esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Comprehensive Discussion
Xception 1 cannot work well for a small image size. It is reasonable because Xception 1 cannot capture enough features for its large input size of 299 \(\times\) 299 \(\times\) 3 and its high layer number of 170. For the models of Type 2--with resizing the original images as the input images, they give similar results and are all better than machine learning models with the best estimates of the Hurst exponent as the only feature. However, the computational cost of model Type 2 is much higher than that of its corresponding model Type 1 because model Type 2 needs resizing and always performs on larger images. The success of the pretrained models illustrates that 2D FBM images, even being random characteristics, can still be learned from concrete lines and/or curves to abstract patterns through low-level to high-level layers. With the success of pretrained networks, on the other hand, we establish a regular structure of network layers according to the characteristics of 2D FBM images. It turns out that our proposed simple sequential deep learning model does work well for 11 and 21 classes under the images of size 32 \(\times\) 32 \(\times\) 1. In addition, our proposed model is the best in terms of the classification rate and computational cost. GoogleNet 1 is the second. For further applications to the estimation of the Hurst exponent, we also want to know whether these promising deep learning models are still equivalently better than the MLE in terms of the mean absolute error (MAE) and mean square error (MSE) as ordinal classification metrics, especially as numerical ordinal classification metrics like our dataset. Since our dataset is balanced and was stratified for the five-fold cross-validation, as well as all classes are equally spaced, each corresponding to a number, we simply chose the general, not weighted, MAE and MSE as our comparison metrics. Table 12 shows the comparison among four methods--the MLE was estimated on two datasets (FBSs and FBIs)--in terms of the MAE and MSE. It is obvious from Table 12 that our model is also the best and the second is GoogleNet 1 in terms of the MAE and MSE. In addition, the results of 21 classes are better than those of 11 classes for our model and GoogleNet 1. Therefore, we choose our proposed model and GoogleNet 1 with 21 classes as tools for estimation or feature transformation and further compare both models with the efficient MLE in the following Applications section.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 2 Framework of Linear Wireless Sensor Networks
### Hypothesis on Energy Management Techniques in LWSNs  
Energy management techniques commonly employed in traditional WSNs, particularly those targeting the optimization of network topology and routing layers, may not be as effective in LWSNs due to the constrained and unique linear topology. We hypothesize that the energy management techniques that involve the network layer will be most impacted by the differences between traditional WSNs and LWSNs. Consequently, only WSN energy management techniques that directly affect the network layer are likely to have a considerable impact when applied to LWSNs, requiring a rethinking of strategies to accommodate the network's linear structure. Also, the most impactful energy management techniques in LWSNs will be those that are capable of reducing the number of multi-hop communications in LWSNs.  
In this study, we focus only on thin LWSNs. Instead of focusing on a hierarchical network structure, such as those found in clustered networks, our interest lies in flat LWSNs, where all sensor-generated messages are relayed in a multi-hop fashion toward the sink node. The following sections present a comprehensive review of the green strategies and energy management techniques used in traditional WSNs and evaluate their applicability to LWSNs. By categorizing these techniques into node energy reduction, network energy balancing, and energy harvesting, we analyze the effectiveness of every technique in each of the categories when applied to the specific requirements of LWSNs.
## 2 Background  
We employed a combination of multi-date radar (PALSAR-2 and Sentinel-1) imagery and single date optical (Sentinel-2) imagery. Optical sensors are \"passive,\" meaning they measure light (visible and infrared) emitted by the sun that has reflected off the Earth's surface. Synthetic aperture radar (SAR) sensors, on the other hand, are \"active,\" meaning that they emit microwave energy and measure its backscatter from the Earth's surface [27]. Microwave energy, unlike visible and infrared light, penetrates cloud cover, affording radar sensors \"all-weather\" capability [28].  
SAR imagery has been described as a perfect complement to optical imagery for several reasons [24]. First, as active sensors, SAR sensors can collect data at nighttime [27] and are not impeded by cloud cover. Second, radar and optical sensors collect data in different, complementary bands of electromagnetic energy. Unlike reflected light measured by optical sensors, SAR backscatter is affected by standing water, soil inundation, surface roughness, and plant structure, biomass, and moisture content [24; 27; 29; 30].  
SAR sensors emit and detect microwave energy in the L-band (longest wavelength, lowest frequency), C-band, or X-band (shortest wavelength, highest frequency). The PALSAR-2 sensor measures backscatter in the L-band, meaning that it penetrates vegetative canopy and thus is sensitive to soil background, soil moisture, and standing water [26; 31]. Co-polarized PALSAR-2 imagery is particularly useful for detection [30] and differentiation of wetland classes due to an enhanced double bounce effect from water surface and tree trunks [31]. Cross-polarized PALSAR-2 imagery, on the other hand, is sensitive to biomass, making it useful for distinction of woody from herbaceous vegetation [32]. The Sentinel-1 sensor measures backscatter in the C-band and provides information about vegetation structure and texture [33].
## 3 Results
### Solution Analysis
Drop Analysis  
After the solution analysis, we performed a drop analysis. This way of operating takes advantage of the configuration of the SPCEs, which have all the electrodes (reference, working, and auxiliary) printed on the same planar support. Thus, instead of dipping the electrode into a solution, we can deposit a single drop that covers all the electrodes and on which we can perform the analysis. The drop has a definite volume: for aSPCE1, it was 60 uL, while for aSPCE2, it was 90 uL due to its higher WE surface. After every analysis, we washed the electrode with ultrapure water, and then, before the following analysis, we conditioned the aSPCEs by immersion into the blank solution under stirring for a few seconds. This procedure ensures good interaction of the drop with the electrode-active surface. Drop analysis could be very useful because it can provide a lot of measures with small volumes of the sample. For a better analysis, in this case, we have to set an equilibration time of 90 s for aSPCE1 and 150 s for aSPCE2. This time allows the correct diffusion of the analyte to the working electrode surface, enhancing the ability of the aSPCEs to detect DCF. Similar to the solution analysis case, the values of LoD and LoQ were estimated by the standard deviation of the intercepts defined by ordinary linear regressions. Figure 9a shows the plots used for the aSPCE1, while Figure 9b shows the plots used for the aSPCE2. The peak heights used for the linear fits were estimated by baseline subtraction. Figure 9: **(a)** Peaks obtained by drop DPV analysis with aSPCE1 on 25–120 ug L\({}^{-1}\) DCF solutions (1.0 \(\times\) 10\({}^{-1}\) mol L\({}^{-1}\) PB, pH 2.0; E vs. Ag/AgCl); the inset graph shows the peaks after baseline subtraction; **(b)** Peaks obtained by drop DPV analysis with aSPCE2 on 29–147 ug L\({}^{-1}\) DCF solutions (1.0 \(\times\) 10\({}^{-1}\) mol L\({}^{-1}\) PB, pH 2.0; E vs. Ag/AgCl); the inset graph show the peaks after baseline subtraction; **(c)** linear fit of signals recorded by drop DPV analysis with aSPCE1 on 25–120 ug L\({}^{-1}\) DCF solutions; **(d)** linear fit of signals recorded by drop DPV analysis with aSPCE2 on 29–147 ug L\({}^{-1}\) DCF solutions. The value of the LoD and LoQ estimated for aSPCE1 and aSPCE2 were, respectively, 9 \(\upmu\)g L\({}^{-1}\) and 28 \(\upmu\)g L\({}^{-1}\), and 15 \(\upmu\)g L\({}^{-1}\) and 45 \(\upmu\)g L\({}^{-1}\). The first observation is that both the aSPCEs have higher values of LoD and LoQ for a drop analysis than for a solution analysis owing to the greater variability in peak heights. The aSPCE1, however, continues to provide better results than the aSPCE2, having the ability to detect lower concentrations of DCF.
## Result and Discussion
### Transmission Electron Microscopy  
In the TEM images, the dark lines represent the clay layers, and the brighter regions represent the polymer matrix. Figure 4A shows that the pure PS particles were transparent spheres with diameters of approximately 130 nm. In contrast, in Figure 4B-D, the clay layers appear to have an exfoliated and/or intercalated arrangement with good dispersion in the polymer matrix. Meanwhile, in Figure 4B,C, mainly exfoliated structures (marked by yellow arrows) were seen in the PS matrix, indicating a high degree of exfoliation and dispersion of the clay layers in the matrix. Furthermore, some individual clay layers appeared to be either located on the surface of the PS particles or partially embedded inside the PS particles as the OMMT load increased to 3 wt %. This morphology was also observed by other researchers [35]. In appropriate amounts (1-3 wt %), the OMMT can be effectively exfoliated to form individual layers due to the strong interactions between the OMMT and the polymer matrix. However, with an increase in the clay content, many intercalation structures (marked by red ellipses) were observed, as shown in Figure 4D. This result suggested that excess OMMT can lead to ineffective exfoliation and the appearance of heterogeneous stacks of clay layers due to adhesion of the nanostructures. Furthermore, all the PS-OMMT nanocomposite samples appeared to have particle sizes smaller than pure PS. This decrease in the size of the nanocomposite particles can be attributed to the increased dispersion of clay in the PS matrix and the increase in SLS concentration, resulting in the formation of more surfactant micelles.
# Buckwheat Disease Recognition Based on Convolution Neural Network
## 3 Materials and Data Processing  
We have established a disease database for buckwheat. The disease images were taken in rural farmland by a Canon EOS 90D digital camera from 2017 to 2019. Buckwheat diseases mainly occur in the leaves. Their occurrence depends on many factors, such as temperature, humidity, rainfall, variety, season, nutrition, and so on. The Chongqing buckwheat industry innovation team of China has carried out extensive research activities in Chongqing and Sichuan Province. In Chongqing, the sampling areas are mainly located in Weituo farm of Hechuan district, Xiema farm of Beibei District, Banxi farm of Youyang District, Fengan farm of Wanzhou District, and Zhongyi farm in Shizhu District; in Sichuan the sampling areas are mainly located in Liangshan, involving Jinqu Township in Zhaojue District, Shaluo Township in Butuo District, Lami Township in Leibo District, and Nanwa Township in Jinyang District. From February 2017 to November 2019, 7230 buckwheat disease images were collected from buckwheat fields. The images with different backgrounds  
Figure 1: Typical convolution neural network structure.  
were collected in real scenes. In order to obtain a group of representative images as much as possible, the images were collected in spring and autumn, and the weather conditions included \"less cloud\", \"cloudy\", and \"overcast\". These steps improve the robustness of the model.  
The database contains eight types of disease images, buckwheat spot blight, buckwheat sclerotinia, buckwheat seedling blight, buckwheat ring spot, buckwheat downy mildew, buckwheat brown spot, buckwheat virus disease, and buckwheat white mold. Sample images of each class have been depicted in Figure 2a-h. 5000 images have been selected in this work, which includes 500 images for each disease and 1000 images without disease. The training data set consists of 400 images for each disease and 800 disease-free images. The remaining were used as the test set. A 5-fold cross-validation was used for the evaluation.
# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 5 Discussion
[44] used an ROC curve to study the performance of PCA-MLP during the authentication of arabica coffee. The tables presented above show that vegetation indices (VIs) are also used to determine crop stresses. Vegetation indices are based on mathematical formulas that are derived from spectral information. In application, VIs have been used to compare with a built model or for masking an image. Each VI describes a different purpose. For example, normalized difference vegetation index (NDVI) is a vegetation index that can be used to assess the impacts of drought on vegetation. NDVI values above 0.6 generally represent dense vegetation, while values in the range from 0.2 to 0.5 commonly appear in aging plants, shrubs, and meadows [136]. The equations for different VIs are presented in Table 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Purpose** & **Formula \({}^{*}\)** & **No. Eq.** \\ \hline Broadband greenness & NDVI = \(\frac{\text{R800}-\text{R800}}{\text{R800}+\text{R800}}\) & (6) \\  & SRI = \(\frac{\text{R800}}{\text{R800}}\) & (7) \\  & EVI = \(2.5\times\left(\frac{\text{R800}-\text{R800}}{\text{R800}+\text{R800}-(7.5 \times\text{R450}-1)}\right]\) & (8) \\  & ARVI = \(\frac{\text{R800}-\text{R800}-(2.8\times\text{R800}-\text{R450})}{\text{R800}+( 2.7\times\text{R800}+\text{R450})}\) & (9) \\ Narrowband greenness & Red edge NDVI = \(\frac{\text{R750}-\text{R705}}{\text{R750}+\text{R705}}\) & (10) \\  & Modified red edge NDVI = \(\frac{\text{R750}-\text{R705}}{\text{R750}+\text{R705}-(2\times\text{R445})}\) & (11) \\  & Modified red edge SRI = \(\frac{\text{R750}-\text{R445}}{\text{R705}-\text{R445}}\) & (12) \\  & VOG REI 1 = \(\frac{\text{R740}}{\text{R740}}\) & (13) \\  & VOG REI 2 = \(\frac{\text{R740}-\text{R747}}{\text{R740}}\) & (14) \\  & VOG REI 3 = \(\frac{\text{R740}-\text{R747}}{\text{R740}}\) & (15) \\ Light use efficiency & PRI = \(\frac{\text{R831}-\text{R851}}{\text{R750}}\) & (16) \\ Dry or senescent carbon & PSRI = \(\frac{\text{R860}-\text{R800}}{\text{R800}}\) & (17) \\ Canopy water content & WI = \(\frac{\text{R8900}}{\text{R800}}\) & (18) \\ \hline \hline \end{tabular}  
* Note: NDVI = normalized difference vegetation index; SRI = simple ratio index; EVI = enhanced vegetation index; ARVI = atmospherically resistant vegetation index; VOG REI = Vogelmann red edge index; PRI = photochemical reflectance index; PSRI = plant senescence reflectance index; and WI = water index.  
\end{table} 
Table 8: Spectral vegetation indices for crop stress investigation (Reprinted/adapted with permission from Ref. [95]. 2023, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]). In the equations, R corresponds to the reflection value obtained using HSI/MSI.
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 3 mm-Wave-Based Vehicular Communication Model
### Blockage Probability  
Various sizes of vehicles at a road can cause blockage in mm-Wave links of V2V communications. Previously, the impact of vehicles as obstacles have been explored at their height point of view since  
Figure 3: 2D-Poisson Point Process (PPP) on multiple lanes locates vehicles along lanes. Vehicle density can be different to the beam coverage that is decided by beam-forming gain.  
Figure 2: Beam directions by sectorized antennae of Tx and Rx. According to beam direction of a Tx and Rx, different antenna gain causes 3 different radio ranges.  
the vehicles with different heights can affect transmitting signals by obstructing the Fresnel zone. In contrast to the DSRC spectrum, 5.9 GHz, mm-Wave has a very small Fresnel zone that is proportional to wavelength that determines diffraction characteristics. For example, zone radius of 5.9 GHz is 3 m while only 1 m at 60 GHz if a communication pair is apart by 500 m. In addition, the zone radius is proportional to the square root of distance. The zone radius converges into zero as the distance comes below 200 m at the mm-Wave link which implies that there is no effect from invisible obstacles in LOS considering the very short length of V2V links (less than 100 m).  
As can be seen in Figure 4, vehicles closer to an origin can block signals from vehicles outside. In other words, the vehicles located on \(d_{2}\) and \(d_{3}\) regions may not have LOS toward the origin especially in higher node density. Based on lanes and node density, the LOS probability of each vehicle can be derived as follow.  
\[\theta=\tan^{-1}\left(\frac{\sqrt{d_{2}^{2}-iL_{w}}}{iL_{w}}\right) \tag{15}\]  
At a lane, \(i\), block probability \(P_{b_{i}}\) can be calculated by road length and vehicle density.  
\[P_{b_{i}}=\frac{L_{\varphi}\lambda iL_{w}\tan(\theta)}{iL_{w}\tan(\theta)}=L_ {\varphi}\lambda, \tag{16}\]  
where the \(L_{\varphi}\) is vehicle length.  
Accordingly, block probability of a vehicle on a \(N\)-th lane is,  
\[P_{b}(N)=1-\prod_{i=1}^{N-1}\big{(}1-P_{b_{i}}\big{)}. \tag{17}\]  
Large vehicles such as trucks and buses typically cause blockage on the road compared to normal size vehicles; average height of the large vehicles is 3.35 m while one of the normal vehicles is 1.5 m. Average proportion of the large vehicles is about 15% of total vehicles. Accordingly, the blockage model can be built by PPP of the large vehicles which are independent of another PPP of the normal vehicles.
# An Intelligent Parking Management System for Urban Areas
## 3 Description of the System
### Detection of the Passage of Vehicles
Lastly, a reading of the status of both sensors is taken, in the expectation that sensor 1 has been deactivated before sensor 2, which means that the vehicle has entered the parking area. To detect a vehicle leaving the parking area, the same process is followed in reverse: first sensor 2 has to be activated and then sensor 1. In Figure 9 we can see the role that the timer plays in detecting vehicle entry; in this case Timer1 (T1), associated with Sensor1 (S1) has been activated after an Off signal from S1. Therefore, it is not counted as an attempted entry since the vehicle would have had to reach a speed of more than 20 km/h. The \"vehicle detection\" event, both at the entry and exit control points, is notified by sending a data frame from the sensor node to the gateway node, then on to the data centre. Following the data frame structure described in Section 3.2 and shown in Figure 6 for notification of entry or exit of a vehicle, the data packet sent by the sensor node will be structured as shown in Figure 10: a header of six bytes with the values \(53_{\text{hex}}\), \(45_{\text{hex}}\), \(45_{\text{hex}}\), \(53_{\text{hex}}\), \(47_{\text{hex}}\), and \(52_{\text{hex}}\) (the word SENSOR) followed by the sensor node identifier, the sensor identifier, the number of bytes in the data field of the frame, which in this case is 1 and, finally, the data field containing the value \(1_{\text{hex}}\) if notifying vehicle entry or a \(0_{\text{hex}}\) if notifying vehicle exit. Figure 10: Structure of data packet sent by the sensor nodes to notify vehicle entry or exit. Figure 9: Sequence diagram showing a failed attempt to enter.
## 3 Results and Discussion
### Suitability of Sorbent Materials  
The suitability of the sorbent tubes was assessed by measuring the recovery yield (for hydrocarbons and halogenated hydrocarbons: ratio of the measured and the spiked content at day 0 (D0) expressed as a percentage, for the sulphur compounds: control of the breakthrough using two tubes in series) and the storage stability over a period of maximum 14 days. The storage stability is defined as the change in content for a given compound as determined on the different storage days compared to at the start of the stability test. Each data presented below is the average of 2 or 3 measurements; except at D0 where 4 to 6 replicates have been analysed.  
As expected, some of the targeted compounds with very low boiling points (BP); methane (BP \(-\)162 \({}^{\circ}\)C), ethane (BP \(-\)88 \({}^{\circ}\)C) and hydrogen sulphide (BP \(-\)61 \({}^{\circ}\)C) were not recovered in the conditions used in this study. These compounds can only be trapped at low temperatures (not practical to implement onsite) and on very strong sorbents. Even methanol (BP 64 \({}^{\circ}\)C) and methyl-mercaptan (BP 6 \({}^{\circ}\)C) were not recovered Other analytical methods should therefore be used for these compounds when performing hydrogen purity analysis. Fortunately, there are already well-established methods for these compounds (e.g., optical methods or other GC methods) that allow their quantification at the level required in ISO 14687-2.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Limitations  
While versatile, our proposed integrated PSO and HE method grapples with inherent variability in medical images, including factors like resolution, noise levels, and modality-specific traits. Recognizing these challenges, the method's adaptability across diverse medical imaging scenarios calls for nuanced optimization tailored to specific image intricacies.  
Like many optimization-driven techniques, our method's performance relies on well-calibrated parameter settings, especially within the PSO algorithm. Sensitivity to these parameters emphasizes the need for meticulous fine-tuning across datasets and medical imaging contexts. Future research may explore adaptive strategies for parameter adjustment to enhance flexibility and integration across varied medical imaging scenarios.  
The integration of PSO with HE introduces computational complexity. While excelling at segmentation accuracy, the associated computational overhead raises considerations for real-time applications and resource-constrained environments. Future optimizations, such as parallelization or algorithmic refinements, are imperative to balance accuracy and computational efficiency.  
Collaborative Adaptability for Overlapping Structures  
Our method encounters unique challenges in chest X-ray images, where overlapping structures challenge traditional image processing methods. Particle swarm optimization (PSO) emerges as a strategic tool to address these intricacies, as it dynamically explores the solution space and adapts to the nuanced features of chest X-ray images.  
The swarm intelligence inherent to PSO becomes a key ally in overcoming challenges related to overlapping structures. The collaborative and exploratory nature of the particle swarm enables efficient optimization, even in complex and overlapping regions. By harnessing the collective intelligence of particles, the algorithm navigates intricate spatial relationships, ensuring that overlapping structures do not hinder the segmentation process.  
PSO's ability to balance exploration and exploitation proves crucial to address the challenges posed by overlapping structures. The algorithm's iterative nature allows it to explore diverse solutions while exploiting promising regions, which prevents entrapment in local minima. This dynamic balance contributes to the robustness of our proposed method, especially in regions where overlapping structures demand adaptive and nuanced segmentation approaches.