esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Polyacrylonitrile-based activated carbon fibers (ACFs), modified using potassium hydroxide (KOH) or tetraethylenepentamine (TEPA), were investigated for carbon dioxide (CO\({}_{2}\)) adsorption, which is one of the promising alleviation approaches for global warming. The CO\({}_{2}\) adsorption isotherms were measured, and the values of isosteric heat of adsorption were calculated. The results showed that the KOH-modified ACFs exhibited a great deal of pore volume, and a specific surface area of 1565 m\({}^{2}\)/g was obtained. KOH activation made nitrogen atoms easily able to escape from the surface of ACFs. On the other hand, the surface area and pore volume of ACFs modified with TEPA were significantly reduced, which can be attributed to the closing or blocking of micropores by the N-groups. The CO\({}_{2}\) adsorption on the ACF samples was via exothermic reactions and was a type of physical adsorption, where the CO\({}_{2}\) adsorption occurred on heterogeneous surfaces. The CO\({}_{2}\) uptakes at 1 atm and 25 \({}^{\circ}\)C on KOH-activated ACFs reached 2.74 mmole/g. This study observed that microporovosity and surface oxygen functionalities were highly associated with the CO\({}_{2}\) uptake, implying the existence of O-C coordination, accompanied with physical adsorption. Well cyclability of the adsorbents for CO\({}_{2}\) adsorption was observed, with a performance decay of less than 5% over up to ten adsorption-desorption cycles.  
carbon dioxide; activated carbon fiber; surface modification; adsorption; characterization 2019
# Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis
## 2 Methods
### Implementation of the Solution
#### 2.5.1 Main Process  
The main process is responsible for receiving real-time images from the scanner via a TCP communication channel. The received data are decoded, extracting essential information such as frame identifier, number of samples, number of lines, and the B-scan data. These data are then passed to the real-time info object to be shared with the other processes, which are notified by setting different work-flow flags.
# Adaptive Multi-Grained Buffer Management for Database Systems
## 4 Performance Evaluation
### Workload Adaptivity  
In this experiment, we aim to verify the adaptivity of AMG-Buffer for dynamic workloads. First, we run all algorithms with a balanced workload R50-H50. As shown in Figure 5a, AMG-Buffer reduces both write and read operations. Compared to traditional algorithms and MG-Buffer, AMG-Buffer reduces up to 29% and 27% overall I/Os, respectively, yielding a high-performance improvement on the balanced workload. We also  
Figure 4: Comparison of I/Os.  
report the time performance in Figure 4(b), which confirms that AMG-Buffer has the best time performance among all algorithms. In particular, AMG-Buffer can save more dirty tuples in the buffer and has fewer write I/Os than traditional ones. Moreover, AMG-Buffer avoids the additional read I/Os when writing incomplete dirty pages, resulting in higher performance than MG-Buffer.  
We can see that the results in Figure 4(a),b are very consistent, indicating that the I/O cost dominates the overall time performance. Hence, in the following experiments, we only report the I/O cost.  
To evaluate the performance of the adaptivity of AMG-Buffer and the adjustment scheme of the P-Buffer, we simulate the change of workloads by varying the hybrid ratio (\(NQ/WQ\)) to generate a dynamic workload. Dynamic workloads are very common in real applications. For example, user queries in an E-Business platform may be changed by holidays or other events. Thus, a static buffer management policy like LRU is not suitable for dynamic workloads.  
Next, we evaluate AMG-Buffer on a dynamic workload. To generate the dynamic work changing with time, we divide the queries in the trace file into epochs, each of which consists of 1 million queries. We prepare 100 epochs in the dynamic workload, i.e., the workload consists of 100 million queries in total. Then, we randomly set the hybrid ratio in each epoch from 10/90 to 90/10. Figure 6 shows the total I/Os for AMG-Buffer and its competitors. Although MG-Buffer has fewer write I/Os than LRU, LIRS, CFLRU, and CFDC, it suffers from high read I/Os. When the requested pages exhibit a high clustering rate, MG-Buffer will cause many additional read I/O operations. On the contrary, AMG-Buffer achieves the best performance on the dynamic workload due to its adaptive use of the P-Buffer and the S-Buffer.
## 1 Introduction
analysed impedance changes in potatoes using four 1.15 mm diameter electrodes in a square array, with one diagonal pair applying PEF and the alternating pairs measuring impedance changes from EIS (1 Hz to 1 MHz). It focused solely on impedance, without analysing conductivity and permittivity, and only varied PV and NP [64]. Another PEF study on potatoes by [PERSON] et al. employed four-point EIS with frequencies from 50 Hz to 1 MHz and copper needle electrodes. Their analysis showed a notable decrease in impedance above a threshold PEF field of 250 V/cm (NP = 8, PW = 100 \(\upmu\)s, PI = 1 s). However, PEF parameters other than PV were not examined [47]. Additionally, open-ended coaxial four-point EIS probes have not been employed in the context of electroporation research on plant-based models. Thus, the aim of this study was to comprehensively assess conductivity changes in response to PEF by EIS in potato samples as a model system. To that purpose, we varied diverse PEF parameters, i.e., PV, PV, PI, and NP, and performed EIS with a custom-designed four-point coaxial probe. We hypothesized that a specific frequency range would show high relative conductivity changes between PEF-treated and non-treated potato regions in EIS, which can be applied to EIT.
## 2 Related Work
### Digital Democracy and Machine Learning  
Digital democracy aims to facilitate the engagement of wide masses of participants in policy-making processes. In recent years, a wide variety of platforms have been developed towards this aim, with \"_Have your say_\" and _Consul_[23] being two of the most prominent ones. Such platforms leverage recent technological advancements to collect and exploit collective knowledge and opinion sharing in order to facilitate governance and to render these processes more transparent, inclusive, and efficient [3]. However, mass digital participation presents new challenges to the stakeholders, as the increase, in terms of scalability and complexity, can be overwhelming for both institutions and participants. In addition, stakeholders must employ personnel with relevant expertise to extract meaningful ideas or opinions from citizen input, thus rendering these tasks too expensive and time-consuming.  
The necessary automation of the extraction and synthetic processing of citizen input has led many research approaches to the employment of ML and NLP methods. The approach proposed in [24] introduces a conceptual framework for integrating AI tools into digital democracy. Specifically, by employing knowledge processing and data management services this approach aims to create a formalization scheme for argumentative deliberations, to augment sense-making, and to facilitate knowledge exchange and pioneer generation via a series of tools. _DelibAnalysis_[25], on the other hand, is a framework that aims to measure the discourse quality of political discussions online. The proposed framework uses supervised algorithms to predict a discourse quality index that exploits six indicators--namely, participation, level of justification, content of justification, respect, counterarguments, and constructive politics--for the corpus of the documents. This index is manually extracted for each discourse during the training phase and is used as a label for the ML model employed. Additionally, _Polis_[26] utilizes social input to automatically collect and synthesize feedback from deliberations, using dimensionality reduction techniques and clustering models to create groups of similar opinions. Then, for each group a series of comments are selected, with the choice influenced both by the users' votes and by their expressivity concerning the rest of the comments.
###### Abstract  
Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.  
Keywords:device-to-device communication; throughput-awareness; cooperative reinforcement learning; system throughput; interference management; adaptive resource allocation +
Footnote †: journal:  
_Future Internet_ **2017**, 9, 72; doi:10.3390/f9040072
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 6 Results and Discussion
### Consumption of Energy (mWh) in the Idle Mode
This can have implications for applications that require real-time monitoring or decision making, such as environmental monitoring or underwater exploration. **Question 3: How do Telnet and Superframe influence the dependability of the UWSN in comparison to other protocols? **  
**Answer 3:** Telnet and Superframe can influence the dependability of an underwater wireless sensor network in different ways compared to other protocols. The dependability of a UWSN refers to its ability to maintain reliable and consistent communication among its nodes, even in harsh underwater environments. Telnet is a simple protocol that does not provide any mechanisms for the reliability or error correction. Therefore, its impact on the dependability of a UWSN is limited, and it may not be suitable for applications that require high levels of reliability or data accuracy. In contrast, Superframe is designed to optimize the dependability of low-power wireless sensor networks, including UWSNs. Superframe incorporates mechanisms such as channel hopping, adaptive modulation, and duty cycling to enhance the reliability of the network. These mechanisms help to reduce interference and fading, minimize energy consumption, and increase network coverage. Superframe can also incorporate mechanisms such as error correction coding to further enhance reliability. When compared to other protocols, the impact of Telnet and Superframe on the dependability of a UWSN depends on the specific application requirements and the environmental conditions. For example, other protocols, such as acoustic communication protocols, may be more reliable in situations where the UWSN needs to operate in highly scattering and absorbing underwater environments. On the other hand, protocols such as Superframe may be more reliable in situations where the UWSN needs to operate in low-power, energy-constrained scenarios.
## 3 Results and Discussion
### The Formation Mechanism and Discriminant Conditions of RSSF
#### 3.2.2 Distinguishing Basis for Forming RSSF  
How to judge whether the current seepage field of the dry-stack tailings pond reaches a relatively stable state is the key foundation for reasonable and accurate calculation of the RSSF. From the analysis in the previous sections, the reason for the formation of the RSSF of the dry-stack tailings pond has been found. It is that when the average rainfall infiltration flow and the initial dam drainage flow are in a dynamic equilibrium state, the internal water content of the dam body does not change much in a year, thus the saturation line of the dry-stack tailings pond is in a relatively stable state. Therefore, as show in Figure 9, the relative relationship between the initial dam drainage flow and the average rainfall infiltration flow can be used to judge whether a RSSF has been formed. However, considering the large fluctuation in the drainage flow of the initial dam (shown in Figure 9), the fluctuation in the pore water pressure field is relatively small, as shown in Figure 5. Hence, the pore pressure curve at the typical position in the dam can be used for discrimination. Practically, when the pore pressure remains basically unchanged over time, it can be judged that the seepage field of the dry-stack tailings pond is relatively stable. Therefore, it is recommended for the pore water pressure curve of the typical position to be used to judge whether the seepage field of the dry-stack tailings pond reaches a relatively stable state.
## 4 Performance Analysis
### Experimental Results and Discussion
#### 4.4.2 Comparison Methods  
To verify the effectiveness of our proposed model, a comparison with some deep learning methods is conducted on the CCKS2020 dataset using the F1 score as the experimental measure, and the specific description of the comparative model as follows:  
(1) BERT-TextRank: [PERSON] et al. [30] extracted semantic information by the BERT pre-trained model and employed the TextRank technique to increase the information of the entity description, where the number of keywords in the TextRank model is three.  
(2) BERT-Flow-Label: The modules of our model are compared in the BERT-based model; to be specific, a layer flow model corrects the sentence vector output from BERT to fully represent semantic information, and label embedding is employed to further enhance the performance of multi-classification.  
(3) RoBERTa-BiLSTM: BiLSTM is able to fully capture the contextual information by concatenating the forward and backward hidden-layer vectors, which is commonly applied in entity linking [49; 50] to fully extract textual information. Inspired by these methods, the RoBERTa-BiLSTM model is constructed to further extract textual information through the BiLSTM module.  
(4) RoBERTa-Attention: [PERSON] et al. [51] employed a layer of attention mechanism to further improve the representation of textual information by further focusing on important information and reducing the interference of irrelevant information. Hence, the RoBERTa-Attention model is created utilizing the aforementioned method as inspiration to further focus on the extracted textual information through the attention module.  
(5) RoBERTa-TextRank: The Textrank technique is applied in RoBERTa to enhance text topics and facilitate the extraction of semantic information, where the number of keywords in the TextRank model is three.  
The outcomes of these comparison experiments are presented in Table 5, and the following conclusions are derived from the analysis of results. To begin, the effectiveness of our flow and label-embedding modules is proved in the experiment of BERT-based model, and specifically, the F1 score of BERT-Flow-Label is 0.53% higher compared to BERT-TextRank. Then, our proposed model outperforms the RoBERTa model using the TextRank technique, in which the F1 score of RoBERTa-TextRank is 1.08% lower than our model. Finally, our proposed approach is superior to some deep learning models commonly utilized in entity linking. To be specific, the F1 score of our method is 0.98% and 0.91% higher than RoBERTa-Attention and RoBERTa-BiLSTM, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1/\%(CCKS2020)** & **F1/\%(CCKS2019)** \\ \hline BERT & 87.61 & 88.10 \\ RoBERTa & 88.69 & 89.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different pre-trained models.
In this paper, we aim to enhance the energy efficiency of WSNs, improve the route reliability, and balance the energy consumption among sensor nodes. To this end, a reliable energy-efficient multi-level routing algorithm for wireless sensor networks (REEMR) is presented, where fuzzy Petri nets are introduced to overcome the above shortcomings. REEMR parameters can be determined more accurately and dynamically by fuzzy reasoning using fuzzy Petri nets. Experimental results demonstrate that the presented algorithm can provide significant energy savings while achieving reasonably high performance for WSNs. Our main contributions include  
1. Fuzzy production rules are mapped into fuzzy Petri nets for representation of a network topology. 2. A cluster head selection mechanism is developed. This mechanism considers the residual energy, number of neighbors, and centrality of each node and uses fuzzy Petri nets for cluster heads selection. 3. Using fuzzy Petri nets and a reasoning mechanism, a reliable multi-hop routing algorithm, which creates routes among cluster heads, is developed. 4. The resulting reliable energy-efficient multi-level routing (REEMR) protocol is implemented and evaluated through simulations. The remaining of the paper is organized as follows: In Section 2 we review the relevant work. In Section 3, background on fuzzy Petri nets for the representation of network topology is provided. In Section 4, the REEMR protocol, which includes a clustering algorithm and a reliable routing algorithm among cluster nodes, is presented. In Section 5, we evaluate the performance of REEMR protocol and compare it to several protocols. Finally, Section 6 concludes the paper.
## 2 Materials and Methods
### Data Processing and Comparison Method
#### 2.3.1 Trajectories Computations from GS14 Data
The most important RNX2 RTKP solution data are: latitude, longitude, and ellipsoidal height, a quality flag (in this case 1 for a fixed solution and 2 for a float solution), the number of used satellites, all the variance and covariances related to the 3D solution uncertainties. All the geographic coordinates were projected to the RDN ETRS89-ETRF2000 reference system using the Python PyProj package [50]. The PyProj package is the Python interface to PROJ.4 library and, for the purposes of this study, can be considered accurate, since it is also involved in the \"VERTO\" online coordinate converter [51], courtesy of the IGM (Italian Geographic Military section). Coordinate projections were performed, since we found it more convenient to assess the comparisons by using projected coordinates. It is worth mentioning that, as a consequence of the simulated reduced operativeness for the three constellations, not all the RTKLIB solutions, composed by only a few computed epochs, were able to produce a significant output trajectory. For this reason, all the RTKLIB solutions characterized by a percentage of fixed epochs less than 1% of the whole survey (almost 4000 epochs, sampling rate 1 Hz) were excluded from the subsequent computation. Table 2 shows the number of remaining solutions after this first filtering process. Lastly, for the GS14 RNX2 RTKP trajectory computation, the whole set of acquired epochs was used to compute the trajectories point by point. As mentioned in Section 2.2 (Survey experimental design), the static epochs recorded were used only to increase the probability to achieve an RNX2 RTKP fixed solution before the beginning of the kinematic path. According to the purpose of this study, for the subsequent comparisons (see Section 2.3.3), only the RNX2 RTKP solution data related to the kinematic epochs of the survey were considered.
# Estimating RQD for Rock Masses Based on a Comprehensive Approach
###### Abstract  
Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size \(D\), beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size \(D\) (RQD(\(D\))) and (4) determining the representative RQD (rRQD), defined as the specific value which, once \(D\) exceeds, the RQD(\(D\)) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates.  
Rock Quality Designation; class ratio analysis; Confidence Neutrosophic Number Cubic Value; discrete fracture network simulation Article
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools
### Resource Management and Scheduling  
Effective resource management and scheduling optimize network performance. OPNET [37], NS-3 [38], OMNeT++ [39], and excel in this area, offering extensive protocol libraries and simulation frameworks to evaluate scheduling algorithms and resource allocation strategies, commonly adopted in both academic research and industrial practice for network optimization. These tools support detailed protocol-driven scheduling simulations under varying network conditions. MATLAB, through scripting and optimization toolboxes, provides algorithmic flexibility but lacks integrated protocol modeling, used frequently for heuristic and AI-based scheduling developments. STK supports manual or scripted resource scheduling without automatic optimization capabilities, typically applied in preliminary scenario planning.