esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the VOCs Under Different Storage Temperatures by GC-IMS
#### 3.4.1 Analysis of Topographic Plots
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Category** & **No.** & **Compound** & **CAS\#** & **MW** & **RI** & **RT [sec]** & **Dt [a.u.]** & **Aroma Characteristics** \\ \hline Esters (9) & 1 & Acetic acid ethyl ester & 141-78-6 & 88.1 & 614.8 & 148.578 & 1.33298 & Fruity \\  & 2 & Methyl acetate & 79-20-9 & 74.1 & 540.2 & 120.785 & 1.18873 & Special aroma \\  & 3 & Ethly butanoate & 105-54-4 & 116.2 & 790.6 & 268.747 & 1.55056 & Fresh aroma, Fruity \\  & 4 & Methyl heptanoate & 106-73-0 & 144.2 & 1020 & 615.058 & 1.80562 & Berry, Iris, Fruity \\  & 5 & Mestiyl oxide & 141-79-7 & 98.1 & 784.7 & 263.245 & 1.44037 & Fruity \\  & 6 & 1-Butly acetate-M & 123-86-4 & 116.2 & 805.8 & 283.67 & 1.2346 & Fruity \\  & & 1-Butly acetate-D & 123-86-4 & 116.2 & 805.8 & 283.67 & 1.2346 & Fruity \\  & 7 & Ethly Proponate-M & 105-37-3 & 102.1 & 708.7 & 196.933 & 1.15528 & Pineapple \\  & & Ethly propanoate-D & 105-37-3 & 102.1 & 708.7 & 196.933 & 1.15528 & Pineapple \\  & 8 & iso-Propyl acetate-M & 108-21-4 & 102.1 & 655.9 & 166.56 & 1.15934 & Special fruity aroma \\  & & iso-Propyl acetate-D & 108-21-4 & 102.1 & 655.9 & 166.56 & 1.15934 & Special fruity aroma \\  & 9 & Ethly trans-2-butanoate & 623-70-1 & 114.1 & 838.9 & 318.841 & 1.1776 & Spicy, Rum, Jackruit \\ Alcohols (5) & 10 & 1-Hexanol & 111-27-3 & 102.2 & 873.5 & 360.257 & 1.63716 & Fruity \\  & 11 & 3-Hydroxy-2-butanone & 513-86-0 & 88.1 & 737.7 & 220.007 & 1.35493 & Milk \\  & 12 & 3-Methyl-1-pentanol & 589-35-5 & 102.2 & 841.6 & 321.92 & 1.59607 & Spicy, Cocoa, Wine \\  & 13 & (E)-3-Hexen-1-ol & 928-97-2 & 100.2 & 859.9 & 343.432 & 1.24638 & Grass \\  & 14 & (E)-2-Hexen-1-ol & 928-95-0 & 100.2 & 853.9 & 336.164 & 1.51193 & Pleasant odor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile compounds identified by GC-IMS. Figure 3: Topographic plots of GC-IMS (**A**). Comparative topographic plots of GC-IMS (**B**). Fingerprint spectrum of GC-IMS on 0 d, 42 d and 84 d (**C**). I: Substances detected in all samples; II: High expression substances in 0 d and IT-42 d; III: High expression substances in 0 d and FT; IV: Substances that do not exist in fresh fruits.
## 1 Introduction
Moreover, it required an initial value of the estimated distance by detecting the peak concentration time at the receiver nanomachine. Furthermore, it required several iterations for convergence, and the time cost or computational complexity was significantly high. Although previous studies have demonstrated improved performance in their proposed synchronization schemes, the complexity of nanomachines has limited performance. In particular, the use of different molecular types or instant message-sending times could be too complex for the computational ability of the transmitter or receiver nanomachine, thereby limiting the adaptability of such schemes in real MC systems. Furthermore, many studies have assumed that the concentration detection time remains constant. This assumption means that when an initial molecular impulse is emitted, the receiver always detects the signal and decodes it after an elapse of a certain time. However, a constant detection time cannot be maintained due to propagation delays or random molecular movements. A small error at the start of detection can adversely affect the detection of signals. Moreover, in practice, when the system parameters are unknown, the initial detection time is not available to the receiver. Therefore, to correctly decode the information under various MC environments, a simple and adaptive signal detection method is needed. This paper proposes a feedback-controlled adaptive method for signal detection in a diffusion-based MC system. The proposed method is designed to enable a receiver nanomachine to automatically adapt the detection times on the basis of the measured molecular concentrations and weighted feedback errors. The proposed method adjusts the signal detection time even when the initial parameter values of the system are unknown to the receiver. In addition, the proposed method does not require tuning of the system parameters between the transmitter and receiver. The main contributions of this work include the following: (1) The detection time is automatically adapted to minimize the ISI- and noise-related detection errors under time-varying channel conditions. (2) The proposed method can be easily realized in a real MC system; the receiver nanomachine can be simply implemented using a single type of molecules even when the system parameters are unknown to the receiver. (3) The proposed method can be easily integrated with existing modulation techniques or asynchronous modulation systems, which can assist in the implementability and scalability of MC systems. The remainder of this paper is organized as follows. Section 2 introduces the proposed signal detection method for MC based on a diffusion model and provides analysis of the system convergence. To evaluate the proposed method, the results are presented in Section 3. Conclusion and future works are presented in Section 4.
## 3 Results
### Prediction of Carbon Monoxide (CO) Gas Levels
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Optimal**} & \multicolumn{4}{c}{**Suboptimal**} \\ \cline{2-9}  & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exe. (s)** & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exe. (s)** \\ \hline RF & 3.540 & 8.508 & 8.615 & 85.37 & 6.985 & 17.581 & 19.897 & 75.76 \\ XGBoost & 3.109 & 7.003 & 7.447 & 45.68 & 5.291 & 12.804 & 14.286 & 32.04 \\ LightGBM & 3.613 & 8.170 & 8.716 & 32.81 & 8.565 & 22.611 & 26.649 & 16.14 \\ ElasticNet & 8.335 & 21.378 & 24.961 & 24.50 & 9.144 & 24.171 & 28.144 & 14.79 \\ AdaBoost & 3.504 & 8.312 & 8.561 & 48.41 & 8.570 & 22.069 & 26.406 & 46.17 \\ GBR & 3.174 & 7.142 & 7.334 & 44.66 & 8.137 & 21.355 & 24.890 & 26.80 \\ KNN & 4.199 & 9.162 & 9.499 & 17.12 & 9.244 & 23.445 & 27.594 & 15.47 \\ DT & 3.786 & 8.860 & 9.180 & 34.04 & 8.386 & 22.009 & 25.326 & 20.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test performances of CO level prediction under optimal and suboptimal conditions. values (R = 0.324 and R = 0.289, respectively). These results demonstrate that the XGBoost model achieved successful CO level predictions under challenging conditions. The difference in prediction performance between the models under optimal and suboptimal conditions was evaluated using a \(t\)-test, and it was found that the difference was statistically significant (t-statistic = 5.03, \(p\)-value = 0.0015). [PERSON]'s d value (1.7788) indicates that hyperparameter optimization substantially impacts model performance. This analysis reveals that different parameters are crucial to model accuracy, and the performance gap is distinctly noticeable.
## 3 Materials and Methods
### Magnetic Susceptibility Measurement
#### 3.1.2 Laboratory Magnetic Susceptibility Measurement  
During magnetic susceptibility measurement, the Bartington sensor (Whitney, UK) generates a subtle magnetic field through alternating electric current and detects the material's magnetization within it [12]. The MS2B sensor was employed, providing magnetic susceptibility values in SI units. The sensor measures samples prepared according to specific protocols. Plastic utensils and bags were utilized for sampling to prevent contamination with metallic materials. Samples were dried for three days in a dryer, crushed, and then filled into 10 cm\({}^{3}\) plastic vials. The MS2B sensor, a portable laboratory device, conducts measurements at two different frequencies, enabling the detection of superparamagnetic minerals, crucial for soil and rock analysis. Its calibration is continuous with the electronic standard based on diamagnetism of water. This was performed using a cylindrical 10 mL sample of distilled, de-ionised water.  
The procedure on the MS2B sensor is more intricate than that on the KM-7 device. It is configured to display magnetic susceptibility in dimensionless SI units (\(10^{-5}\)). Before sample measurement, a plastic calibration vial provided by the manufacturer was used to verify the MS2B device's calibration. Samples were first measured at a low frequency (LF) of 0.46 kHz and a 0.1 range multiplier, with air and sample measurements taken sequentially. Ideal measurements exhibit a zero difference between the first and third readings; otherwise, the measurements were repeated. The device calculates volume susceptibility, indicating the ratio of sample magnetization to magnetic field (80 Am\({}^{-1}\)). These values on the MS2B device are typically in the order of \(10^{-5}\). After low-frequency measurements, samples were measured at a high frequency (HF) of 4.65 kHz. The plastic vials were consistently oriented to minimize measurement errors [12].  
The MS2B sensor finds extensive application across various fields. In geology and soil analysis, it is instrumental for individual sample assessments. In archaeology, it aids in former settlement location, stratigraphic analysis, and magnetic material identification. Additionally, it supports environmental pollution research by analyzing rock, soil, and vegetation samples. Furthermore, it is utilized in construction material testing, facilitating the determination of geological origin and assessment of overall material permeability [12].
## 3 Sky vs. Earth Observations with Antenna Arrays
### The Radio Astronomy Point of View  
In radio astronomy, the field of view (FOV) of an interferometer is determined by the size of the individual antennas and by the observing frequency (or wavelength) [6]. It is expressed in terms of the primary beam, which is nothing but the antenna response as a function of the angle away from the main axis. The full-width-at-half-maximum (FWHM) of the primary beam is usually taken as the diameter of the field of view synthesized by the interferometer; however, it is well-known that the sensitivity is not uniform over this field of view, which has a maximum at the center and tapers off towards the edges. The principle of aperture synthesis is to mix the signals from an array of antennas to obtain images having the same angular resolution as an instrument the size of the entire collection. The signals collected by the elementary antennas of the array are combined pairwise in a correlation unit to produce interferometric data known as complex visibilities. An image of the sky brightness distribution is reconstructed from these visibilities with the aid of the computer. These measurements have continuous coordinates because the elementary antennas of the arrays are non-regularly spaced and because they point outside the zenith direction as Earth rotates. As a consequence, for computational reasons, these complex visibilities first need to be resampled onto a regular grid before being inverted. This preliminary step is called \"gridding\" [27]. The relation between the sampling step \(d\) of that regular grid and the overall angular extent of the synthesized field of view \(\theta_{FOV}\) is to be found in the Fourier theory:  
\[d/\lambda=\frac{1}{\sin\theta_{FOV}}, \tag{5}\]  
where \(\lambda\) is the observing wavelength. For example, for every elementary 15 m antenna of the NOEMA millimeter interferometer [28], the primary beam is about \(51^{\prime\prime}\) at 100 GHz (\(\lambda=3\) mm). As a consequence, here the sampling step \(d\) is about \(4000\lambda\), that is to say, 12 m. As the antennas can move on rail tracks up to a maximum separation of 760 m in the E-W direction and 368 m in the N-S direction, the minimum number of pixels \(N^{2}\) to satisfy the Shannon sampling theorem [29] is \(256^{2}\). With the most extended configuration of the array, the resolution is about \(1^{\prime\prime}\). As the pixel size is \(\theta_{FOV}/N=51/256\simeq 0.2^{\prime\prime}\), the synthesized beam is about five pixels wide. Finally, due to the diameter of the antenna dishes and to the observing frequency, it is important to keep in mind that with radio-astronomy interferometers, the overall extent of the synthesized field of view is much smaller than the \(2\pi\) sr space in front of the instrument. To fix the ideas concretely, with NOEMA, the solid angle \(\Omega_{FOV}\) is about 48 nsr.
###### Abstract  
Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term Agroecosystem Research (SP-LTAR) grassland study, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) has been established with a cluster of six eddy covariance (EC) systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie systems located in different landscape positions. The purpose of this paper is to describe this long-term experiment, report preliminary results on the responses of differently managed tallgrass prairie under variable climates using satellite remote sensing and EC data, and present future research directions. In general, vegetation greened-up and peaked early, and produced greater forage yields in burned years. However, drought impacts were greater in burned sites due to reductions in soil water availability by burning. The impact of grazing on vegetation phenology was confounded by several factors (e.g., cattle size, stocking rate, precipitation). Moreover, prairie systems located in different landscapes responded differently, especially in dry years due to differences in water availability. The strong correspondence between vegetation phenology and eddy fluxes was evidenced by strong linear relationships of a greenness index (i.e., enhanced vegetation index) with evapotranspiration and gross primary production. Results indicate that impacts of climate and management practices on vegetation phenology may profoundly impact carbon and water budgets of tallgrass prairie. Interacting effects of multiple management practices and inter-annual climatic variability on the responses of tallgrass prairie highlight the necessity of establishing an innovative and comprehensive long-term experiment to address inconsistent responses of tallgrass prairie to different intensities, frequencies, timing, and duration of management practices, and to identify best management practices.  
global positioning system; interacting effects; remote sensing; unmanned aerial systems; vegetation phenology +
Footnote †: journal:  
11 July 2019; Accepted: 21 August 2019; Published: 23 August 2019
## 1 Introduction
One agricultural waste material is the husk separated from rice. Due to its limited applications, this material is usually burnt and destroyed. The product remaining after the burning phase, called RHA, has unique characteristics, such as a high silica content [10; 33]. [PERSON] [10] indicated that RHA has positive impacts on the improvement of gypsum clays' mechanical properties with lime. The author also recommended 6-8% lime and 8-10% RHA as the optimum amounts for stabilization of gypseous soil. RHA has an adequate amount of silica with a high specific surface area, which is very suitable for activating the soil and lime reaction. Reactive magnesia (M) is a more effective substance in GGBS activation compared to lime, as it facilitates a higher rate of strength development [2; 6; 20; 25; 26; 28]. Reactive M is commonly obtained from the calcination of magnesite in the temperature range of 700-800 \({}^{\circ}\)C (below the temperature for dead-burned M (>1400 \({}^{\circ}\)C). About 2400 MJ of energy is required to produce one ton of reactive M, and the process emits about 1.4 tons of CO2 [23; 24; 27]. Reactive M is used at low concentrations during GGBS activation; hence, the overall CO2 emission during the reaction of M with GGBS is considered relatively lower than that of PC. For instance, the production of one ton of M-GGBS with M at the optimum M:GGBS ratio of 1:9-1:4 only emits about 0.20-0.34 tons of CO2 [9; 26]. The study by [PERSON] et al. [2] also showed that M-GGBS binder protected gypseous soil samples from swelling and contributed to better strength after soaking compared to PC. This work aimed to establish a new method to stabilize soils subjected to internal sulfate attacks by gypsum (CaSO4) using a combination of nano-magnesium oxide (M), RHA, and GGBS. For this purpose, unconfined compressive strength (UCS), linear expansion (LP), and flexural strength (FS) tests were performed before and after exposure to water. In this study, two steps were taken to achieve the objectives:  
Step 1: Running compaction characteristics for each mixture. Step 2: Performing UCS before and after sulfate exposure, LP, and FS tests. The curing times applied were 7, 28, and 90 days. In addition to the UCS, LP, and FS tests, SEM and EDS were performed to study the stabilized soil samples to determine their mineralogical and microstructural performance.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 8 Proposed Framework: Multi-Layer Threat-Defense Alignment Framework
### MLTDAF Contributions  
The MLTDAF framework provides several unique features:  
* **Cross-Layer Integration:** This framework methodically matches threats with the smart grid's architectural makeup in contrast to other taxonomies that concentrate on specific attack types.
* **Strategic Risk Management:** By connecting the threat landscape to vital system operations, strategic risk management enables the allocation of security resources in a prioritized manner.
* **Scalability and Flexibility:** These facilitate ongoing adaptation by allowing for modular upgrades that take into account new attack methods and defense strategies.  
The MLTDAF paradigm can be used in both brownfield (new infrastructure) and greenfield (existing infrastructure) smart grid deployments due to its modular versatility. Its multilayered architecture facilitates plug-in compatibility with third-party intrusion  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Smart Grid Layer** & **Primary Threats** & **Defensive Mechanisms** \\ \hline Perception & Physical tampering, sensor spoofing, FDIAs & Tamper-resistant hardware, ML-based anomaly detection, hardware encryption \\ \hline Network & DoS/DDoS, eavesdropping, jamming & SDN-based intrusion detection, traffic shaping, redundant topologies \\ \hline Control & Malware, command injection, timing attacks & Patch management, real-time behavioral analysis, integrity monitoring \\ \hline Application & Data breaches, unauthorized access, phishing & Role-based access control (RBAC), Two-Factor Authentication (2 FA), LLM-based anomaly detection \\ \hline Management & Insider threats, misconfigurations, policy bypass & Zero-trust architecture, continuous auditing, behavioral biometrics \\ \hline \hline \end{tabular}
\end{table}
Table 34: Layer-based threat-defense mapping.  
detection, anomaly detection, and encrypted communication layers, as well as integration with current SCADA security regulations. However, interoperability standards such as IEEE 2030 and IEC 61850, as well as the financial and training limitations that utility companies confront, must be taken into account in real-world applications.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 2 Related Work
In the proposed scheme, Resource Description Framework (RDF) statements were used to represent the extracted knowledge. A statement may describe either data or conceptual information. In addition, each peer maintains meta-information about these statements (e.g., source of the statement, overall confidence in the statement and respective source peer, etc). The local ontology of each peer is then exploited by a specific k-neighbors-Selection (K-NS) algorithm to select the \(k\) relevant neighbors for the query. [PERSON] et al. (2013) introduced the Intelligent Search (IS) query-oriented method. IS maintains a routing table that stores neighbors profiles. They suggested a simple vectorial representation of each neighbor profile consisting of the most recent queries processed by the neighbor altogether with the number of query hits. Whenever a peer initiates or receives a query it performs an on-line ranking of its neighbors with respect to their profiles and the query content. The query is then routed to the first \(k\) relevant neighbors. [PERSON] et al. (2016) introduced a supervised machine learning algorithm, called Route Learning, to solve the K-NS problem. In the learning phase, the introduced routing algorithm accumulates data from sent queries and related query-hits. Indeed, a feature space for each neighbor is created. Furthermore, neighboring peers and forthcoming queries are respectively considered as classes and new objects to classify in a supervised multi-classification problem. Therefore, neighbors (i.e., classes) to which the query is assigned are considered as the \(k\) relevant neighbors. [PERSON] et al. (2019) relied on the Formal Concept Analysis (FCA) theory ([PERSON] et al., 2013), which is a method of extracting interesting clusters from relational data, to implicitly extract user interests from previous sent queries and associated query-hits (e.g., query identifier, query terms, the downloaded documents and source peers). User interests are stored in a local knowledge base per peer. Indeed, user interests express strong relationships between previous queries, common terms, and related positive peers. To forward the query, a learning query routing algorithm first computes the similarity between user interests and the query keywords. Peers included in the closest user interests to query are then selected as the \(k\) relevant peers to forward the query to.
## 1 Introduction
However, the proposed methodologies have primarily examined specific landscapes, and their performances in different landscapes have not been investigated. For example, [PERSON] et al. [20] proposed a hybrid data balancing method for mountainous regions. In another study, [PERSON] et al. [21] investigated the impact of different data balancing techniques for mapping crops. To this end, the recently proposed method, namely the Geometric Synthetic Minority Over-sampling Technique (G-SMOTE), by [PERSON] and [PERSON] (2019) [22], has been introduced as a robust method to address the class imbalance problem. However, there is still a lack of research that can thoroughly assess the performance of G-SMOTE in different landscapes by applying different ML algorithms. Given the importance of the issues mentioned above, the present study was an attempt to investigate the performance of G-SMOTE to handle the class imbalance problem in LC classification at six different landscapes applying three frequently used ML algorithms,including RF, SVMs, and ELM. Furthermore, the SVM-RFE method was applied for each landscape to select the most informative features and use them as classification inputs to obtain the optimal feature subset from radar and optical bands, spectral indices, and texture information. Specifically, we are going to answer the following questions in this study:  
1. [label=(0)]
2. What are the most informative features from Sentinel-1, Sentinel-2, spectral indices, and textural information for LC mapping using three well-known ML algorithms in different landscapes? 3. What is the performance of the G-SMOTE algorithm in LC classification in different circumstances? 4. Which ML classifier has higher accuracy on LC mapping at diverse landscapes?