esa-sceva/satcom-chunk-collection huggingface dataset:
# Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
## 1 Introduction
One of the latest examples among the topic-modeling algorithms is BERTopic--an advanced algorithm developed by Grootendorst in 2022--which harnesses the power of the embeddings obtained from BERT, a well-known transformer architecture [28]. BERTopic can segment a dataset of text documents--an operation commonly known as clustering--by their semantic content and extract a series of representative keywords, which BERTopic then concatenates to create a topic label for each cluster [29]. Compared to previous topic-modeling algorithms, transformer-based architectures such as BERTopic capture contextual meanings much more effectively, especially on smaller amounts of data, which, in turn, results in more coherent and interpretable topics [30]. These new methods are being increasingly applied to the analysis of scientific literature [31; 32; 33; 34]. Based on the available data in the literature, we assumed that topic modeling could also be used as an intermediate step within the literature screening process to filter out undesired papers that may have been retrieved with a conventional keyword-based search so to clean the dataset before investigators manually screen it. To this purpose, algorithms providing a contextual interpretation of words and sentences appeared as the only viable option to improve the results of keyword-based searches, because they might be able to correctly interpret the meaning of ambiguous expressions depending on their context of use. BERTopic, therefore, appeared as a valuable choice to pursue this goal. The main aim of the present paper is thus to explore how BERTopic can be applied effectively on datasets of the scientific literature and identify relevant papers for systematic reviews. To do that, we used two datasets that were recently used for two published systematic reviews in the dental field. We analyzed them separately with BERTopic,identified topic clusters within these two corpora, and investigated whether individual non-relevant topic clusters could have been discarded from the screening without negatively affecting the outcome of the review. The approach we propose might prove helpful in filtering out non relevant articles from further assessment, thereby enhancing the speed and efficiency of the search process. While topic modeling has been applied in various fields, to the best of our knowledge, our approach is unique in leveraging BERTopic as an intermediate step to segment scientific datasets for systematic reviews in the biomedical (dental) field and filter out irrelevant papers retrieved by conventional keyword-based searches.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 5 Reinforcement Learning for Routing Optimization
### Literature Review
In the first stage, a GNN-based DRL agent is used to generate the initial routing solution. In the second stage, a local search algorithm is used to improve the solution without adding computational overhead to the optimization process. The proposed ENERO operates in real-world dynamic network topologies in 4.5 s on average for topologies up to 100 edges and outperforms the shortest available path heuristic baseline in terms of the link utilization ratio. Although the GNN-based DRL framework has been proven effective in the above studies, as discussed, DRL scales poorly with the problem size and complexity, which is further considered in [120]. To solve this challenge, evolutionary strategies are introduced for the first time in the training process of DRL agents and help to speed up the training time by 128 and 6 times for two network topologies, namely, NSFNET and GEANT2, respectively. With the joint minimization of end-to-end delay and packet loss rate as the routing objective, a routing optimization policy based on GCN and DDPG is proposed in [121], in which the GCN is responsible for network topology structure information extraction and DDPG is responsible for making routing decisions. The proposed approach outperforms OSPF algorithm, DRL-TE strategy, and DDPG routing algorithm in terms of average end-to-end delay and packet loss rate. To enhance the robustness against topology changes, GAPPO is proposed for network routing in [122], which combines GAT and PPO. The state, action and reward in DRL are carefully designed and the experiments demonstrate a superior robustness performance of the proposed approach against different link failures, which outperforms benchmark algorithms with a lower packet loss ratio and a lower end-to-end delay. The Actor-Critic architecture is further explored in [123] for network routing and GCN is used to update the link weight of the whole network. The proposed scheme outperforms baselines in terms of network average end-to-end delay, packet loss rate, and throughput. Knowledge-defined networking is leveraged to handle the dynamic characteristics of the network topology in [124], in which message-passing deep reinforcement learning (MPDRL) is proposed for routing optimization. The message-passing mechanism in MPNN is used to extract exploitable knowledge and DRL is used to make routing decisions. Experiments show that MPDRL achieves the load balance of network traffic and improves network performance, compared with baseline methods. To deal with the network topology changes, a GNN-based multi-agent DRL routing scheme is proposed in [125], in which the deployed multi-agent approach is capable of routing in dynamic network conditions without retraining. The proposed distributed approach is compared with traditional routing baselines as well as multi-agent learning without GNN, and experiment results show that the proposed achieves fewer flow set collisions. The relevant studies with reinforcement learning for routing optimization are summarized in Table 5.
## 3 Results
#### 3.1.4 Berry and Must Quality Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{**3-Year Period (2018–2020)**} \\ \hline \multirow{3}{*}{**Clone**} & \multirow{3}{*}{**Colour**} & \multirow{3}{*}{**Tone**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Extractable**} & \multirow{3}{*}{**Polyphenols**} & \multirow{3}{*}{**TPI**} & \multirow{3}{*}{**AE Index**} & \multirow{3}{*}{**SM Index**} & \multirow{3}{*}{**Tannins** (mg L\({}^{-1}\))} \\  & & & **Anthos-** & & & & & & & \\  & & & **cyanins** & & & & & & & \\ \hline
4 & 5.0a & 0.94 & 740 & 341 & 39.6 ab & 19.9 & 52.9 & 65.4 ab & 3.39 ab \\
94 & 5.4 ab & 0.97 & 704 & 340 & 36.8a & 18.1 & 51.3 & 62.7a & 3.51 ab \\
188 & 5.3 ab & 0.95 & 761 & 335 & 45.3b & 21.4 & 56.1 & 68.4 bc & 4.34b \\
276 & 6.7d & 0.97 & 682 & 288 & 38.2 ab & 17.7 & 56.9 & 64.2 ab & 3.55 ab \\
360 & 6.2 cd & 1.06 & 671 & 321 & 42.2 ab & 21.0 & 53.4 & 72.0c & 7.39c \\
372 & 5.8 bc & 0.94 & 607 & 343 & 34.0a & 19.8 & 55.3 & 67.0 abc & 3.58 ab \\
373 & 6.9d & 0.95 & 606 & 347 & 38.6 ab & 20.6 & 55.0 & 67.9 abc & 2.72a \\
**Treat. ** & & & & & & & & & \\ Control & 5.7 & 0.99 & 621 & 307 & 38.1 & 19.2 & 54.2 & 66.9 & 3.98 \\ Stress & 6.0 & 0.94 & 745 & 355 & 40.4 & 20.3 & 54.6 & 66.7 & 4.16 \\
**Clone \(\times\) Treat. ** & & & & & & & & & \\
4 & Control & 4.7 & 0.94 & 680 abc & 351 & 43.8 bcd & 19.0 & 51.4 & 65.4 & 3.66 \\  & Stress & 5.2 & 0.95 & 799 bc & 330 & 35.4 abc & 20.8 & 54.3 & 65.5 & 3.12 \\
94 & Control & 5.7 & 1.01 & 535 ab & 277 & 34.2 ab & 19.0 & 50.1 & 66.9 & 3.24 \\  & Stress & 5.0 & 0.93 & 872c & 404 & 39.4 abcd & 17.2 & 52.6 & 58.6 & 3.78 \\
188 & Control & 5.3 & 0.99 & 714 bc & 335 & 44.6 cd & 21.0 & 54.8 & 67.6 & 4.55 \\  & Stress & 5.2 & 0.91 & 808 bc & 335 & 46.0 cd & 21.8 & 57.4 & 69.2 & 4.13 \\
276 & Control & 5.8 & 1.01 & 733 bc & 261 & 35.2 abc & 15.7 & 58.0 & 62.6 & 3.61 \\  & Stress & 7.5 & 0.93 & 631 abc & 315 & 41.3 abcd & 19.7 & 55.8 & 65.7 & 3.50 \\
360 & Control & 6.0 & 1.00 & 594 abc & 274 & 34.4 abc & 22.8 & 52.8 & 73.2 & 6.85 \\  & Stress & 6.5 & 1.12 & 767 bc & 368 & 50.0d & 19.2 & 54.1 & 70.7 & 7.93 \\
372 & Control & 5.6 & 0.99 & 405a & 306 & 37.7 abcd & 19.2 & 52.7 & 65.8 & 2.99 \\  & Stress & 6.0 & 0.88 & 809 bc & 379 & 30.4a & 20.5 & 57.8 & 68.1 & 4.17 \\
373 & Control & 6.8 & 1.01 & 683 abc & 343 & 36.9 abcd & 18.1 & 59.8 & 66.9 & 2.98 \\  & Stress & 7.0 & 0.89 & 528 ab & 351 & 40.3 abcd & 23.1 & 50.2 & 68.9 & 2.46 \\ \hline \multicolumn{2}{c}{**ANOVA**} & \multirow{3}{*}{Clone} & \multirow{3}{*}{**Tannins**} & \multirow{3}{*}{**ns**} & \multirow{3}{*}{**ns**} & \multirow{3}{*}{**ns**} & \multirow{3}{*}{**ns**} & \multirow{3}{*}{**\(\ast\)**} & \multirow{3}{*}{**\(\ast\)**} \\  & & & & & & & & & \\ \cline{1-1} \cline{6-
# I3D-Shufflenet Based Human Action Recognition
## 1 Introduction
Motion stream (ResNet-50) [11] extracts the spatial-temporal information by introducing the connection between spatial-temporal flow in a two-stream network with fusion blocks. S3D-G (separable 3D CNN) [12] builds an effective video classification system, seeks to strike a balance between speed and accuracy, and replaces many 3D convolutions with low-cost 2D convolutions. MFNet (Multi-Fiber Networks) [13] proposes a Multi-Fiber architecture, which cuts a complex neural network into a lightweight network or a collection of optical fibers running through the network. ARTNet(Appearance-and-Relation Networks) [14] is constructed by stacking multiple general building blocks called SMART to simultaneously model the appearance and relationship of RGB input in a separate and explicit manner. FASTER [15] is used for feature aggregation of spatial-temporal redundancy. Its framework can integrate high-quality representations in expensive models to capture subtle motion information, and the lightweight representations in cheap models to cover scene changes in videos. The existing C3D (3 DConvolutional Networks) neural network has a wide application. The I3D (Inflated 3D ConvNet) neural network is improved based on the C3D neural network [16], and the recognition speed and accuracy performance of the network is greatly improved. However, both the C3D and I3D neural network have a traditional way for convolution, some channels may be less useful information and consume the computational power. The article is inspired by the ideas of GoogleNet and Shufflenet. The original convolution kernel in I3D is replaced by a two-layer convolution kernel, which has the same effect but with lower calculation amount. The channel fusion method is incorporated into the I3D network. The feature extracted by the proposed model will be exchanged with different channel features through shuffle operation [17], and more useful information will be used to improve the performance of human action recognition. This article mainly includes four parts. The 3D convolutional network is introduced in Section 2, the proposed model is introduced in Section 3, and the Section 4 is the experiments. Finally, it is the conclusion.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 2 Background
### Fuzzing Proprietary System  
Fuzzing proprietary systems has many challenges compared to open-source-based fuzzing. Open source allows fuzzers to recompile and instrument the target code for measuring various features in their fuzzing process, thus enhancing performance. However, most proprietary applications, such as IoT and UAV firmware, cannot be recompiled and instrumented. One way to apply coverage feedback in such closed-source applications is by using an emulator [17] such as QEMU. Unfortunately, only a few proprietary systems can be emulated in reality. Even worse, some of these firmware are cryptographically signed and encrypted, thus preventing them from being modified, even at the binary or assembly level. Our research can be a reference for designing fuzzers for such systems.
## 4 Challenges and Future Directions
### Migration from Legacy Systems  
The old-fashioned legacy systems that are being used at present in the underwater environment are more vulnerable to data modification and thefts. The applications of underwater things benefit from implementing the blockchain as a solution that adapts DLT. The amalgamation of the IoUT and blockchain projects tends to be complex since both involve niche skills. Finding appropriately skilled developers for implementing blockchain technology in an organization is hard [104]. The governance structure of the blockchain in the IoUT defines the participants, data ownership, exit and entry criteria, and conditions for information sharing among the participants. The tools required for building the blockchain ecosystem are currently in progress. This compounds the complexity of integrating the technology with the existing systems. Migrating from older systems is expensive as the application has to rely heavily on the experts to handle the problems that occur in the initial stages of deployment, thus making it a time-consuming process.
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 2 Background
### Autonomous Vehicles  
Autonomous, or driverless, vehicles enable the replacement of some or all human interaction by electronic devices [13]. Advancements in the sensing and decision-making capabilities of autonomous vehicles have allowed for significant improvements to be made in transportation [14]. A major capability that distinguishes autonomous vehicles from traditional ones is their advanced external sensing capabilities [15]. One of the most critical technologies in autonomous vehicles is LiDAR, which is used to remotely sense obstacles' distance [16]. This proves to be critical for autonomous vehicles to sense and interpret their surroundings, including nearby vehicles and road obstacles [16]. Other key technologies that autonomous vehicles leverage include radar, cameras, and ultrasonic sensors, which are most effective when paired with each other [17].  
Another component that is key to successful autonomous vehicle deployment is the communication methods between vehicles and the network infrastructure [18]. Communications between vehicles may be classified as single- or multi-hop. The single-hop system is ideal for short-range communication, while multi-hop is better for long-range communication [19]. Each these is dependent on the nature of connectedness in the system, which is discussed further below. Both forms of connectivity are also critical for applications such as collision avoidance and passing assistance [19].
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance
### Determination of the Globally Optimal Facility Locations  
After the construction of the candidate locations for the facilities, what we need to do is to select the optimal facility locations from the mesh points. We formulate the mathematical model for the multi-facility location problem, which is also called the location-allocation problem. Besides, the construction of the candidate locations for the facilities makes it possible to determine the globally optimal locations for the capacitated facilities. Then, we present the different models for different facility location problems.
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 5 Soil Vibration Response above the Buried Pipeline
### Pipeline Dynamic Response Characteristics  
Analysis shows that the maximum impact of the blast source was directly above the pipeline, so the PES values at the corresponding monitoring points were extracted to analyze the dynamic response law (Figure 11). The maximum PES value, 1.87 MPa, appeared directly above the burst source, and the minimum value, 0.87 MPa, appeared at a \(-\)60 m horizontal distance. The effective stress on both sides of the burst source decayed exponentially with increasing distance.  
Figure 10: Distribution of the axial surface soil’s _PPV_.  
Figure 9: Distribution of the surface soil’s _PPV_.  
The PES values at each monitoring point of the buried pipeline cross-section were extracted to analyze the dynamic response of the buried pipeline under hazardous construction conditions (blast source directly under the pipeline) (Figure 12). The maximum _PPV_ value that occurred at the top of the pipeline was 2.47 cm/s, and the minimum value was 0.89 cm/s, which appeared at the left waistline of the pipeline. This shows that when the blast wave came from directly below, the top of the pipeline was also more affected by the blast due to the influence of the ground soil. In addition, the maximum PES at the pipeline, 1.53 MPa, appeared at the 45\({}^{\circ}\) point, and the minimum value, 0.42 MPa, appeared at the 225\({}^{\circ}\) point. Stress in the lower part of the pipeline was significantly lower than that in the upperpart. Since the test value of each surveying point was less than the safe working threshold pressure of a DN500 pipe (\(\leq\)4.6 MPa), the tested blasting vibration did not adversely affect the buried pipeline, and the buried pipeline could work normally.
## 3 Results
### Accuracy of Soil Water Content and Soil Dry Density Prediction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Model.**} & \multirow{3}{*}{**Texture**} & \multicolumn{3}{c}{**Accuracy (Correct/Total)**} \\ \cline{3-5}  & & **216 \(\times\) 216** & **432 \(\times\) 432** & **864 \(\times\) 864** \\  & & **Resolution** & **Resolution** & **Resolution** \\ \hline \multirow{6}{*}{WC prediction} & SL & 100\% (20/20) & 90\% (18/20) & 95\% (19/20) \\  & L & 100\% (20/20) & 100\% (20/20) & 35\% (7/20) \\  & SiL & 100\% (20/20) & 100\% (20/20) & 85\% (17/20) \\  & SiCL & 100\% (20/20) & 100\% (20/20) & 100\% (20/20) \\ \hline \multirow{2}{*}{Overall accuracy} & \multirow{2}{*}{100\% (80/80)} & \multirow{2}{*}{97.5\% (78/80)} & \multirow{2}{*}{78.75\% (63/80)} \\ \cline{2-2} \cline{4-4}  & SL & 75\% (15/20) & 60\% (12/20) & 50\% (10/20) \\ \multirow{2}{*}{Dry density} & L & 70\% (14/20) & 65\% (13/20) & 50\% (10/20) \\  & SiL & 65\% (13/20) & 75\% (15/20) & 65\% (13/20) \\ \multirow{2}{*}{prediction} & SiCL & 60\% (12/20) & 37.5\% (6/16) & 40\% (8/20) \\ \hline \multirow{2}{*}{Overall accuracy} & \multirow{2}{*}{67.5\% (54/20)} & \multirow{2}{*}{60.5\% (46/76)} & \multirow{2}{*}{51.3\% (41/80)} \\ \cline{1-1} \cline{5-5}  & & & & \\ \end{tabular}
\end{table} 
Table 1: Classification results by the convolutional neural network model. Figure 12: Quantitative results of soil water content prediction by the convolutional neural network model. Figure 13: Cont.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 3 System Architecture and Key Technologies
### Receiver Cells and Structure  
The receiver being studied is composed of photovoltaic cells specifically designed to convert the laser's monochromatic light, and in this case are also called photonic power converters. The bandgap of their absorber material closely matches the incoming photon energy, allowing higher conversion efficiencies. The cells are arranged on a deployable structure which can be deployed either manually or autonomously on the surface of the Moon. The design of such a cell is presented in [35] and in [36]. Although this type of cell is not currently an off-the-shelf component, like the more widely used solar cells, several European companies and research centers are currently developing this type of technology. Traditional photovoltaic cells are designed to convert a broad range of the solar spectrum and are thus subject to efficiency limitations because of the limited bandgap of the cell materials [37]. This new technology allows to overcome the efficiency limitations of traditional cells by matching the power and wavelength of the laser light with the properties of the photovoltaic cell: in the case of NIR wavelengths, cell structures made of InGaAs material can be used [35; 36]. Thanks to the specifics of the materials of which the cell is composed, efficiencies are more than doubled with respect to traditional materials. At the Fraunhofer ISE, a record conversion efficiency of 68.9% was recently achieved with a very thin gallium arsenide photovoltaic cell under monochromatic laser light [38]. As a result, smaller surface areas are used to convert the same amount of power, with lower mass and cost compared to traditional technologies.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
###### Abstract  
Topology optimization has become a popular tool for designing optimal shapes while meeting specific objectives and restrictions. However, the resulting shape from the optimization process may not be easy to manufacture using typical methods like machining and may require interpretation and validation. Additionally, the final shape depends on chosen parameters. In this study, we conduct a sensitivity analysis of the main parameters involved in 3D topology optimization--penalization and filter radius--focusing on the density-based method. We analyze the features and characteristics of the results, concluding that a machinable and low interpretable part is not an attainable result in by-default topology optimization. Therefore, we propose a new method for obtaining more manufacturable and easily interpretable parts. The main goal is to assist designers in choosing appropriate parameters and understanding what to consider when seeking optimized shapes, giving them a new plug-and-play tool for manufacturable designs. We chose the density-based topology optimization method due to its popularity in commercial packages, and the conclusions may directly influence designers' work. Finally, we verify the study results through different cases to ensure the validity of the conclusions.  
topology optimization; manufacture filter; penalization; filter radius; machining