esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The other techniques were seen to have a comparatively lesser accuracy. [PERSON] et al. proposed a wrapper based methodology [12] for selecting the features. The features used are power spectral density features along with the AAR co-efficient and Hjorth parameters. These averages of these features are calculated and then compiled into a single vector. These features are then selected and subjected to different regression techniques and it has been seen that the Least Angle Regression (LARS) algorithm works better than Wilcoxon rank test. [PERSON] et al. [13] presented a statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure and automatically select the optimal subject-specific time segment. The autoregressive model and log variance are employed on the Common Spatial Patterns (CSP) for the feature extraction. These extracted factors are spatial and temporal correlated power spectral features. In the experiment, they only performed binary classification. We used a four-class motor imagery tasks in this research. [PERSON] et al. [14] proposed FDA techniques for reducing the linear dimensionalities, where the FDA and linear discriminant analysis (LDA) were combined. A graph that is data adaptive is created with the L1 or L2 norm constraints and then merged with the LDA approach for a better analytic solution. The experimental results are implemented on different datasets to demonstrate the effectiveness. It is seen to be effective in lower dimensions and small training datasets. [PERSON] et al. [15] used spatial filter techniques along with PCA. It has been seen that common average reference filter performs better than other well-known spatial filter techniques. However, the feature reduction using PCA did not improve the accuracy, but the performance of the classification was maintained. [PERSON] et al. combined an autoregressive model and sample entropy model [16] for extracting the features. The coefficients of these models have been used along with SVM and Radial Basis Function (RBF) for the classification. The obtained accuracy was higher than the individual autoregressive models; however, the obtained accuracies were lower than existing combined techniques. [PERSON] and [PERSON] [17] considered a deep learning approach based on convolutional neural network (CNN). CNN and their application to four-class motor-imagery based problem were analysed in the research. The experimental results are similar to more complex state-of-the-art EEG analysis techniques. [PERSON] et al. [18] proposed an approach that combines CNN and variational autoencoder (VAE) networks. Deep learning approaches were used in the experiment. The deep learning approach takes extremely large amount of data to perform better than other methods [17]. Hence, it is difficult to obtain better results in the deep learning approach. There will be lesser chance to improve the classification accuracy.
## 1 Introduction
Since the materials will be humanly consumed, they should be based on raw materials, generally referred to as safe (GRAS) [16]. Chitosan is a world-wide abundant and safe polysaccharide, with excellent antimicrobial and biocompatibility properties broadly incorporated in edible coatings and applied on fruit surfaces for preservation purposes [14; 17]. However, its hydrophilic nature is a weak moisture barrier that needs to be improved by the development of composites incorporating lipids such as essential oils and fattyacids [18]. Therefore, alternatives incorporating hydrophobic compounds to strengthen moisture barrier such as some essential oils, having antioxidant, antibacterial, and antifungal properties that preserve and protect food during the post-harvest of the fruit is an added-value strategy [19]. [PERSON] et al. [6] investigate the effect of alginate coating and cold storage on the bioactive compounds present in cape gooseberries [6]. [PERSON] et al. [20] studied the effect of apply gelatin-calcium chloride on cape gooseberry without Calyx, which provided a reduction of weight loss and fungal deterioration [20]. However, no reports using chitosan-_Ruta graveolens_ essential oil have been investigated for cape gooseberry storage under refrigeration conditions. The incorporation of Rue essential oil (_Ruta graveolens_ L.) could be an excellent option to complement the antimicrobial activity of chitosan. _Ruta graveolens_ L. essential oil (RGEO) components, such as the 2-undecanone, 2-nonanone, and monoterenes, have been recognized as antifungal components [21,22]. The incorporation of the RGEO components in the chitosan (CS) matrix improves barrier, antimicrobial, antifungal, and antioxidant properties, as previously demonstrated, especially to control _Colletotrichum gloesprioroides_ fungi, which allowed to prolong the stability in guavas harvested in Colombia [23]. In this research, our interest is to evaluate the effect on the physicochemical, antioxidant, and quality properties of cape gooseberries after the CS + RGEO coatings application. To the best of our knowledge, this is the first time that the application of CS + RGEO coatings is reported in cape gooseberries to evaluate the effect in quality aspects of cape gooseberry fruits.
## 5 Experiment and Results  
### Proposed Model  
After the data preprocessing phase, the subsequent experiments are divided into two parts: with the use of WordNet and without the use of WordNet. Afterwards, word embedding methods were used both with WordNet and directly with the RF, SVM and MLP classification algorithms. Since there is no need to use word embedding methods in the BERT algorithm, separate experiments were conducted both with and without WordNet. The purpose of using WordNet in the study was to investigate its contribution to classification success by reducing the feature vector space. The system architecture for the studies carried out is illustrated in Figure 8. This proposed architecture is composed of five main parts: dataset, data preprocessing, feature reduction with WordNet, word embedding, and classification, respectively.
## 2 Data and Methods
### Model Descriptions
#### 2.3.4 The VPM  
The VPM assumes that leaves and plant canopies are composed of photosynthetically active vegetation (PAV, mostly chloroplast) and non-photosynthetic vegetation (NPV, mostly senescent foliage, branches, and stems) [7]. Based on the conceptual partitioning of PAV and NPV within the canopies, the VPM was built as follows:  
\[\mathrm{GPP}=\varepsilon\times\mathrm{FPAR}\times\mathrm{PAR}, \tag{10}\]  
where FPAR is the fraction of PAR absorbed by the plant canopy, the product of FPAR and PAR is the PAR absorbed by the plant canopy (APAR), and \(\varepsilon\) is the LUE.  
The VPM acquires biome-specific \(\varepsilon_{\mathrm{max}}\) from the literature or from the relationship between \(\mathrm{NEE}\) and incident PAR; the temperature and water stress are chosen to down-regulate \(\varepsilon_{\mathrm{max}}\). Thus, in the VPM, \(\varepsilon\) is described as:  
\[\varepsilon=\varepsilon_{\mathrm{max}}\times\mathrm{T_{scaled}}\times\mathrm{W _{scaled}}\times\mathrm{P_{scaled}}, \tag{11}\]  
where \(\varepsilon_{\mathrm{max}}\) is the maximum LUE; and \(\mathrm{T_{scaled}}\), \(\mathrm{W_{scaled}}\), and \(\mathrm{P_{scaled}}\) are the down-regulation scalars for the efficiency of temperature, water, and leaf phenology on the \(\varepsilon_{\mathrm{max}}\), respectively.  
\[\mathrm{T_{scaled}}=\frac{\left(\mathrm{T_{a}-T_{min}}\right)\times\left( \mathrm{T_{a}-T_{max}}\right)}{\left(\mathrm{T_{a}-T_{min}}\right)\times\left( \mathrm{T_{a}-T_{max}}\right)-\left(\mathrm{T_{a}-T_{opt}}\right)^{2}} \tag{12}\]  
here, \(\mathrm{T_{a}}\) is the mean temperature for each of the eight days; \(\mathrm{T_{min}}\), \(\mathrm{T_{max}}\), and \(\mathrm{T_{opt}}\) are the minimum, maximum, and optimal air temperature for photosynthetic activities, respectively. If \(\mathrm{T_{a}}\) falls below \(\mathrm{T_{min}}\), \(\mathrm{T_{scaled}}\) is set as 0. In this study, the values of \(\mathrm{T_{min}}\), \(\mathrm{T_{max}}\), and \(\mathrm{T_{opt}}\) can be seen in Table 2.  
\[\mathrm{W_{scaled}}=\frac{1+\mathrm{LSWI}}{1+\mathrm{LSWI}_{\mathrm{max}}} \tag{13}\]  
here, \(\mathrm{LSWI}_{\mathrm{max}}\) is the maximum LSWI within the growing season.  
Calculation of FPAR depends upon a linear relationship with EVI. It is also a challenging variable in the VPM.  
\[\mathrm{FPAR}=\alpha\times\mathrm{EVI} \tag{14}\]  
generally, the coefficient \(\alpha\) is set to be 1.0 [7].  
\[\mathrm{P_{scaled}}=\frac{1+\mathrm{LSWI}}{2} \tag{15}\]  
during bud burst to leaf full expansion; \(\mathrm{P_{scaled}}=1\), after full leaf expansion.
## 2 Materials and Methods
### Methodology
#### 2.3.3 Pixel-Based Transformer (PB-Transformer)  
Crop classification uses the encoder part of the Transformer model [35], which is called PB-Transformer. The PB-Transformer model comprises an input module, encoder module, fully connected layer, and output module. The encoder module realizes feature extraction by stacking multiple multi-head attention mechanisms [36; 50]. ShortCut was used to obtain better results, and Layer Normalization was used to avoid overfitting. Finally, a fully connected layer is used for crop identification.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model Hyperparameters** & **Hyperparameters Value** \\ \hline BiLSTM layer & 1, 2, 3, 4, 5, 6, 7, 8, 9 \\ Dropout & 0, 10\%, 20\%, 30\%, 40\%, 50\% 60\%. \\ \hline \hline \end{tabular}
\end{table}
Table 3: PB-BiLSTM hyperparameters.  
In order to optimize the hyper-parameters of PB-Transformer's model, we conducted a search to determine the optimal number of self-attention heads from the options 2, 4, 6, and 8, the self-attention layers from the choices of 2, 4, 6, and 8, and the fully-connected layers within the range of 1024 and 2048.
## 4 Experiment and Results
### Visualization Analysis  
Figure 6 visually compares our DEPANet with competing methods in diverse images. The third column displays our DEPANet segmentation results. Figure 6 shows that DEPANet consistently delivers the highest accuracy in defect segmentation, enabling complete and precise delineation of defect areas. Our method effectively detects and segments defects across a range of sizes and morphological variations, demonstrating robustness to  
Figure 5: Comparison of quantitative metrics for different models on SD-saliency-900.  
both small and large defects. In the images shown in Rows 1-3, where the contrast between the defect regions and the background is low, existing methods exhibit two typical failure modes. Methods such as BASNet and MINet, being overly sensitive to texture variations, often misclassify background regions as defects. In contrast, methods such as 2 LSG and BC, due to their limited feature representation capability, frequently fail to fully segment the defect regions. For images in Rows 4-6, which contain finely detailed defects, certain models yield segmentation results with unclear boundaries. However, DEPANet produces results with sharp and well-defined contours, ensuring precise defect localization. In the last two rows of images, which contain complex backgrounds and challenging scenarios, most existing methods generate saliency maps with poorly defined edges and imprecise highlighting of defect regions. In contrast, DEPANet effectively distinguishes defect regions from the background, demonstrating superior segmentation capability.  
To further illustrate the role of edge information extracted by DEPP, we visualize the intermediate edge maps generated at different stages of DEPP in Figure 7. As shown in Figure 7, Stage 1 and Stage 2 focus on capturing fine-grained textures and noise-like contours, while other stages highlight more semantically meaningful and structurally consistent defect boundaries. These progressive representations demonstrate the hierarchical edge learning ability of our AFSFs and provide insight into how DEPANet progressively refines edge information to assist defect segmentation.  
Figure 6: Visualization comparison of different models on SD-saliency-900. (**1**) Input images, (**2**) Ground Truth (GT), (**3**) ours, (**4**) 2 LSG, (**5**) BC, (**6**) SMD, (**7**) MIL, (**8**) PFANet, (**9**) NLDF, (**10**) DSS, (**11**) R3 Net, (**12**) BMPM, (**13**) PoolNet, (**14**) PiCANet, (**15**) CPD, (**16**) BASNet, (**17**) SAMNet, (**18**) ITSD, (**19**) F3 Net, (**20**) MINet, (**21**) EDRNet, (**22**) DACNet, and (**23**) RCRR.  
Figure 7: Visualization results and edge visualization results at different stages of DEPANet.
## 3 Results  
### Compaction Curve  
The compaction curve displayed in Figure 8 provides valuable insights into the behaviour of kaolin under different compaction energies and moisture contents. By examining the curve, the relationship between dry density, compaction energy, and moisture content can be observed.  
The compaction curve shows the variations in dry density as a function of compaction energy, represented by the number of blows (15, 25, 35, 45 and 55 blows) and the moisture content in the range from 10 to 50%. Each combination of compaction energy and moisture content corresponds to a specific point on the compaction curve. At 15 blows, the dry density increases from 1.14 at 10% MC to a peak of 1.39 at 30% MC, then descends to 1.20 at 50% MC. At 55 blows, the dry density ranges from 1.31 at 10% MC to 1.61 at 30% MC, decreasing to 1.33 at 50% MC. The peaks observed on the compaction curve indicate the optimum moisture content (OMC) of the kaolin, which is at 30% moisture content in all cases studied. The OMC represents the moisture content at which the kaolin reaches its maximum dry density during the compaction process. This optimum moisture content is important to achieving the desired compaction properties and overall soil performance. The OMC effectively divides the compaction curve into two distinct sides: the dry side and the wet side. The basis for dividing the compaction curve is primarily to categorize soil adhesion behaviour into two stages: one strongly affected and the other weakly affected. The moisture content ranging from 10 to 30% represents the dry side, while the range of 30 to 50% represents the wet side.
## 1 Introduction
For example, [PERSON] studied the applications of eNose technology for the early detection of plant, animal, and human diseases [55]. [PERSON] et al. [56] developed an eNose with various MOX sensors types as a small portable device for air quality measurement. The sensor data are analyzed using Principal Component Analysis (PCA) and Partial Least Squares (PLS) to correlate sensor response to different pollutant concentrations. [PERSON] et al. [57] analyzed the performance of an eNose for synthetic flavor classification using eight different types of low-cost MOX sensors. They also used PCA for the processing and classification of the data to differentiate between two fruit flavors. In the field of gas leakage, [PERSON] et al. [58] developed an Internet of Things device to monitor liquified petroleum gas that could communicate and send warnings through a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gas** & **Power** & **Size** & **Specificity** & **Response Time** & **Price (USD)** & **Sources** \\ \hline Laser chromatography & High, 1 to 5 W & Big, 10–30 cm\({}^{3}\) & Single gas & Very Fast, \textless{1 s} & 1000–20,000+ & [39; 40; 41; 42] \\ Catalytic combustion & Moderate, 100 & Small, \textless{5 cm}\({}^{3}\) & Combustible gases only & Slow, 10–60 s & 50–500 & [43; 44; 45] \\ Electrochemical & Low, \textless{50 mW} & Small, \textless{5 cm}\({}^{3}\) & Single gas or few gases & Slow, 5–20 s & 50–500 & [46; 47] \\ Photoionization & High, 1–2 W & Medium, 5–15 & Multiple VOC gases & Fast, \textless{5 s} & 500–10,000 & [48; 49; 50] \\ Metal-oxide & Moderate, semiconductor & Small, \textless{5 cm}\({}^{3}\) & Multiple gases & Fast, \textless{10 s} & \textless{50} & [30; 51; 52] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different gas sensing technologies (Not exhaustive). Wi-Fi network. [PERSON] also presented a gas leakage detection approach that combines eNose data and thermal imaging using a complex multimodal data processing model [59]. The main goal of combining multiple sensors in an eNose implementation is to mitigate their selectivity issues through the combination of different sensor readings and data processing techniques [54; 60]. These devices can be seamlessly integrated into existing robotic platforms, thereby extending their functionality to include environmental monitoring tasks.
## 4 Discussion  
In the LINGO solution report presenting the control scheme shown in Table 8, only the 0/1 variable \(x_{(6,1),(6,2)}^{\text{bus}}\) equaled to 1, which indicates that there were car green wave bands on each road segment in the test road network, and so were bus green wave bands except the road segment between (6, 1) and (6, 2). This was also consistent with the bandwidth results shown in Table 8. The reasons for the bus bandwidth on the road section between (6, 1) and (6, 2) being 0 are as follows. Due to constraints of factors such as road segment length, vehicle travel time, bus dwell time, traffic light time, etc., cars and buses cannot simultaneously obtain green wave bands that meet the limit requirements on the road segment between (6, 1) and (6, 2). At the same time, since the weighting coefficients of the car bandwidths on this road segment are relatively large, the green wave band was allocated to cars instead of buses.  
According to the LINGO solution reports for the first comparative study, the objective function values (the sum of weighted bandwidths) under optimizable phase sequence and fixed phase sequence schemes 1, 2, and 3 are 2378.660, 2171.864, 2044.018, and 2032.889, respectively. Compared with the fixed phase sequence schemes 1, 2, and 3, the total weighted bandwidth generated by the presented model increased by 9.5%, 16.4%, and 17%, respectively. It can be observed that the optimizable phase sequence contributes to obtaining larger bandwidths.  
As can be seen from Table 11, regardless of which fixed value of the common cycle time was taken, the model with constraint relaxation could always achieve the global optimal solutions, while the model without constraint relaxation failed to obtain a feasible solution when the common cycle time was fixed at 100, 101, 102, or 103 s.
## 2 Physically Distributed Logically Centralized (PDLC) SDN Architecture and Components
### Database  
A distributed control plane's most essential component is the Database Management System, as shown by the yellow box in Figure 2. This database serves as the repository for all intra- and inter-domain information, constituting the core of each controller. It can be leveraged as a means of information exchange among controllers, eliminating the need fora specific communication protocol. Various SDN solutions surveyed utilize either relational (SQL) or NoSQL databases [6].  
A significant challenge arises in preserving and disseminating the network state within the distributed control plane. A non-distributed database may exhaust system memory. Alternatively, the number of network events (produced by the database) or the amount of work necessary to manage them could increase to the point where they overwhelm a single control plane instance's CPU. The distribution database framework allows the control plane to scale to accommodate large networks and withstand controller and network failures [22].
## 8 Performance Evaluation  
We have evaluated the proposed LoWMob scheme by developing a complete simulation in Qualnet v4.5 and through numerical analysis. The terrain area is 2,500 \(\times\) 2,500 m\({}^{2}\). A total of 50 SNs are deployed in an 8 \(\times\) 8 logical grid. The main reason of dividing the whole area into a grid is to examine the MN behavior at each step. We have used the random way point mobility model and the fluid flow mobility model. The minimum speed of MN is 1 m/s, and the maximum speed varies to 20 m/s, 25 m/s, 30 m/s, and 35 m/s. The MN pause time is 30 sec. AODV is used as a routing protocol. The simulation is run for 500 seconds and there are 20 simulations run.  
The performance metrics of interest are (a) End-to-End delay: delay of packets transmitted from MN to the GW, and the number of packets that are transmitted from one MN to the other MN; (b) packet success ratio: the number of packets successfully received by the GW, out of the ones that are transmitted by a MN; and for the communication between multiple MNs packet success rate is the number of packets that are successfully received by a MN out of the ones that are transmitted by another MN (c) hand off overhead.
## 2 Results and Discussion
### Optimization of DART-HRMS/MS Conditions  
DART desorption temperatures were evaluated over a range from 250 to 450 \({}^{\circ}\)C. Testing was performed using SPMESH sheets following extraction of IBMP spiked into saturated NaCl solution at 40 ng/L. The optimal temperature for IBMP response was at 350 \({}^{\circ}\)C showed a 10-fold signal increase as compared to the previous setting of 450 \({}^{\circ}\)C (data not shown). The lower response at temperatures below 350 \({}^{\circ}\)C was likely due to incomplete thermo-desorption of the analyte from the SPMESH sheet. The decreased response at higher temperatures was likely due to increased fragmentation of the precursor ion reducing the amount of the target mass reaching the MS detector, as has been reported for other DART-MS analyses, e.g., of n-hexadecane [18]. The pressure setting for the VAPUR Interface pump was also evaluated, with maximum signal intensity (22% increase at 5 ng/kg as compared to previous work) observed at a setting of (9.5 \(\times\) 10\({}^{4}\) Pa). In previous work on SPMESH-DART-MS when the pump was set to the highest setting (8.8 \(\times\) 10\({}^{4}\) Pa), a significant number of ions were evacuated from the VAPUR Interface without entering the vacuum entrance chamber of the mass spectrometer [19]. Conversely, at a low setting, less ions are sampled from the reaction zone, limiting signal intensity.  
Mass transitions, collision energies, and analytical cycle time were optimized on the Orbitrap-MS to further improve sensitivity and selectivity [20]. A new MS-MS transition was identified for d\({}_{3}\)-IBMP and collision energies were optimized (25 eV to 17 eV) for both IBMP and d\({}_{3}\)-IBMP. An isobaric interference was noted at the previous transition (_m/z_ 170 \(\medmedown\)_m/z_ 127.0818) which was resolved at the new transition (_m/z_ 170.1351 \(\medown\)_m/z_ 128.0896). Scan density--a critical parameter for sensitivity [20; 21]--was increased by reducing mass resolution to from 140 K to 70 K, reducing automatic gain control (AGC) from 10\({}^{6}\) to 10\({}^{5}\), and reducing injection time from 250 ms to 100 ms. These changes more than doubled the sampling rate. In combination, the improvements to the DART-MS  
Figure 2: Comparison of reported relative standard deviation values from previous work ([PERSON] et al., 2020) [13], samples analyzed with improved SPMESH extraction device, and samples analyzed using improved SPMESH extraction device and optimized conditions (**a**). Comparison of analytical limits of detection from (i) [PERSON] et al., 2020 [13], (ii) current work with optimized sample prep and new device, and (iii) improved device, optimized sample prep, and optimized MS/MS parameters (**b**).  
parameters led to increased repeatability (5% RSD) and significant decreases in the limit of detection (<0.5 ng/kg) (Figure 2).