esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Some Issues Associated with the Empirical Models, GLPs, and Climate Warming  
PAR direct absorption and indirect utilization are associated with NO2*, H2O, OH, and BVOCs + OH + O3 [112]. The photochemical term expresses PAR use by all kinds of GLPs. In empirical models of GPP, Re, and NEP, the multiple interactions between PAR and its related terms (photochemical, scattering, and GPP/Re term in previous studies instead of BVOC terms) and EMBE are fully reported [40; 41; 70; 85].  
The sensitivity displayed that the GPP increases with the decrease of S/Q (associating with the increase of scattered PAR by GLPs, including aerosols and clouds) [40], which was in line with other model studies and observations (the increased diffuse radiation due to aerosols brings an increase of GPP) [83; 113; 114; 115; 115; 104; 105; 106; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 13  
It is found that the increases in absorbing and scattering GLPs contribute to the enhanced losses of global solar radiation in the atmosphere. This energy can heat the atmosphere and increase the air temperature (i.e., regional climate warming), as well as weaken the airmass movements (e.g., wind speed). The phenomena and mechanisms are evidently displayed in the Antarctic and Arctic regions (Sodankyla and Dome C sites) and other sites, especially (1) in the Dome C station because it is the cleanest and driest atmosphere [5]; (2) the highest air temperature increase rate appeared in the winter and the lowest (or lower) in the summer in some sites in China [87], corresponding to the large losses of solar UV and visible (or PAR) radiation in winter [125]; and (3) the decreasing trend in precipitation in Beijing (bigger decrease in the city than in the suburbs) from 1961-2004 [125].  
The more GLPs (including water and water vapor) in the atmosphere, the more absorption and scattering energy accumulation and use/release in the atmosphere, resulting in large changes in precipitation and atmospheric vertical and horizontal movements (related to natural disasters, e.g., rainstorm, flood, mud-rock flow, and wild wind) of the atmosphere. Therefore, it will have multiple-win effects to reduce GLPs (GHGs, non-GHGs, particles, etc.) emitted from the anthropogenic and natural sources into the atmosphere and produced through CPRs in the atmosphere on achieving carbon peak and neutralization, controlling air pollution, and mitigating climate warming and extreme weather disasters [88; 5].  
The most important issue should be emphasized that the synchronous and high-quality observational data in multiple fields, such as atmosphere, biosphere, hydrology, land, and solar radiation, are very necessary in the stations in China and the world [126], especially for empirical model development and evaluation, so as to comprehensively study the individual process and mechanism along with their integrated processes and mechanisms in above associated fields as a whole system.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid  
Communication play an important role in SGs, as one of the most significant differences between traditional grids and SGs is two-way communication. Traditional power grids only provide one-way communication between the utilities and the customer, whereas SGs provide two-way communication [3,10]. This enables use of distributed smart sensors, distributed power generation, real-time measurements and metering infrastructure, and monitoring systems. Information exchange is of great importance for the SG to provide reliable power generation and distribution. Following is an overview of SG infrastructure, domains, network architecture, and SG applications.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 4 Results and Discussions  
### Dataset (MBRSC)  
The dataset utilized in this study comprises 72 aerial images of Dubai obtained by MBRSC satellites, meticulously annotated through pixel-wise semantic segmentation across six distinct classes. These images are organized into eight larger tiles, enabling efficient handling of the dataset. The semantic segmentation classes include Building, Land, Road, Vegetation, Water, and Unlabeled, with each pixel assigned a specific label. The Unlabeled class caters to ambiguous or unannotated regions. The dataset is instrumental for training and evaluating models tailored for semantic segmentation tasks in urban environments. The MBRSC satellite-derived imagery holds significance for applications in urban planning, environmental monitoring, and infrastructure development. Additionally, the dataset allows for insights into the composition of Dubai's landscape, and the annotation of building structures, roads, and vegetation facilitates detailed analysis for various research purposes. This study underscores the dataset's importance in remote sensing and urban landscape analysis; considering both its spatial and semantic richness, the dataset is openly available on Kaggle. The original image and the mask generated can be seen in Figure 5.  
Figure 5: Dataset semantic segmentation of aerial imagery.
## 4 Case Study of Punjab OSM Data
### Results  
#### 4.2.1 Network Length Completeness  
In this study, two datasets (OSM dataset downloaded on January 2016 and February 2017) were evaluated using the network model (Figure 2) for the road network completeness. The statistical analysis of the OSM road network concluded that the total mapped length of highway type features is 33,813,720.6453 m (old) and 38,147,564.3035 m (recent). Further, it was evaluated that 98.27% (old) and 92% (recent) of the data could be used for the navigation of cars, whereas the rest of data is suitable for other domains, including pedestrian and cycleways. Further, about 4,333,843.6582 m more road network data have been added during the period January 2016 to February 2017.  
It was analyzed that for old data, there exists a total of 23 ways classes, and for recent data, this increased to 26. Further, few classes of ways diminished, and some new classes emerged from recent data, e.g., unclassified ways, earlier present in the data, have now been properly assigned tags and classified. The statistics of the resultant analysis are shown in Figure 6. Further, the two datasets were analyzed to find where geographic changes took place and found that during the period January 2016 to February 2017, rural area (in particular the areas around place name (Raikot) and geographic area, bounding box: 30.4842, 75.4376, 30.7961, 75.7892) has developed more as compared to urban area. In addition, it has been witnessed that the road network of the Malwa region of Punjab [97] is complete as compared to Doaba and Majha regions (Figure 9c).
## 2 Related Work
ASTGNN [20] designs a dynamic graph convolution net (DGCN) for dynamic dependencies. For calculating spatial correlations at both local and global scales, the utilization of an attention mechanism is prevalent in capturing dynamic spatial dependencies. Spatial-temporal prediction models, such as attention-based spatial-temporal graph convolutional network (ASTGCN) [32] and STTNs, adopt attention mechanisms for dynamic spatial dependencies. Meta graph transformer (MGT) [33] employs a multi-graph with a self-attention mechanism that achieves outstanding performance. ASTGCN [32] applies spatial-temporal attention and spatial-temporal CNNs to make a prediction. STTNs proposes a spatial transformer structured by a self-attention layer and a ChebNet to extract the dynamic and fixed spatial dependencies simultaneously. As mentioned before, GCNs are extensively utilized in spatial-temporal prediction. [PERSON] et al. [34] adopt spectral CNN to implement the convolution on graph by graph Fourier transform. For lacking enough flexibility on feature aggregation, [PERSON] et al. [35] proposed graph wavelet neural network (GWNN) by substituting the graph Fourier basis with the graph wavelet basis, which results in feature aggregation within a particular neighborhood range.
# Non-Local Effects in Brillouin Optical Time-Domain Analysis Sensors
## 3 Mitigation of Non-Local Effects
### Probe Dithering for ER Impairments Mitigation
Thus, as they counter-propagate, their wavelength difference is correlated just at certain locations in the fiber (correlation peaks), where there is efficient Brillouin interaction. Therefore, the gain experienced by the probe due to the pump pulse pedestal is reduced, together with the extra depletion that it induces on the pulses.
## 3 Results  
### The Spatial Pattern of the CUE Extremes  
The BEPS model simulation results show that the global annual mean CUE was approximately 0.45, ranging from 0.33 to 0.66 (Figure 1a). CUE in the Northern Hemisphere was higher than in the Southern Hemisphere. The highest CUE occurred in some areas of middle-high latitudes in the Northern Hemisphere (>40\({}^{\circ}\)N), where the CUE was larger than 0.55. In subtropical regions and the Southern Hemisphere, the CUE was smaller than 0.45. From 1982 to 2016, the spatial pattern of the CUE standard deviations showed that higher CUE regions had higher standard deviations, except for some regions of Australia, where the CUE was smaller than 0.4, but the standard deviation was larger than 0.03 (Figure 1b). These results indicate that the higher CUE regions have more potential to enhance carbon transformation proficiency.  
Due to the CUE determined by GPP and Ra, we compared the spatial pattern of GPP and Ra with CUE. The spatial pattern of the annual averaged GPP (Figure 1c) and Ra (Figure 1d) presented a discrepancy from the spatial pattern of CUE. The largest GPP occurred in some areas of tropical regions, where GPP was larger than 2500 gC/m\({}^{2}\)/y In some regions of eastern North America, European, and Southeast Asia, the GPP was larger than 1500 gC/m\({}^{2}\)/y but smaller than 2500 gC/m\({}^{2}\)/y. Other regions' GPP was smaller than 1500 gC/m\({}^{2}\)/y. The spatial pattern of Ra (Figure 1d) was similar to GPP. Ra was larger than 1500 gC/m\({}^{2}\)/y in some tropical areas. In some regions of eastern North America, European, and Southeast Asia, Ra was larger than 600 but smaller than 1500 gC/m\({}^{2}\)/y. Additionally, in other regions, Ra was smaller than 600 gC/m\({}^{2}\)/y.  
The spatial distribution of the positive CUE extremes was similar to negative CUE extremes (Figure 2), indicating that areas with positive extremes are also prone to negative extremes. Central North America, Europe, and most regions of Australia contributed the largest to positive CUE extremes, with CUE anomalies larger than 0.05. In Northern South America, some areas of Africa, and Southeast Asia, the anomalies of positive CUE were smaller than 0.02 (Figure 2a). Conversely, Central North America, Europe, and most areas of Australia contributed most to negative CUE extremes. The anomalies of negative CUE
## 3 Results and Discussion  
### Detection of Carotenoids by Raman Spectroscopy  
In general, chlorophylls show only very weak Raman responses. Carotenoids, on the other hand, provide very intense Raman signals, so that even small amounts in biological samples such as fruits can be detected. This characteristic feature is important to help detect and compare carotenoid contents in the very early stages of hot pepper maturation. In this respect, only the still completely green hot peppers showed no carotenoid signals at all. In Figure 1, Raman spectra of hot peppers are shown depending on the maturation process of the fruit on the plant. The important and intense carotenoid bands were observed in the range of about 800 to 1600 cm\({}^{-1}\). Most bands of green peppers can be assigned to chlorophyll, polysaccharides and waxes, which are present in the cuticular of the fruit.  
The band at about 860 cm\({}^{-1}\) could be assigned to a C-O-C asymmetric stretching of the glycoside linkage in acidic pectins [29; 36]. The weak band at 1327 cm\({}^{-1}\) was attributed to chlorophyll a which is known for the highest intensities in a neat chlorophyll spectrum [10; 13]. The most intense bands in the spectrum were assigned to the carotenoids, observed at 1150-1170 and 1500-1550 cm\({}^{-1}\). The band is due to the in-phase C=C as well as C-C stretching vibration of the polyene chain. In the range of 1000-1020 cm\({}^{-1}\), methyl groups attached to the polyene chain were recognized, showing moderate intensity [23]. [PERSON] et al. demonstrated the dependence of a peak shift in the range of 1510-1536 cm\({}^{-1}\) on the number of conjugated C=C bonds [10; 16; 37]. The complete assignment of the predominant bands in Raman spectra of hot peppers is given in Figure 1.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 5 Conclusions  
This paper proposed a novel method to enhance the performance of K-means algorithm-based channel estimation by utilizing a CNN-based two-step learning method. To overcome the limitation of the conventional K-means-based blind channel estimation method, which determines the same channel for all received signals processed through clustering and thus fails to effectively reflect fine-grained channel variations at the subcarrier and OFDM symbol levels, this study designed dynamic clustering grouping patterns and a two-step learning structure adaptable to dynamic channel conditions. The simulation results demonstrated that the proposed method consistently maintained high channel estimation performance under various channel conditions. Compared to the optimal pattern, the proposed scheme showed similar channel estimation performance across all SNR ranges, achieving near-optimal performance in low SNR and progressively improving to closely match the optimal pattern in high SNR. Additionally, the proposed CNN-based learning method outperformed the conventional pattern in all conditions in terms of MSE and maintaining stability, even in high-mobility scenarios with significant channel fluctuations. These results highlighted the proposed scheme's adaptability to dynamic channel conditions.  
Conceptualization and Methodology, G.K. and Y.K.; Data Analysis, G.K.; Data Collection and Experimentation, G.K., Y.K. and D.J.; Experimental Results Analysis--D.J.; Writing--Original Draft, G.K. and D.J.; Writing--Review and Editing, B.-G.K. and T.K.; Supervision and Funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Soonchunhyang University Research Fund and in part by an Institute of Information Communications Technology Planning Evaluation (IITP) grant, funded by the Korean Government (MSIT) (No. RS-2024-00397789, Development and Standardization of 6G Radio Transmission Standard Technology).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Evaluation of PID-Based Algorithms for UGVs
## 3 Methodology
### PID Controller  
The PID (Proportional, Integral, and Derivative) controller is widely used in linear control systems, known for its ease of implementation and effectiveness in controlling processes and variables (such as temperature, pressure, speed) to a desired value [31]. PID control involves three separate control actions, whose outputs are summed to form a single output signal. The current reading of the controlled parameter is subtracted from the command to generate an error signal. This error signal is processed by three distinct units [32]. The PID controller is commonly used in industry and automated systems due to its effectiveness and ease of implementation, as shown in Figure 5[33].  
Proportional Action (P), also known as the proportional gain (Kp), multiplies the error between the desired value and the actual value by this coefficient. Therefore, the larger the error, the greater the correction applied by the controller. The proportional controller can correct the error, but if the gain is too high, it may cause oscillations around the desired value [34; 35].  
Integral Action (I) helps to eliminate the accumulated error over time. This control action, known as the integral gain (Ki), allows the controller to correct persistent errors, even if the proportional action is not sufficient to eliminate them. Additionally, the integral term is useful for eliminating steady-state error, which can occur when there is a constant disturbance in the system [36].  
Derivative Action (D) predicts the rate of change of the error over time. This control action, known as the derivative gain (Kd), is useful to prevent excessive oscillations and reduces the controller's response to rapid changes in the error variable. It acts as a \"damping\" effect that smooths the response and improves the overall stability of the system [37; 38].  
The combination of these terms allows the controller to react quickly to disturbances, correct persistent errors, and keep the controlled variable close to the desired value. The tuning of the coefficients is essential to optimize system performance, minimize oscillations,  
Figure 4: Trajectory with the ON–OFF algorithm.  
Figure 5: Basic description of the PID controller.  
and improve the response time. The PID controller represents the contribution of these different control actions. Over 95% of control problems can be solved with this type of control system, although some may only require two control actions, such as PI (Proportional-Integral) without the derivative action or PD (Proportional-Derivative) without the integral action [39].
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Food Storage  
Food preservatives comprising chitosan are environmentally friendly, developed from renewable resources, and intrinsically biocompatible. Chitosan also has unique characteristics, including high film-forming capabilities and barrier properties against aromas and in the dry state, making it a good choice for packaging structures and food coatings [183; 184]. Nevertheless, although chitosan films are ecologically safe and sustainable, they tend to have poor mechanical performance. Furthermore, edible coatings and packaging materials that come into direct touch with food may have an impact on the quality [185].  
Nanofiber technology has the potential to create ultra-thin non-woven mats for food packaging. Because of their physicochemical characteristics allowing them to conserve the quality and safety of food supplies across distribution and storage, nanofibers with a high chitosan content are employed for antibacterial packaging [186]. The future implementation of chitosan nanofibers in food packaging materials and processing might promote food production efficiency, sustainability, and storage capacity, while also promising more ecologically friendly food delivery.
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 3 Single Cell Patterning
### Patterning results  
According to the methods above, HL-60 cells with good cell activity were used and prepared for cell suspension with the concentration of 5 \(\times\) 10\({}^{6}\) cells/mL. We took the cell suspension 100 \(\upmu\)L to the centrifuge tubes of 0.5 mL, added antihuman CD45 Biotin 2 \(\upmu\)L to it, and fully blended the antibodies with the cell suspension. After 30 min incubation at room temperature, centrifuging was conducted for 5 min at the speed of 1000 rpm. Subsequently, the supermate was extracted, and 500 \(\upmu\)L PBS was added for rinsing; this was repeated thrice. Finally, 100 \(\upmu\)L of PBS and 2 \(\upmu\)L of biotinylated streptavidin solution were added and fully blended. After 30 min incubation at room temperature, 100 \(\upmu\)L of PBS was dropped in a Petri dish for observations via a fluorescence microscope. HL-60 cell lines were patterned on the glass substrate, and the results are shown in Figure 11, with the single-cell capture rate of 92%.  
Figure 11: Single cell patterning results of HL-60 cells. Left: The fluorescence image after the patterning of antibodies. Right: HL-60 patterning results (the photo under both the bright field and the fluorescence excitation).  
Figure 10: The ratios of patterning islands capturing no cells, only one cell, two, or more than two cells. It is indicated that the islands with diameters between 15-25 \(\upmu\)m are the optimal ones to capture only one cell, and are favorable for forming a single-cell patterning chips.  
The biocompatibility of the fabrication and the surface modification processes was tested using HL-60 cells in the RPMI-1640 media, supplemented with FBS and antibiotics for 10 h. After 10 h, the cells were still in the former patterned area. Meanwhile, cell viability was evaluated using Trypan blue staining. The results are shown in Figure S5. It can be seen that more than 93% of the cells were still alive after 10 h, indicating good biocompatibility.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 3 The Discrete HMM for SMS Spam Detection
### HMM Learning  
The Baum-Welch algorithm [15] is typically used for finding HMM parameters \(\lambda=\ (\pi,A,B)\). That is, given HMM with initial parameters  
\[\lambda_{0}=(\pi_{0},\ A_{0},\ B_{0}) \tag{5}\]  
Figure 3: (**a**) Observation state distribution in ham messages set; (**b**) observation state distribution in spam messages set.  
and observation sequence  
\[\underline{X}=\{\omega_{t}\}_{t=1}^{N} \tag{6}\]  
to update \(\lambda=(\pi,\ A,\ B)\) iteratively and find parameters that maximize the likelihood of observed data, i.e.,  
\[\underset{\lambda}{\operatorname{argmax}}\Big{(}\{\omega_{t}\}_{t=1}^{N}\big{|} \lambda\Big{)} \tag{7}\]  
The initial parameters \(\lambda_{0}=(\pi_{0},\ A_{0},\ B_{0})\) are initialized as:  
\(\pi_{0}=[0.5,\ 0.5]\) indicates that the state can start from ham or spam with the same probability.  
\(A_{0}=\left[\begin{array}{cc}0.5&0.5\\ 0.5&0.5\end{array}\right]\) means each state transmission has the same probability.  
\(B_{0}=\left(b_{ij}\right)_{2\times n}\) infers the initial observation probability distribution calculated in Section 3.4.  
The Baum-Welch algorithm starts with initial parameters and then repeatedly takes two steps: Expectation step (E-step) and maximization step (M-step) until convergence (i.e., the difference of log-likelihood is less than small number \(d\)), as shown in Figure 4.