esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Technologies of 6G for Smart Grid Applications
### Energy Harvesting (EH) by Wireless Power Transfer (WPT)  
The joint power and information transfer is a novel approach in providing energy harvesting and communication simultaneously through the wireless network [14]. Simultaneous wireless information and power transfer (SWIPT)-based NOMA (NOMA-SWIPT) is capable of serving massive connectivity, as well as enhanced spectral efficiency, data rates, reliability, and energy efficiency [86; 87; 88]. The SWIPT protocol is usually considered the best RF-based energy harvesting method for energizing the devices and overcoming the battery draining issue in the case of relaying and other purposes as well [87; 89].  
The cooperative relaying improves the coverage area and minimizes network outages for the cell edge devices [90; 91]. Moreover, cooperative NOMA-enabled SWIPT protocols and an energy harvesting-based green IoT can provide improved spectral and energy efficiency, massive connectivity, wide coverage area, as well as low-latency communication [1; 92].
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.6 Robot Control I  
In [64], the authors present an advanced toolbox for training robots using RL for practical applications in industrial settings. The architecture was enhanced from the prior work \"_gym-gazebo_\" [21] with the inclusion of ROS 2 for more functionality. A Modular Robotic Arm (MARA) was used to create diverse habitats. The authors focused on bridging the gap between training robots in simulation and deploying them in the real world, aiming to transfer trained policies effectively.  
The authors in [64] utilized the PPO RL algorithm to train a MARA robot in simulated environments. The robot acquired knowledge through trial and error, obtaining incentives for behaviors that resulted in favorable outcomes, such as precisely reaching a goal location and avoiding collisions. The robot utilized an ROS, specifically ROS 2, which served as the foundation of the \"_gym-gazebo2_\" [64] design. Through robot simulation training, the objective was to improve the transferability of the acquired policies to actual robots. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work promotes the collaboration and knowledge exchange in the field of robotics, inspiring others to explore different algorithms and complex scenarios. It provides a platform for assessing the performance of various RL algorithms in simulated environments, allowing for advancements in the industrial field of robotics without the need for costly physical experimentation. This approach accelerates innovation and facilitates the development of more efficient and capable robotic systems.
## 7 Performance Evaluation of NDA-EVM-AM on an NLOS Scenario
### Average Spectral Efficiency  
The average spectral efficiency of two adaptive modulation schemes at target \(P_{out}=0.1\), \(S_{TH}=10^{-3}\) in NLOS scenario (i.e., the \(\eta-\mu\) channel with parameter values of \(\eta=1\) and \(\mu=0.5\)), along with the theoretical outage capacity, is shown in Figure 6. Specifically, the theoretical outage capacity is the upper bound of the average spectral efficiency of adaptive modulation, which can be obtained by using the method in Section 5. In Figure 6, it is shown that NDA-EVM-AM is closer to the theoretical outage capacity than DA-SNR-AM. Although both schemes suffer performance degradation within the low SNR region, NDA-EVM-AM has a 0.4 bit/s/Hz increment of spectral efficiency over DA-SNR-AM in the middle SNR region and a 0.8 bit/s/Hz increment in the high SNR region. From the analysis in the Section 7.2, NDA-EVM-AM has a more accurate modulation order selection over DA-SNR-AM, while the switching thresholds of NDA-EVM-AM lead to a more efficient modulation order adjustment than that of DA-SNR-AM. Overall, NDA-EVM-AM is an effective adaptive modulation for an NLOS scenario of WSN.  
Figure 5: The comparison of switching thresholds for the two different adaptation strategies based on SER curves.  
Figure 6: The average spectral efficiency comparison for NDA-EVM-AM and DA-SNR-AM.
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 4 Future Improvements
Closely spaced thin beds within the width of a single seismic event, typically ranging from 5 to 40 m (\(\lambda/4\)) in their interval, are challenging to differentiate in both vertical sections and horizontal views, which can lead to confusion. We need next-generation, broad-bandwidth acquisition and processing technologies to tackle the mapping difficulties associated with these thin beds. Some new developments in high-resolution 3D (HR3D) [56; 57] are promising in revealing thin-bed geology with a signal of up to 200 Hz at 1 s (1000-1200 m) depth.
## 7 Supervised Classification
### Data Models: Systems' Features Fusion and Feature Selection  
Each _data model_ is a group of features associated with a single system, a single data analysis method or a meaningful combination of the two. The choice of features in each _data model_ was driven by a meaningful and operationally realistic data acquisition and processing concept. For instance, _data models m1-m3_ regard using a single dual frequency SSS, where each frequency or both are used for textural analyses. _Data models m4-m8_ respect a data acquisition concept in which a single MBES is used, but with the application of different levels of data processing i.e., terrain, backscatter texture or backscatter angular response analysis, a combination of the above or all together. _Data model m9_ concerns both SSS and MBES used together, but with just their backscatter mosaics being analyzed, which is a concept adapted to exploring any possible rate of improvement when increasing the spectral bands of the backscatter texture analysis. Finally, _data model m10_ assumes that both MBES and SSS are used together and all available feature extraction methods are applied to them. For each group, a feature selection algorithm is used to detect the features (no matter which system they are derived from) that offer the best classification accuracy possible (see Section 7.3). A wrapper feature selection method [70] has been chosen, evaluating the feature sets in each _data model_, by using a learning scheme (Naive Bayes here--see Section 7.3) that offer the best information gain. In this way, the most important features in each group are determined to reduce the overall complexity of the feature space that in turn will lead to producing simpler decision rules during the supervised classification process. Feature selection is an attractive option to validate which feature extraction methods offer better habitat discriminations in each concept and weight their relative importance per _data model_ as well as per habitat type. The final _data models_ considered after the feature selection stage are presented in Figure 5, organized by feature extraction method and swath mapping system. It is important to mention that at least one derivative from each feature extraction method has been selected through this process, implying that each one is offering information gain in the habitat discrimination process and they are all worth being considered.
# Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide
## 3 The Tunnel Monitoring System
### The Web-Based Platform  
A web-based LAMP platform (Linux, Apache, MySQL and PHP) was implemented for real-time data management and analysis, which also allows users, upon authentication, to interactively query the raw and processed data.  
The website architecture is composed of a framework of automatic scripts for data management, implementing the following functions:  
* data gathering: data from all the sensors are automatically acquired at customised intervals;
* data storage: raw data are collected, upon proper normalisation, in a relational database;
* data analysis: raw data are analysed to produce additional information like displacement rate, sensors whose signal is above a certain threshold, abnormal peaky behaviour, sensors whose data is missing, etc.;
* data visualisation: a set of Java script codes based on Dygraphs' charting library ([[http://dygraphs.com](http://dygraphs.com)]([http://dygraphs.com](http://dygraphs.com))) constitutes the backend for the interactive plots of displacement evolution, rain and water discharge.  
Along with these scripts, additional codes have been developed for alarm triggering in the case of system malfunctioning (e.g., network failure, sensor failure) and anomalous data (excessive displacement or inclination) and to communicate via email the occurrence of such situations. Other communication tools are also available, such as SMS, Twitter and Facebook, but they are not activated at the moment.  
The platform is highly scalable and permits us to integrate several sensors with different sampling times. This capability of integrating and managing many different sensors improves resiliency by device redundancy in the monitoring system.  
The web interface makes it possible to produce graphs of the displacements or rotations measured by instrument vs. time. Single extensometer or clinometer measurements can be visually compared to the rainfall or discharge in the tunnel drainage system in real time. The time range is customisable, so that zooming to particular phenomena within the three-year dataset is straightforward. Another useful feature allows us to linearly interpolate the data over a selected time span to estimate the deformation rate.  
In another page, the displacements recorded by the crackmeters are also plotted as a colour map, while the cumulative inclination of the clinometers is plotted on an arrow map (Figure 7). For enhanced clarity, crackmeters have been clustered into two groups, with different magnitude ranges, one for the instruments located along cracks and one for the instruments located at hinges between two tunnel segments. This is because the displacements recorded at the hinges are one order of magnitude larger than those measured over the cracks.  
In another page, data from crackmeters deployed along the lining cracks (1-16) and exceeding the threshold crack size of 0.3 mm are shown. The threshold value of 0.3 mm was chosen considering the Eurocode directive and [PERSON] et al. [18].
## 2 Theoretical Section
### Measurement of Weight Fraction of Glass Fiber (Gc) of Laminate Structure
#### 2.2.1 Rule Calculation  
International standards and classification societies provide rules for ship design, with formulas to calculate Gc and the mechanical properties of the laminate, as well as the required thickness of the laminate structure, by considering the Gc and the hull shape [7]. This process is called \"scantling\" in ship design. The rule calculations allow easy estimation of Gc from the properties of the fiber cloth and resin and are widely used for the structural design of composite ships. Gc can be calculated from the densities of the glass fiber and cured resin and the thickness of the laminate structure. ISO 12215 and classification society rules suggest that the glass fiber and resin densities should be taken as 2.56 and 1.2 g/cm\({}^{3}\), respectively, resulting in Equation (1) for calculating Gc [6].  
\[\text{Gc}=2.56/(3.072\times t/w+1.36)\times 100, \tag{1}\]  
where \(t\) is the laminate thickness (mm) and \(w\) is the weight of the glass fiber cloth per unit area (kg/m\({}^{2}\)).  
The thickness of a single impregnated ply, T\({}_{\text{single ply}}\), can be obtained from the thickness of the laminate structure, t, as follows:  
\[\text{T}_{\text{single ply}}=w/3.072((2.56/\text{Gc})-1.36), \tag{2}\]  
where \(w\) is the weight of the glass fiber in a single ply of laminate (kg/m\({}^{2}\)).
## 5 Experiment
### Strategy Visualization  
In this section, we take the NMT model as an example. We will display the best parallel strategy searched by Trinity on 4 GPUs clusters. Figure 4 shows the fine-grained partition of the NMT model by Trinity. Compared with the Layered Expert, Trinity has a finer-grained division of the LSTM layer, attention layer and softmax layer. This is not possible for expert design: it colocates all operations in a step. Compared with Hierarchical, the partition and schedule strategy of Trinity is generally similar to Hierarchical, but Trinity groups the LSTM operations in the decoder more intensively, and tends to trade part of the memory overhead for the optimization of the communication cost.  
Experiments show that the Trinity method supports the fine-grained division of neural network layers and has the ability to trade off computation and communication overhead. In Figure 4, different colors in the figure represent different GPUs. We find the following: (1) embedding is a typical parameter-intensive submodel. The results show that Trinity is suitable for embedding to use memory or storage in exchange for computing and communication resources; (2) The LSTM is a computationally intensive operation. Trinity seeks to divide the LSTM layer more flexibly and achieves load balancing by weighing the computation and communication costs. Attention and softmax both have parameter and computation intensiveness, i.e., dual intensiveness. Trinity implements partition and schedule costs for attention and softmax from multiple dimensions, to reduce the parameter synchronization while ensuring load balancing. The parallel strategy of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Networks** & **Operators** & **Edges** & **Datasets** & **Field** \\ \hline InceptionV3 & 12,745 & 21,928 & & \\ NASNet-Large & 18,644 & 27,505 & CIFAR-10 & CV \\ PNASNet-Large & 14,063 & 20,718 & & \\ GNMT & 8738 & 16,564 & & \\ NMT-layers & 16,432 & 24,235 & PTB & NLP \\ \hline \hline \end{tabular}
\end{table}
Table 3: The model and datasets selected for the experiment.  
Figure 4: Partition and schedule strategy visualization. The color represents the device.  
the layered NMT network model shown in Figure 4 takes only 2.56 s to execute forward propagation, backpropagation, and gradient computation.  
Figure 5 shows the convergence curves of the Trinity algorithm based on NMT, NASNet, and InceptionV3. We proved the convergence and effectiveness of this method. It is worth noting that, in this experiment, we used the standard Adam method to implement the gradient descent algorithm. No noise is introduced in the initial stage of training, and only the standard Adam method is used to implement the gradient descent algorithm. The total number of iterations \(step=100\). We recorded the loss value for each iteration. It can be seen from the curve that the algorithm continues to converge. The results show that, whether for the NLP network (NMT) or CV network (InceptionV3 and NASNet), it only takes approximately 25 rounds of iterations for the loss of the reinforcement learning search algorithm to drop to less than 10.
## 4 ML-Based Resource Allocation
### Machine Learning Approach  
To overcome the limitations of traditional resource allocation methods and autonomous decision-making processes, ML and DL techniques can play a significant role [34]. with out being explicitly programmed, devices can learn, implement, and improve results automatically with the help of Machine Learning techniques [35]. Model training and decision-making are the two phases of an ML approach. In the training phase, a system model is trained by using training datasets, and later, this trained model is used to estimate each new input [36]. Depending on how the learning is achieved, Machine Learning algorithms are simply classified as supervised, unsupervised, and reinforcement learning [37]. In supervised learning, labeled training datasets are used to train the model, while in unsupervised learning, raw and unlabeled training datasets are used for the same, and in the reinforcement learning method, agents are used to interact with its environment and learn from the actions to achieve an optimal solution. By using historical data, supervised learning is used to predict or estimate future events. Regression, Decision Trees (DT), Support Vector Machines (SVM), Neural Networks (NN), and K-Nearest Neighbor (K-NN) are the most common supervised learning techniques. Whereas to discover the patterns and characteristics of raw, unlabeled data unsupervised learning is more suitable. Self Organizing Maps (SOM), K-means clustering, and Gaussian mixture models (GMMs) are the most important techniques in unsupervised learning [34; 36].  
In reinforcement learning, a learning entity known as an agent interacts with the environment and obtains rewards or penalties for each action; by this means, it creates a policy to set up its learning scheme and decides which action to choose in a certain situation. Maximizing the rewards over time is the main aim of the RL algorithm [38]. Unlike other ML algorithms, in RL agents are not advised what actions need to be performed to achieve optimal strategies, rather based on each interaction's output with the environment it makes the decision autonomously. Action-state, rewards and penalties, policy, value function, and environment model are the main components of an RL system.
## 7 Conclusions  
This paper addressed the challenge of intelligent decision-making for UAV defense in electromagnetic spectrum warfare within complex urban environments. It introduced a MURPPO algorithm based on branch training. By applying Markov modeling to the task process, this paper elucidated how UAVs performed closed-loop perception, localization, and tracking of anomalous radiation sources under designed reward conditions. Simulation results demonstrated that the MURPPO algorithm effectively perceived anomalous radiation sources, achieved detection and localization through multi-UAV collaboration, and attained stable convergence. Moreover, it outperformed baseline algorithms in key metrics such as localization rate, stability, and convergence speed. The proposed algorithm exhibited a 38.5% higher localization rate compared to MAPPO in the later stages of training. The MURPPO algorithm's ability to handle sparse rewards and coordinate multiple UAVs highlights its potential for enhancing autonomous decision-making capabilities in challenging electromagnetic environments. Future research directions could explore hierarchical structures and formation control to further improve performance in large scale applications, as well as enhanced sensing capabilities with finer angular resolution or multiple frequency bands for better source discrimination.  
Conceptualization, J.C.; methodology, J.C.; software, J.C., Z.Z. and X.Z.; validation, J.C., Z.Z. and D.F.; formal analysis, Z.Z.; investigation, J.C. and Z.Z.; resources, Z.Z.; data curation, Z.Z. and D.F.; writing--original draft preparation, D.F. and C.H.; writing--review and editing, J.C., Y.Z. and X.Z.; visualization, T.H.; supervision, D.F., T.H. and J.Z.; project administration, J.C.; funding acquisition, Y.Z., D.F., C.H. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under grant 2022 YFC3301404.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company China Satellite Network E-Link Co.Ltd., and author [PERSON] was employed by the company The 22 th Research Institute of China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Recent Applications of Solid Phase Microextraction Coupled to Liquid Chromatography
## 2 Applications
### Biological
#### 2.1.4 Partitioning Studies  
Binding of chemicals to dissolved organic carbon, sediment, proteins, or other components may reduce the free concentration and thus the bioavailability or effectivity of the chemical. Therefore, knowledge of the extent of binding or partitioning of the chemical of interest is essential in several scientific disciplines and much research is focused on measuring partition coefficients or binding affinities. For highly bound compounds, the concentration of the free fraction is frequently low and difficult to detect. SPME is the perfect solution for these applications due to its ability to determine chemicals in aqueous matrices even at very low concentration and to extract only a minimum amount of ligand (negligible depletion), so that the free concentration in the solution will not change significantly, and the equilibrium between the bound and free form will remain undisturbed. Thus, when partitioning studies involving semi- or non-volatile molecules must be performed, SPME-LC coupling is a good alternative to more conventional approaches. For instance, a method for the determination of the free fraction of chlorpromazine in medium containing bovine serum albumin (BSA) by SPME with commercial polyacrylate fibers and LC-UV detection was developed [55] and used to measure the protein binding of chlorpromazine and to calculate the chlorpromazine-bovine serum albumin (BSA) affinity constant. A similar analytical challenge, i.e., the study of the binding of human serum albumin (HSA) with carbamazepine, was faced by [PERSON] et al. [56] by SPME coupled to LC-UV or LC-MS, and by spectroscopic fluorescence, and nuclear magnetic resonance (1H NMR). The multi-instrumental approach provided synergic information that allowed for the identification of high and low binding sites for carbamazepine, while SPME-LC-MS was used to determine in vivo the free concentrations of carbamazepine and to verify in vitro binding predictions at therapeutically relevant doses. Drugs-bovine serum albumin interactions were also the object of an SPME (C18 fibers) study [57] devoted to quantifying the protein binding of diclofenac, 2,4-dichlorophenoxyacyacetic acid (2,4-D), ibuprofen, naproxen, torasemide, warfarin, and genistein to BSA, phospholipid liposomes, fetal bovine serum (FBS), and cells.