAN Based Anomaly Detection in Chromatic Process for Smart Manufacturing
## 2 Related Work
### Unsupervised Learning for Anomaly Detection  
Anomaly detection is the task of identifying test data that do not match the normal data distribution identified during training. The approach for anomaly detection is derived from image analysis [13] and research papers exist on various application cases [10]. There are numerous challenges in detecting anomalies in a changing manufacturing process image. In general, algorithms are based on the assumption that there are structural differences between images with and without anomalies [14]. The algorithms of the anomaly detection technique are broadly classified as supervised and unsupervised learning. In general, supervised learning is often used when the presence or absence of anomalies in a given dataset is known, but there are few ways to objectively prove which datasets actually contain anomalies and how much they contain in a real factory manufacturing environment. As a result, the significance of anomaly-detection techniques based on unsupervised learning has emerged, and numerous studies have recently been conducted in this regard. In a related study, \"Deep Ant: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series\" [15], there is a research method that applies unsupervised learning to difficult-to-determine anomalous time-series data [16]. It is replaced by the LSTM-RNN structure to process time series data, and the application of the adversarial training method of GAN results in computational complexity [17].  
One of the unsupervised learning-based anomaly detection is the GANormaly paper [18], which is a methodology similar to AnoGAN.For problems with a lot of unlabeled data, it outperforms traditional approaches for semi-supervised anomaly detection. The GANnormal model is proposed to perform the process of creating an image and the steps of learning in latent space at once. Compared to the proposed paper, the model proposes performance improvement using adversarial auto-encode. Figure 2[15] shows a new unsupervised learning model called \"DeepAnt\" based on CNN, which successfully detects anomalies in time series data.  
Figure 1: Anomaly detection procedure in smart factory.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 5 Analysis of Escalator
### Performance Analysis  
#### 5.3.1 End-to-End Delay Analysis by the Overlap of Convergecast and Baseline Slofframes  
We analyze the average end-to-end delay of packets transmitted from a node to the sink in Escalator. The convergecast slotframe provides a consecutive timeslot schedule where nodes can send packets to sinks with minimal delay. However, the packet transmission delay may increase due to a collision caused by slotframe overlap. We calculate the average end-to-end delay of the packet transmission from a node to the sink considering the delay due to the slotframe overlap. The \(S_{Conv}\) and \(S_{Base}\) combined schedule is repeated for each Least Common Multiple (LCM) timeslot of both \(L(S_{Conv})\) and \(L(S_{Conv})\). We define \(r\), the number of times \(S_{Conv}\) is repeated during \(LCM(L(S_{Base}),L(S_{Conv}))\) timeslots, as in Equation (11).  
\[r=\frac{LCM(L(S_{Base}),L(S_{Conv}))}{L(S_{Conv})}. \tag{11}\]  
The end-to-end delay of packet transmission from node \(v_{i}\) to the sink using the convergecast slotframe is \(H_{v_{i}}\) timeslots. If a slotframe collision occurs while a packet is being delivered to the sink, the end-to-end delay of this packet increases to \(H_{v_{i}}+L(S_{Conv})\) timeslots. Let \(d\) be the number of packets delayed by collision during \(LCM(L(S_{Base}),L(S_{Conv}))\) timeslots. In Escalator, node \(v_{i}\) can send one packet to the sink in two cycles of the convergecast slotframe, so \(d\) cannot exceed \(r/2\). Each node transmits \(r-d\) packets to the sink during \(LCM(L(S_{Base}),L(S_{Conv}))\) timeslots by schedule collision, and the collision-induced delay increase occurs in \(d\) packets. Therefore, we sum up the delays in \(r-d\) packet transmissions and use them to calculate the average end-to-end delay. Equation (12) represents the average end-to-end delay of node \(v_{i}\).  
\[D_{e2e}(v_{i})=\frac{(r-2d)\times H_{v_{i}}+d\times(H_{v_{i}}+L(S_{Conv}))}{r-d }\quad\textit{timeslots}. \tag{12}\]