esa-sceva/satcom-chunk-collection huggingface dataset:
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
## 3 Results and Discussion
### Estimation of Regional Methane Emissions
#### 3.3.2 Regional Anthropogenic Emissions  
The anthropogenic methane emissions in the 14 regions are shown in Figure 4. China is the biggest emitter, reporting a 16% lower value than the UNFCCC in 2012 than the estimation of the EDGAR inventory. Our inverse modeling results are more in line with the UNFCCC reports, which reduced the estimates from 48 (S0) to 44 (S1) Tg CH\({}_{4}\) yr\({}^{-1}\), which is very similar to the estimation by [PERSON] et al. (2015) with 44.9 Tg CH\({}_{4}\) yr\({}^{-1}\) in 2010. In India, the inverse model optimized the UNFCCC emissions upwards by 25% reaching 25 Tg CH\({}_{4}\) yr\({}^{-1}\). This estimate is higher than that reported by [PERSON] et al. (2013) (22 Tg CH\({}_{4}\) yr\({}^{-1}\)). Europe reported lower anthropogenic emissions by about 10% with the UNFCCC value compared with the EDGAR estimation, but the optimized estimates were 24.5 and 23.9 Tg CH\({}_{4}\) yr\({}^{-1}\)--6% and 8% higher than prior emissions for cases S0 and S1, respectively. The most distinct changes were found for Russia, where the prior anthropogenic methane emissions almost doubled from 17 to 33 Tg CH\({}_{4}\) yr\({}^{-1}\) by implementing UNFCCC scales, and the posterior emissions also increased from 19.6 to 32 Tg CH\({}_{4}\) yr\({}^{-1}\). The inversion-optimized results suggest that reports to the UNFCCC  
Figure 3: Regional total methane emissions (unit Tg CH\({}_{4}\) yr\({}^{-1}\)) in 2010–2012 for cases S0 and S1. Error bars are the posterior uncertainties.  
by Russia may overestimate the emissions while EDGAR may underestimate them. To some extent, our estimates of the anthropogenic emissions are influenced by natural emissions, as local concentration increases can be attributed both to natural and anthropogenic sources. Better separation between these emission categories is achieved when source regions with dominant anthropogenic and natural emissions are geographically separated as they are in the USA, Western Europe, or China. The notable advantage of high-resolution simulations, which are useful for separating between emission categories, are their ability to reproduce high concentration methane plumes of anthropogenic origin significantly exceeding the signals of natural wetland emissions. Another factor contributing to separating the sources is the difference in seasonality of anthropogenic and wetland emissions (Section 3.3.1), e.g., low wetland emissions in extratropical winter.
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 3 System Model
If the \(k\)-th status packet in group \(g\) is successfully decoded (with probability \(p_{s}\)), the edge server updates the status of sensor (\(g,k\)) using the information contained in the packet. On the other hand, if the \(k\)-th status packet in group \(g\) fails to decode (with probability \(1-p_{s}\)), the edge server cannot update the status of sensor (\(g,k\)). In the latter case, the failed packet is not retransmitted and is considered lost. Last, we describe the packet arrival model. We assume a generate-at-will model, as adopted in e.g., [5], where each sensor generates a status packet whenever needed. Accordingly, when a sensor is scheduled for transmission, it generates a packet just before transmission. As a result, each sensor always transmits a new status packet, regardless of whether the previous one was successfully decoded at the edge server. Furthermore, under this generate-at-will model, status packets from each sensor are generated periodically, with a period of  
\[T_{\text{round}}=NT_{1}+\frac{N}{K}T_{2}. \tag{3}\]  
It is worth noting that this packet arrival model effectively captures IoT networks with periodic and synchronous updates, such as those in periodic wireless sensor networks [27; 28]. In contrast, for event-driven or asynchronous systems where status packets are generated randomly, a more general arrival model would be required. The AoI performance under such models remains an active research topic; see, e.g., [21; 22; 29] and references therein.
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 4 Experiments
### Network Comparison Experiment  
In this section, in order to evaluate the performance of the model proposed in this paper, it is compared with the evaluation indexes of the algorithm models ResNet50 [12], BERT [13], LMF [14], TFN [15], MFN [16], MuIT [17] and GMFN [18].The experimental results are shown in Table 2.  
Firstly, the ResNet50 and BERT models, which utilized only the visual and text modalities, lacked deep fusion and complementary information between modalities. This limitation restricted their performance in complex tasks compared with multimodal models that can comprehensively leverage multiple sources of information.  
Although LMF simplifies computation through reducing the rank of modality features, its ability to handle complex relationships is limited, as evidenced by its unsatisfactory performance on Mae and Corr metrics. TFN, using higher-order tensors to simulate complex interactions between modalities, exhibited improved performance, but its high computational complexity restricts its practical application range. GMFN, which introduces graph structures to capture dynamic relationships between modalities, showed better performance, but its reliance on graph structures increases the model's design complexity. MFN attempts to integrate different modalities through adaptive learning, achieving relatively good results, but it still fell short in handling nonlinear complex relationships. MuIT utilizes cross-attention mechanisms to handle text, visual, and auditory modalities, achieving significant progress with multiple metrics, yet its understanding of long-term dependencies and deep interactions between modalities remains limited. The MISA model achieved higher performance through reinforcing intra-modal and cross-modal self-attention mechanisms, achieving finer information fusion, but there was still room for improvement in capturing and utilizing modality-specific information.  
From Table 2, it is evident that the ACAM model outperformed the aforementioned models on several key performance metrics. Its Mae was lower and Corr was higher compared with competitors. Additionally, the Acc-6 and F1 scores reached 49.9% and 80.5%, respectively, demonstrating its significant advantages in multimodal fusion for classroom  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CSMD** & **Mae** & **Corr** & **Acc-6** & **F1** \\ \hline ResNet50 & 1.212 & 0.505 & 39.1 & 68.2 \\ Bert & 1.032 & 0.579 & 46.8 & 77.0 \\ LMF & 1.074 & 0.569 & 45.1 & 75.1 \\ TFN & 1.057 & 0.577 & 46.5 & 76.4 \\ GMFN & 1.014 & 0.58 & 48.0 & 77.5 \\ MFN & 0.989 & 0.594 & 48.2 & 77.2 \\ MuIT & 0.984 & 0.612 & 49.3 & 79.2 \\ ACAM & 0.976 & 0.618 & 49.9 & 80.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the Network Comparison Experiment.  
assessment tasks. This performance improvement not only reflects the progress of ACAM in modal fusion accuracy but also underscores its efficient capability in understanding and utilizing multimodal information.
## 5 Conclusions  
The majority of existing machine learning-based intrusion detection systems (IDSs) rely on labeled predefined classes, which necessitate domain specialists to rapidly and effectively identify anomalies and threats. However, obtaining a reliable, up-to-date, and sufficient labeled data set for an efficient traffic intrusion detection model is extremely difficult. This paper presents a novel automatic labeling algorithm based on self-augmentation and ensemble classification strategies. The proposed SAL approach, in particular, has three phases: (1) an ensemble-based decision-making phase to address the limitations of a single classifier by aggregating the predictions of multi-classifiers, (2) a function agreement phase to assign a class label based on an adaptive confidence threshold to unlabeled observations, and (3) an increment update of an labeled set phase to maximize the accuracy and efficiency of the intrusion detection system classifier mode. In the experiments, two data sets of network traffic are used, which are collected from a variety of domains and are commonly used to evaluate anomaly detection systems. The experimental results show that the proposed SAL approach can accurately and efficiently detect and classify network traffic attacks and can outperform the performance of the baseline algorithms in terms of accuracy and F-score measurements. Future work will be reserved to minimise the run-time of the SAL approach since it can achieve the worst run time performance in the experiment.  
Formal analysis, B.A.; Investigation, A.A.; Methodology, B.A. and A.F.; Software, A.F.; Supervision, A.A.; Validation, B.A.; Visualization, A.A.; Writing--original draft, B.A.; Writing--review & editing, A.F. All authors have read and agreed to the published version of the manuscript.  
The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia, has funded this Project, under grant no. (KEP-MSc: 78-611-1443).  
Figure 11: Comparison of labeling processes based on F-measure metrics on the ISCX dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **SAL** & **KNNVWC** & **PGM** \\ \hline
**NSL-KDD** & 818.976 s & 46.818 s & 3.486 s \\ \hline
**ISCX** & 6874.436 s & 1052.318 s & 36.96 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Runtime comparison of the three semi-supervised algorithms.  
Data Availability Statement: The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Analysis of Soil-Water Characteristic Curve and Microstructure of Undisturbed Loess
SEM allows for the visual observation of two-dimensional (2D) images, facilitating the examination of the cross-sectional distribution of pores and particles [41]. Meanwhile, MIP is utilized to study the relationship between PSDs of unsaturated soil and SWCCs, as well as permeability [42; 43; 44; 45]. Despite the growing interest in unsaturated soils over recent decades, limited data on undisturbed soils have been published [46; 47]. It is worth acknowledging the existence of various studies focused on the SWCC of loess; however, most of these studies have prioritized compacted specimens, as obtaining undisturbed specimens is both labor intensive and costly. Consequently, further research on the soil structures and SWCCs of undisturbed loess buried at different depths remain a subject requiring exploration and understanding. This research presents experimental studies on the relationship between the SWCC of the undisturbed loess buried at different depths with the microstructure characteristics. Five sets of undisturbed loess samples at depths ranging from 3.4 to 51.9 m were acquired from the Chinese Loess Plateau. The basic physical properties of the loess samples, such as their dry density, particle size distribution curve, etc., were obtained through indoor testing. On this basis, the SWCCs of undisturbed loess buried at different depths were measured using the HYPROP system. Subsequently, with regard to the microscopic structure of the loess samples, the pore distribution characteristics of the samples were measured using the MIP test. The characteristics of pore morphology and particle contact for loess samples at different depths were obtained using SEM. Finally, the macroscopic SWCC test results and the microstructure characteristics of loess samples at different depths were linked, and the inherent relationship between the pore distribution characteristics and the SWCC of undisturbed loess was explored. The findings of the study can improve our understanding of how the hydraulic characteristics of undisturbed loess change as the burial depth increases. This can aid in predicting and explaining how water moves within thick, stratified loess layers.
## 3 Study Area and Data Collection  
The campus of Yunnan Normal University in Kunming, China, is used as the study area, which is located between 24\({}^{\circ}\)51\({}^{\prime}\)42\({}^{\prime\prime}\) N-24\({}^{\circ}\)51\({}^{\prime}\)52\({}^{\prime\prime}\) N and 102\({}^{\circ}\)50\({}^{\prime}\)42\({}^{\prime\prime}\) E-102\({}^{\circ}\)50\({}^{\prime}\)56\({}^{\prime\prime}\) W. Half of the study area is covered by buildings, and the other half is covered by wood (Figure 7). The EMR can be analyzed under different environmental conditions. There are three major mobile communication base stations within the study area, and there are five places around the study area where mobile communication base stations exist (Figure 8). A handheld electromagnetic radiation detector HF-60105 is used to measure the EMR value in the environment, and the frequency range of this device is 9 khz-9.4 Ghz. The radiation frequency of the mobile communication base stations on campus is usually between 1710 and 1920 Mhz. To cover this frequency range and measure the electromagnetic radiation of mobile communication base stations, 1800 Mhz is selected as the center frequency, and the frequency range width is 400 Mhz for measurement. First, the global navigation satellite system (GNSS) receiver is used to obtain the corresponding coordinate information at each point, and the coordinate system is based on the China Geodetic Coordinate System 2000 (CGCS2000): measure the electromagnetic radiation value at a height of 1.5 m, set the measurement unit of the instrument to \(v/m\) and the sampling time to 30 min, and then measure the electromagnetic radiation value at each point (Figure 9). A total of 367 data measurements were collected, and each piece of data contains coordinate information and EMR values, and the EMR values were between 0.01 and 0.73 \(v/m\).  
Figure 8: Various types of EMR base stations.  
Figure 9: Coordinate acquisition of sampled data.
# Durability of High Volume Glass Powder Self-Compacting Concrete
## 3 Results
### Drying Shrinkage  
Drying shrinkage of concrete is characterized by the time-dependent volume decrease due to moisture migration and transfer when exposed to a relatively lower humidity environment. Figure 2 shows the drying shrinkage strain of the control mixes and glass powder mixes at 30% replacement level for different particle size. GP concrete mix showed higher shrinkage measurements compared to the fly ash and glass powder SCC mixes. The finer pore structure and consumption of larger quantity of water in the pozzolanic reaction could reduce the evaporation of water due to drying. [PERSON] et al. (2018) [29] reported a larger transformation of free water to bound water in cement binders containing SCMs and hence a subsequent reduction in drying shrinkage due to the lower availability of water for evaporation. The drying shrinkage of glass mixes decreased as the glass particle size became finer, which can be related to the denser microstructure of concrete due to the presence of finer glass, which suppressed drying shrinkage. The 40G30% mix showed higher drying shrinkage compared to fly ash or the finer particle size glass powder mixes. The higher dry shrinkage could be due to overall higher porosity (Section 3.3) of the concrete because of the slower pozzolanic reaction of the coarser glass particles leading to a greater evaporation of water.  
Figure 3 shows the drying shrinkage strain of the mixes at different glass powder replacement level. The shrinkage strain was found to increase with an increase in the replacement level. However, the drying shrinkage of 20G40% was similar to GP at 180 days. The higher permeable void volume and lower consumption of water in the hydration process at the higher replacement level could be the reason for higher shrinkage. [PERSON] and [PERSON] [15] also reported an increase in drying shrinkage strain with increase in glass replacement level. Overall the results indicate the glass powder SCC mixes produced acceptable drying shrinkage values, below 0.075% after 56-days drying and met the requirements of the Australian Standard AS 3600 [30].  
Figure 1: Relative strength of mixes normalized with respect to general purpose (GP) mix at the respective age.
## 1 Introduction
FSS shows low profile characteristics and effective suppression, but it has the disadvantages of significantly increasing the weight and decreasing the gain in the operating band. Therefore, this study proposes a filter that can be integrated with a feeding line without increasing the size of the system. Additionally, the filter does not degrade the gain of the operating band, and it suppresses unwanted radiation through its sharp skirt characteristics. In this study, to shape a wide-beamwidth radiation pattern in both the E and H planes, respectively, optimization was carried out using genetic-learning particle swarm optimization (GLPSO). The GLPSO algorithm introduced in [16] was implemented using MATLAB software. Using VBA Link with CST Studio [17], the height of each pixelated dielectric bar was optimized to provide a broad HPBW. The method of pixelating DRA to make the antenna's operation in the desired direction through an optimization algorithm has been reported in [18]. In [18], they optimized an \(8\times 8\) grid square DR using a Genetic Algorithm (GA), and the main purpose of the optimization is the formation of circular polarization and the expansion of bandwidth rather than beam pattern and HPBW. To prevent radiation of the harmonics and high modes in sections other than the operating band, a modified seventh-order Chebyshev low-pass filter (LPF) was integrated into the feeding line coupled to the DR. It is difficult to integrate a step-impedance filter [19; 20] within the narrow space of the DRA ground due to limitations concerning the bending of lines. Therefore, a step-impedance filter was modified using [PERSON]'s identities and [PERSON] transformation [21]. This was easy to miniaturize and deform, and accordingly, it was confirmed that the maximum boresight gain was suppressed sufficiently to \(-13.5\) dBi in the 4-9 GHz band other than the operating band due to filter integration with the antenna. The simulated S11 under \(-10\) dB was \(3.06\)-\(3.46\) GHz (\(12.3\%\)), and the measured S11 was \(2.96\)-\(3.44\) GHz (\(14.7\%\)). The simulated 3-dB beamwidths at \(3.25\) GHz were \(184.6^{\circ}\) and \(189.3^{\circ}\) in the \(x\)-\(z\) and \(y\)-\(z\) planes, respectively, and the measured beamwidths were \(187.5^{\circ}\) and \(168.6^{\circ}\) in the \(x\)-\(z\) and \(y\)-\(z\) planes, respectively. In the operation band (\(3.25\)-\(3.35\) GHz) with wide HPBW, the total efficiency of the antenna was over \(89\%\).
## 3 Data and Methods
### Establishment of the Basic Database
#### 3.3.2 Soil Type Data  
The soil type data were extracted from the HWSD soil texture dataset of the World Soil Bank, compiled by the National Tibetan Plateau Scientific Data Center. The primary soil classifications in the study region were reclassified within the SWAT model, as illustrated in the subsequent table. Utilizing the Soil Water Characteristics module of the ArcSWAT soil database auxiliary tool in SPAW software (version6.02.75), parameters including clay, sand, and organic matter contents were employed to compute the necessary available water capacity (SOL_AWC), saturated hydraulic conductivity (SOL_K), organic carbon content (SOL_CBN), and soil wet bulk density (SOL_BD) within the soil database, See Table 1 for details, Table 2 shows the specific situation of soil stratification.  
Furthermore, the danger of soil erosion must be evaluated in actual applications, with the most utilized method being the estimation formula for the Williams soil erodibility factor \(K\) value [28] as follows:  
\[K_{USLE}=f_{csand}\cdot f_{ci-si}\cdot f_{orgc}\cdot f_{disand} \tag{5}\]  
In the Equation (5), \(f_{csand}\) is the coarse sand texture soil erosion factor; \(f_{ci-si}\) is the soil erosion factor of clay loam; \(f_{orgc}\) is the soil organic matter factor; and \(f_{disand}\) is a high sandy soil erosion factor.
Enhanced Optical and Thermal Stability of Blue Pyrrole Derivatives for Color Filter in Image Sensors  
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and transmission properties of colorant materials to produce high-quality images in compact pixel structures [7; 8]. Specifically, blue colorants for image sensors must maintain high transmittance in the blue region at 450 nm while ensuring low transmittance in the red region at 650 nm [2; 3]. To achieve this, the development of new colorant materials with high molar extinction coefficients is required, along with thermal stability that can withstand high-temperature manufacturing processes [9; 10; 11]. High-temperature conditions can negatively affect the stability and performance of materials, so developing materials that meet these requirements is a crucial area of research for high-performance image sensors. Examples of colorants used in blue color filters include xanthene, cyanine, azo, anthraquinone, dioxazine, triarylmethine, and pyrrole [12; 13; 14]. These compounds are suitable as color filter materials for displays due to their excellent optical and thermal properties and ease of synthesis. However, there are still issues related to thermal stability that need to be addressed before they can be applied to color filters for image sensors [3; 15]. [PERSON]'s group reported improved thermal and chemical stability by producing a dense thin film using an azo blue colorant with cross-linkable acrylate additives [16]. [PERSON]'s group reported a blue material based on dimerized triarylmethine derivatives [17]. This approach resulted in superior optical and thermal properties by incorporating two chromophore moieties within a single molecule through dimerization. In particular, pyrrole colorants can be considered superior candidate materials for blue color filters due to their high transmittance and strong color intensity in the 400-500 nm wavelength range [18]. [PERSON]'s group combined a dimeric triarylmethine derivative, ABD, with an anthraquinone derivative, PAQ, to enhance the optical properties and stability of blue colorants [19]. The study reported that by mixing these two different colorants, the thermal and chemical stability was improved while the optical properties of the material were maintained. In this study, two new pyrrole derivatives, 2-(3-cyano-4-(4-(dibutylamino)phenyl)-5-oxo-1,5-dihydro-2H-pyrrol-2-ylidene)malononitrile (BAPCP) and 2-(3-cyano-4-(4-(dihexylamino)phenyl)-5-oxo-1,5-dihydro-2H-pyrrol-2-ylidene)malononitrile (HAPCP), were designed and synthesized as blue colorants, and their optical and thermal properties were analyzed. [PERSON]'s group previously reported a study on the dispersibility of Disperse Blue 359, a representative blue material, using a polymer dispersant, and they developed a method for predicting dispersibility [20]. Because of this, Disperse Blue 359 was selected as a reference material for comparative evaluation in this study.