esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Circuit Design
### Analog Front-End
#### 3.1.4 Line Buffer and Multiplexer  
A circular shift register, controlled by a \(625\) kHz clock (\(ck_{mux}\) in Figure 1), sequentially enables each amplifier to access the common data lead; each pass gate switches on both the rising and the falling edge of the multiplexer clock, avoiding clock transitions in the middle of the sampling window. As in this topology each amplifier has to drive a long routing line connecting all the pass-gate outputs, a class-AB buffer is needed to boost the line. To save power, each buffer is switched off when the corresponding amplifier is not selected and turned on when the previous amplifier is selected (_i.e._, one clock edge before). In this way, at any time, only two buffers are powered, draining about \(25\)\(\mu\)A each from the supply. Further amplification is provided before sampling by a variable-gain amplifier (VGA) added to the amplification chain at the multiplexer output. The gain can be digitally varied between \(1\) to \(8\) by means of two auxiliary control bits.
## 5 Numerical Experiments
### Impact of IoT-SBL Intersection Efficiency  
Intersection efficiency is another key indicator for evaluating the performance of the signal intersection, primarily reflecting the capacity for emitting vehicles during the green light cycle. Like the analysis of delays, it assesses throughput efficiency at various saturation levels under two different strategies  
Figure 8a compares the throughput efficiency of various IoT-SBL strategies with different capacities against the bus lane strategy. The blue line indicates the performance of the bus lane strategy, while the other colored lines represent the IoT-SBL strategies with varying capacities. It is clear that when the saturation level of the intersection is below 0.7, both strategies achieve a throughput efficiency of approximately 95%, indicating that the current signal intersection can effectively accommodate the vehicle flow. However, as saturation levels rise, the throughput efficiency of the signal intersection under the bus lane strategy declines linearly. By the time saturation reaches 0.97, the efficiency plummets to below 50%. In contrast, the IoT-SBL strategy manages to sustain the signal intersection's throughput efficiency at around 90%, even under these high saturation conditions.  
Figure 8b further underscores the effectiveness of the IoT-SBL strategy in enhancing the throughput efficiency of the signal intersection. It also reveals that bus departure frequency has a significant impact on the performance of the IoT-SBL strategy. Notably, the effectiveness of the IoT-SBL strategy increases as the frequency of bus departures decreases. First, this emphasizes the importance of IoT-SBL capacity for optimizing signal intersection  
Figure 8: Comparing intersection efficiency at various saturation levels: (**a**) Different IoT-SBL volumes; (**b**) Different bus departure frequencies.  
throughput efficiency. At lower saturation levels, the IoT-SBL strategy is less engaged, so the capacity does not significantly affect throughput efficiency. For instance, at a saturation of 0.1, the improvement in signal intersection throughput efficiency is merely 2.02% across all scenarios. In contrast, at a saturation level of 0.97, the enhancement in throughput efficiency ranges from 86.22% to 100.11%, demonstrating that proper IoT-SBL capacity is crucial for maximizing the improvement in signal intersection throughput efficiency.  
Additionally, bus departure frequency also impacts the throughput efficiency of the signal intersection. Like the previous observation, the effect on throughput efficiency enhancement is minimal at low saturation levels. However, at a saturation of 0.97, the degree of enhancement in signal intersection throughput efficiency varies from 41.22% to 93.62%, representing a substantial range of 52.4%. This indicates that bus departure frequency plays a significant role in influencing the improvement of signal intersection throughput efficiency.
## 3 Results and Discussion
### Principal Component Analysis and Hierarchical Cluster Analysis  
The identified 73 volatile compounds were used as variables to perform the principal coordinate analysis (PCoA, Figure 7a) and hierarchical clustering analysis (HCA, Figure 7b). The clustering results of PCoA were evaluated by PERMANOVA in vegan, which showed that the processed XYMJ green tea samples were divided into two clusters (PERMANOVA \(R^{2}=0.976\), \(p<0.001\)). Cluster-1 consisted of the tea shoots and spreading samples, and Cluster-2 consisted of the remained XYMJ green tea samples from fixing to drying. The results indicated that fixing was the most important manufacturing process for the aroma formation of XYMJ green tea. A total of nine volatile compounds with a variance importance value (VIP) > 1 might contribute to separating the processed XYMJ green tea samples. Linalool had the highest VIP (4.50), followed by \(\beta\)-myrcene (3.75), nonanal (2.99), methyl salicate (2.39), \(\beta\)-ocimene (2.28), (E)-\(\beta\)-ocimene (1.86), trans-furan linalool oxide (1.75), gerani (1.42) and D-limonene (1.06).  
Figure 6: Changes in the concentrations of \(\beta\)-ocimene and (E)-\(\beta\)-ocimene (**a**), \(\beta\)-myrcene (**b**), cis-\(\beta\)-farnesene and (Z,E)-\(\alpha\)-farnesene (**c**), D-limonene (**d**), (+)-\(\delta\)-cadinence (**e**) and copaene (**f**).
## 2 Materials and Methods
### Chl-\(a\) Retrieval Indexes  
The Chl-\(a\) retrieval methods for satellite imagery broadly fall into four groups: empirical, semi-empirical, analytical, and semi-analytical [48]. In this study, we focus on semi-empirical methods. Semi-empirical models analyze the inherent optical properties (IOPs) of optically active constituents (OACs) by integrating water spectral theory with statistical techniques [13]. They offer ease of implementation, reproducible results, and reasonable accuracy without overfitting risk, all achieved by selecting the most suitable band combinations for retrieving water quality parameters [50]. Here, the choice of semi-empirical methods was also dictated by the limited availability of in situ matchup data for our study site.  
Upon conducting an extensive literature review, 27 commonly used semi-empirical Chl-\(a\) indexes were selected. These indexes employ various band combinations to retrieve the Chl-\(a\) concentration from the matching vector of pixel values. The applicability of these indexes varies across different satellite platforms. Notably, Sentinel-2 and Landsat 8 demonstrate broader compatibility owing to their superior spectral resolution, particularly in ultra-blue, red-edge, and narrow near-infrared bands. The selected indexes, together with the corresponding formulas (band math), names, references, and applicability, are found in Table 4. Next, these indexes were applied to atmospherically corrected images, selecting the most important feature and atmospheric correction processor, followed by regression analyses between the retrieved pixel values and in situ Chl-\(a\) concentrations.  
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\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Index Code** & **Band Math** & **Also Known As** & **Supported Sensors** & **Original Reference(s)** \\ \hline \multirow{6}{*}{\(\mathrm{\textbf{1}_{18}}\)} & \(l_{\mathrm{2}}\) & BRC Index or & & \\  & \(l_{\mathrm{18}}\) & \(l_{\mathrm{2}}\) & RYU & TM, ETM+, OIL, MSI & [70,71] \\ \cline{2
## 2 Materials and Methods
### Data Processing
#### 2.5.1 Surrogate Channel  
The Laplacian filter is a spatial filtering technique used to enhance the spatial resolution of the EEG signals by emphasizing the difference between the signal at one electrode and the average of the signals at its neighboring electrodes. In this study, a large Laplacian filter was applied to nine channels (Fz, Cz, Pz, F3, C3, P3, F4, C4, P4) to create a surrogate channel centered around the Cz electrode [12]. This was executed using the equation provided below, where N\({}_{\text{ch}}\) is the number of channels and x\({}_{\text{i}}\) is the ith channel, which in this case is Cz.  
\[x_{i}=\left\{\begin{array}{cc}1&i=1\\ -\
icefrac{{1}}{{N_{ch-1}}}&i\
eq 1\end{array}\right.\]  
The purpose of applying the Laplacian filter is to increase the spatial resolution of the EEG signals and to focus on the activity at the Cz electrode.
## 5 Innovative Approaches for Enzyme Immobilization
### Nanomaterial-Based Enzyme immobilization
#### 5.1.2 Metal-Organic Frame (MOF)-Based Enzyme Immobilization  
Metal-organic frameworks (MOFs) are porous crystalline hybrid materials formed by the self-assembly of metal ions and organic ligands. Their high surface area and adjustable pore sizes enhance the thermal and chemical stability of embedded enzymes, while facilitating substrate diffusion [116]. A dynamic defect production method using MOF degradation has been demonstrated to be an optimal enzyme immobilization [89], as shown in Figure 4.  
Zeolitic imidazolate frameworks (ZIFs), a subclass of MOFs, mimic traditional aluminosilicate zeolites in structure, with ZIF-8 (comprising zinc ions and imidazolate ligands) widely used due to its superior structural and textural properties [117]. Horseradish peroxidase (HRP) and glucose oxidase (Gox) were immobilized on a smart polymer membrane (PSMN), encapsulated in a hierarchical ZIF-8 (HZIF-8), showing 17.8-fold, 10.8-fold, and 6.0-fold higher catalytic activity than that of free enzymes and GOx-HRP@ZIF-8 at 37 \({}^{\circ}\)C, alongside enhanced stability, reusability, and efficiency [118; 119]. Similarly,  
Figure 4: Example of a dynamic defect production method using MOF degradation for optimal enzyme immobilization.  
immobilizing cellulase and GOx on magnetic gold mesoporous silica nanoparticles (mAu@PSNs) with ZIF-8 improved thermal stability, storage life, reusability, and catalytic activity [118; 120; 121]. The abundant NH\({}_{2}\) and COOH groups in MOFs further enhanced enzyme stabilization [121]. Table 1 summarizes the MOF-based immobilization methods, including the MOF types, enzyme reaction, and features of improved enzymes.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Experimental Set-Ups
#### 4.1.2 Evaluation Methods  
In this experiment, the confusion matrix and the overall accuracy were used to evaluate the classification performance, and the results were compared with those obtained using other, recently developed methods. The confusion matrix can be applied to the performance analysis of two-class or multi-class classification. After the model made its predictions, each class was assigned to one of a group of tables so that the data could be displayed and so that the detailed classification results for each category after the predictions were made could be seen. To evaluate the accuracy of the classification results, the overall accuracy was used. The accuracy ranged from 0 to 1, where a closer number to 1 denotes better classification performance. The total number of images that were correctly classified was divided by the number of test images.  
Figure 7: Image explanation using LIME (local interpretable model, agnostic explanation): (**a**) example of correct classification; (**b**) misclassified example.  
In addition to the confusion matrix, the kappa coefficient is also often used to analyze the difference in the classification results for indicators of the multi-category classification quality. This coefficient is a method used in statistics to evaluate consistency. It calculates the index of the overall consistency and the classification consistency. The value range is [1, 1, 2]. A higher coefficient value denotes higher accuracy of the classification achieved by the model. The kappa coefficient (\(K\)) calculation formula with a higher degree is expressed as follows:  
\[K=\frac{(P_{0}-P_{c})}{(1-P_{c})}. \tag{3}\]
## 1 Introduction  
Atoms and molecules exposed in an intense laser field show several fascinating effects, such as high harmonic generation (HHG), above threshold ionization (ATI), multiphoton ionization, _etc._[1, 2]. Most of these are nonlinear processes and are beyond the conventional perturbation theory. HHG is attractive due to its potential application in functional devices [3] and for measuring novel states in biological systems [4] or even traditional materials like water [5]. It is such a general phenomenon that can be emitted even from the two dimensional metallic system [6]. Recently, with the development of ultra-intense laser pulses, the efficiency and control of HHG have been remarkably improved [7].  
The graphene system, which has drawn intensive investigations due to its massless Dirac Fermions, is a two dimensional conductor. It is a promising material for the next generation of electronic and spintronic devices. For instance, graphene nanoribbons (GNR) can be half-metallic under an external field [8,9], and GNRs with hydrogen-passivated edges have been shown to behave as a metal or semiconductor, depending on the spin on each edge [10]. It is interesting to see the multiphoton processes in this system under a high intensity laser field. Some efforts have been made to explore the nonlinear effects in graphene [11, 12, 13, 14, 15, 16, 17, 18]. With an electron-hole model, [PERSON] theoretically predicted the strongly nonlinear response to the external electric field in graphene [11]. Later on [PERSON] _et al._ reported experimental probe of the second harmonic generation (SHG) in graphene. Distinct symmetries between graphene and graphite are observed in the anisotropy of SHG [14]. Recently, the nonlinearity is examined under a strong magnetic field up to 3 Tesla [18].  
In this article, we examine the higher harmonic generation in graphene molecules using first-principles calculations. Harmonics up to the 19 th order are identified. Three types of signals are noticed in the spectrum. The first one is the HHG, the frequency of which is always an integer multiple (harmonic order) of that of the laser pulse. The second signal is a shift of the HHG due to the limited duration of the laser field. The last one is the intrinsic emission, which corresponds to the optical transition between eigenstates. We explain the difference and the origin of the signals of these three types. We have compared the graphene molecules with and without hydrogen passivation, and found that the dangling bonds at graphene edge will enhance the HHG signal. Therefore HHG is very sensitive to the passivation situation of graphene nano-molecules.
###### Abstract  
The size distribution and total particle mass concentration (_TPMC_) of smoke particles from para rubber wood (Hewea brasiliensis) combustion in the ribbed smoked sheet (RSS) process were studied. In this experiment, temperature data values of para rubber wood combustion were recorded at 500 mm above the base of the fire by K-type thermocouples. The wood moisture content and wood combustion period were used to find and improve an equation of smoke particle size distribution (SPSD) and _TPMC_ by the response surface method (RSM). An eight-stage Andersen air sampler and a high-volume sampler were used to measure and calculate SPSD and TPMC, respectively. Resulting data in this experiment showed that _TPMC_ ranged from 3.12 to 77.42 mg/m\({}^{3}\). SPSD was single mode in which _MMAD_, mass median aerodynamic diameter, ranged from 0.64 to 1.27 microns for para wood with moisture content ranging from 31.5 to 89.7% dry weight basis. The combustion period and moisture content of para wood have a direct effect on the change of temperature data above the base of the fire and the _TPMC_ and _MMAD_ values. For predicting _TPMC_ and _MMAD_ values by the para wood moisture contents in each combustion period, the results found that the second-degree model was a better plot than the first-degree model, confirmed by higher values of the coefficient of determination (\(R^{2}\)).  
response surface; para rubber wood; _MMAD_; size distribution; prediction
## 3 Results and Discussion
### The Impact of Different Influencing Factors on Carbon Emissions in Wastewater Treatment
#### 3.4.2 The Influence of Scale upon Carbon Emissions  
Figure 6 presents the carbon emission intensity of wastewater treatment plants at various scales. Among wastewater treatment plants of different scales, no substantial difference was observed in direct emission intensity. In contrast, the indirect emission intensity steadily declined as the scale increased, which was in line with the research findings of [PERSON] et al. [38; 39; 40]. The results showed that the emission intensity of small-scale WWTPs with a scale of \(\leq\)20 km\({}^{3}\)/d was the highest, at 0.467 kgCO\({}_{2}\)-eq/m\({}^{3}\). The main reason was the low equipment efficiency and high indirect emissions. The electricity-related carbon emission intensity was 0.258 kgCO\({}_{2}\)-eq/m\({}^{3}\), which was higher than the average electricity intensity of the 49 WWTPs (0.196 kgCO\({}_{2}\)-eq/m\({}^{3}\)), revealing that there was significant room for energy efficiency optimization in other small-scale WWTPs and the facilities. The carbon emission intensity of large-scale WWTPs with a scale of \(\geq\)100 km\({}^{3}\)/d was relatively high (0.499 kgCO\({}_{2}\)-eq/m\({}^{3}\)). The main reason was that the influent COD and TN concentrations of some large plants were significantly higher than the average values.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
### VANETs Characteristics
#### 1.2.6 Communication Medium  
VANETs use wireless communication same as Mobile Ad-hoc Network (MANET) but the nodes involved in VANETs have more mobility as compared to MANETs. Like other wireless networks, VANETs encounter security issues because of wireless communication [7].
## 5 Description of the Heuristics  
Due to the complexity of the problem, the time required to find the optimal solution is not compatible with the requirements of the application. Even for small systems with only a few servers, switches and links finding a solution may take several minutes or even hours.  
A successful practice is to attack these types of problems under a sequential approach. For example, in [22] a multiprocessor energy aware scheduling problem was solved in two steps. Decisions made at each phase restrict the options of the next one reducing the number of alternatives to explore. This methodology facilitates the search of a solution; however, this does not guarantee that the optimal one is found. In this case, three stages are proposed.  
In the first phase, flows are time scheduled in each slot following an EDF approach as explained in Section 3. The output of this stage is a time schedule in which for every slot the flows that can be transmitted are listed. It is notated as \(S=\langle S_{1},\ldots,S_{H}\rangle\), where \(S_{h}\), \(1\leq h\leq H\), is the set of flows, as (source, destination) pairs, that the next stage will try to find a path in slot \(h\).  
The schedule obtained is used by the second phase in assigning available paths between source and destination while trying to keep the number of switches active as low as possible. In the second phase, flows may have different paths in each slot, and the path is not reserved for the whole flow duration. This means that a flow with a certain number of slot duration may be transmitted through different paths for each slot. This is similar to the routing of IP datagrams on the Internet in which each datagram may follow different paths. The paths are specified for each flow in each slot considering the energy demand in each case. This stage computes a schedule where some flows may have not been transmitted or some instances of them remain without being sent. This new schedule is notated with \(\overline{P}=\langle\overline{P_{1}},\ldots,\overline{P_{H}}\rangle\), where \(\overline{P_{h}}\) indicates the set of disjoint paths in \(R\) followed in slot \(h\) by the flows delivered in that slot.  
The second phase searches paths for the flows scheduled in each slot but it does not control if each activation or flow is completely scheduled; therefore, the schedule might not be feasible for some flows or activations. This job is left to the third phase in which the schedule is revised. When an activation is not completely sent before its deadline, it is removed, and a different schedule is tried.  
This means that first flows or activations are tried to be completely feasible. When this is not possible, the third phase eliminates completely the activation of flow from the whole schedule. If there are flows or activations that have not been scheduled previously, this last heuristic tries to schedule them. In this way, what the output of the third phase is a schedule of the system in which the flows or activations scheduled are completely transmitted.  
In Figure 3, the diagram shows the interactions between the different stages that are explained in the next subsections.
###### Abstract
In this study, we propose a method to reduce energy consumption when transmitting/receiving data between sensor nodes and BSs by reflecting the physical structure of the WSN, thereby increasing the lifespan of the nodes and the service life of the entire sensor network. The composition of this paper is as follows. Section 2 reviews related research that examines routing methods to reduce the energy consumption of sensor nodes. Section 3 proposes routing methods to reduce the energy consumption of sensor nodes. In Section 4, the performance of the proposed method is verified through simulation. Section 5 provides conclusions and future tasks.