esa-sceva/satcom-chunk-collection huggingface dataset:
# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 4 Active Learning and Object Detection: A Literature-Driven Multi-Perspective Analysis
### (Rq5) Other Techniques  
#### 4.5.1 Combining Localisation and Classification  
There are works demonstrating that an object detector's localisation and classification output should be considered in AL. For example, two metrics were proposed in [5]. The former, localisation tightness, uses the intersection over union (IOU) ratio between the region proposal and the final prediction to select images with inconsistency between the classification and the localisation. The second, localisation stability, focuses on the variation of predicted localisation behind images corrupted by noise (Figure 12). It measures the predicted bounding boxes variation when noise is added to the original image.  
In subsequent works [48], the uncertainty was efficiently estimated, making a single forward pass in a single model and considering both the classification and detection information (Figure 13).  
Additionally, instead of a single value prediction for each convolutional neural network output, the authors designed an output layer to predict the probability distribution of the whole image and introduce a new loss function in the training of the Gaussian mixture model (GMM), increasing the overall performance (its code is available at [[https://github.com/NVlabs/AL-MDN](https://github.com/NVlabs/AL-MDN)]([https://github.com/NVlabs/AL-MDN](https://github.com/NVlabs/AL-MDN)) accessed on 20 July 2023).  
Document layout detection is addressed using OD in [14]. In this case, AL selects regions considered the most ambiguous by the object detector. The perturbation-based scoring step considers object position and category to evaluate the prediction ambiguities of the DL model. Additionally, the authors proposed a semi-automatic correction algorithm, which rectifies out of selection predictions, with minor supervision, based on prior knowledge of layout structures, i.e., identifying duplicated and false negative predictions.  
Figure 12: Overview of the AL algorithm proposed in [5].
# Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances +
## 5 Results and Analysis
However, the flip side is that the cheaper instances are also the less reliable ones. MW-DBS is a scheduling algorithm agnostic to the reliability of SIs, and both MW-DBS and ECPTR ignore the eventual high delay in acquiring spots which is of paramount importance to consider when executing deadline constrained tasks. This explains why the performance of MW-DBS degrades substantially as spots are chosen to allocate tasks. Similar behavior in terms of performance degradation occurs for ECPTR regarding SIs. In fact, ECPTR was designed to exploit redundancy in executing tasks to deal with the problem of premature interruption of SIs, but it is incapable of dealing with the delay in acquiring spots. Moreover, the results obtained for the case of MW-DBS-OBS configuration are in line with those of [14], in which case tasks were scheduled onto Amazon EC2 SIs. Diversely, MISER makes a better usage of unreliable SIs and spot block instances, as was shown for the cases of *-OB and *-OBS. By spending more on renting powerful yet cheaper and less reliable resources, MISER strongly accelerates the execution of tasks. Tasks executing faster are less susceptible to interruption of SIs, as time goes by. Additionally, the increasing number of instances as configuration evolves from *-O, to *-OB, and then to *-OBS, evidences that tasks initially scheduled to spot resources might need to be rescheduled onto eventually new on-demand instances because spot requests take too long to be fulfilled. Moreover, spot block instances are restricted to running for six hours at most, which can make impossible the scheduling of long tasks onto free slots of already running instances. In turn, the scheduling of tasks onto SIs is limited by their reliability, aiming at avoiding interruption, which forces allocation of tasks onto new spots over free slots of long-running ones. Table 3 shows a detailed characterization of MISER, in the configurations of *-OB and *-OBS, in terms of unfulfilled SIs and spot block requests, additional number of schedules in relation to MISER-O configuration, and number of tasks migrated away from SIs reclaimed by EC2. The number of unfulfilled spot requests was higher for *-OBS configuration than that of *-OB, since the delay in acquiring spots affected both spot block and SIs. Additionally, migration of tasks was performed for *-OBS only because MISER had to deal with the interruption of SIs. This justifies the higher number of additional schedules observed for the MISER-OBS configuration. Considering that 190 workflows, totaling 5370 tasks, were submitted to the cloud, the additional schedules and the number of migration  
of tasks remained relatively small. Moreover, MISER has shown itself to efficiently use spots in order to strongly reduce the task execution time without compromising costs, thereby successfully completing 100% of tasks for almost all experiments.
## 5 Case Studies of Dima Optimization
### Case Study of Single Objective Optimization  
[PERSON] et al. [54] considered a representative aeronautical system, where there are 20 end systems, 73 connections and 4187 signals to be routed. In total, there are 56,088 binary variables in the formulation for optimizing availability (OD), bandwidth (OB), number of nodes (ON), number of edges (OE), subject to constraints of availability (D), bandwidth consumption (B), segregation (S) and virtual link configuration (VL).  
The authors measured the processing time of lp_solve and SAT4J PB for solving the optimization problems under a particular objective and certain constraints, in which the experiments were conducted on an Intel Core 2 Duo CPU, 2.93 GHz with 8 GB RAM. When the number of constraints is smaller and a simpler objective function is considered, for example, in the case of availability optimization (OD), lp_solve is more efficient than SAT4J PB; however, when the number of constraints is larger, for example, in the case of optimizing nodes and edges (ON and OE) with segregation constrain (S), SAT4J PB is much more efficient. For bandwidth optimization (OB), it takes both solvers more processing time, with more than one hour using LP_solve and SAT4J to solve, respectively. As a result, the running time of various objective functions and constraints differs from each other; it depends on what sort of optimization problem is to be solved and what sort of optimization method is to be leveraged.
# Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
## 5 Discussion  
In this study, we examined the named entity recognition of forest disease texts. The research mainly includes two parts: multi-feature embedding and the named entity recognition method for forest disease texts based on Transformer-BiGRU-CRF.  
The first part of the paper is the discussion of multi-feature embedding. We analyzed the characteristics of the texts and determined that many diseases and fungicides have specific radicals, a large number of entities do not have obvious boundary characteristics, and there are certain rules in the part-of-speech distribution of some entities. Three features, namely, word boundary features, partial radical features, and part-of-speech features, were thus selected for multi-feature embedding experiment verification.  
The second part of the paper is the discussion of the named entity recognition method for forest disease texts based on Transformer-BiGRU-CRF. We found that the RBP-Transformer-BiGRU-CRF model proposed in this paper outperformed the other mainstream algorithms under two conditions of multi-feature embedding and only character vector embedding. The experimental results show that this method can effectively model the text of forest diseases and has suitable adaptability for the forest disease text. The introduction of the transformer encoder can address the problem related to the long-term dependence of the model. The addition of the BiGRU network structure improved the ability of the model to supplement deep features, had a better extraction ability for hidden features, and made it able to adequately solve the task of named entity recognition in forest disease texts.  
There are numerous domain-specific entity concepts and rare words in the collected forest disease texts. RBP-Transformer-BiGRU-CRF not only takes into account the text features of forest diseases, but also makes full use of transformer abilities of the excellent parallel processing and global feature extraction and BiGRU abilities of the higher computing speed and bidirectional feature extraction compared with BiLSTM under the same performance. This model has achieved satisfactory results in the named entity recognition task of Chinese forest disease texts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **P** & **R** & **F1** \\ \hline RBP-BiLSTM-CRF & 89.79 & 85.64 & 87.67 \\ RBP-BiGRU-CRF & 91.74 & 89.16 & 90.43 \\ RBP-Transformer-BiLSTM-CRF & 90.11 & 92.37 & 91.20 \\ RBP-Transformer-BiGRU-CRF & 93.16 & 92.97 & 93.07 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the different methods for multi-feature embedding.
# Single-Lens Imaging Spectral Restoration Method Based on Gradient Prior Information Optimization
## 6 Conclusions  
Spectral imaging information is composed of the two-dimensional spatial image information of natural objects and one-dimensional spectral information. It not only provides a visual display of the detailed textures of objects, but also characterizes target properties. With increasing demand for spatial and spectral resolution, the complexity, volume, and weight of systems have been continuously increasing, leading to difficulties in system installation and increased costs. However, with the application of computational imaging technology in the field of spectral imaging, these problems have been alleviated to some extent. Single-lens imaging is a type of computational imaging technology known for its advantages of high throughput, low complexity, and low cost. The key to obtaining spectral information based on single-lens imaging technology lies in the spectral imaging restoration algorithm. In this paper, we give details of an in-depth study of a spectral imaging restoration algorithm based on single-lens imaging and a proposed spectral imaging restoration method based on the optimization of gradient prior information. By optimizing gradient information, the quality of restored spectral images was improved. Compared to the unoptimized algorithm, we evaluated the quality of the restored images and spectra using RMSE, SSIM, SMSE, and SCC. Both the simulation and experimental results demonstrate the superior quality of the algorithm proposed in this study, thus confirming its effectiveness and accuracy.  
The algorithm presented in this paper has already been validated for its feasibility in single-lens imaging systems. However, whether it is applicable to multi-lens imaging systems is unknown. Future research will explore this aspect and further optimize the regularization terms to enhance the method's generality. The proposed single-lens imaging system is designed to be portable and practical, so it places high demands on its volume and data processing speed. Currently, there is room for further optimization of the computational complexity of the algorithm presented in this paper. However, reducing computational complexity may decrease the quality of the restoration, so striking a balance between the two is one of the issues that needs to be addressed in the future.  
Conceptualization, P.H., Z.L., Y.T. and Q.L.; methodology, P.H.; software, P.H. and Y.B.; investigation, Z.L., J.W. and Y.T.; resources, J.W.; data curation, P.H.; writing--original draft preparation, P.H.; writing--review and editing, P.H. and Q.L.; supervision, J.W. and Q.L.; project administration, Z.L., J.W. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA28050401).  
Not applicable.  
The authors declare no conflict of interest.
# Modeling and Simulation of IGCC Considering Pressure and Flow Distribution of Gasifier
## 3 IGCC System Modeling
### Steam System Model  
The steam system, including the heat recovery steam generator (HRSG) and steam turbine, is described as several simplified drums, heat exchangers and turbine stage groups.  
The state equations of pressure and enthalpy for the superheater are written as:  
\[\frac{dp}{d\tau}=\frac{G_{in}h_{in}-G_{out}h_{out}+Q}{V\left(1+c_{p}/R\right)} \tag{12}\]  
\[\frac{dh}{d\tau}=\frac{G_{in}h_{in}-G_{out}h_{out}+\left(1-R/c_{p}\right)\left( G_{out}-G_{in}\right)h_{2}}{\rho\left(1+R/c_{p}\right)} \tag{13}\]  
The modeling of the drum, evaporator and economizer is similar and is introduced in detail in Reference [20].  
The steam turbine model is based on the Frugal formula, which can be written as:  
\[\frac{G}{G_{0}}=\sqrt{\frac{p_{in}^{2}-p_{out}^{2}}{p_{in,0}^{2}-p_{out,0}^{2 }}}\cdot\sqrt{\frac{T_{in,0}}{T_{in}}} \tag{14}\]  
where subscript 0 stands for the design parameters.  
Similar to the gas turbine, the heat exchanger models calculate the pressure and temperature, and then send the results to the steam turbine models for calculating the flow rate. It is a non-iterative model because all the equations could be solved through the results of the last simulation step.
## 4 Discussion
### Study Limitations and Future Improvements  
Despite its valuable insights, this study has several limitations. First, we focused solely on text-based multiple-choice questions, excluding multimodal elements such as patient imaging and waveform analysis. This choice was necessary to ensure a fair evaluation across all models, including those without image capabilities. However, multimodal LLMs, like GPT-4V and Gemini, are gaining traction in medical AI and have shown promise in visual diagnostic tasks [31; 33]. Future research should include multimodal benchmarks to better simulate real-world clinical conditions.  
Second, we evaluated only nine LLMs, and emerging open-source models (e.g., those fine-tuned on biomedical corpora) were not included. All models were tested in zero-shot mode without prompt engineering or domain-specific fine-tuning. While this ensures a standardized baseline comparison, it may underestimate the full potential of LLMs when they are adapted to specific tasks or domains [23].
# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 3 Jet Substructure and Deep Tagging
### ParticleNet  
The ParticleNet algorithm [30] is a dynamic graph CNN used for jet-tagging problems. A jet is represented as an unordered, permutation-invariant set of particles. A jet is represented as a particle cloud, having, on the one hand, all the advantages and flexibility of a particle-based representation, and on the other hand, the algorithmic strength of the point-cloud representation of 3D shapes used in computer-vision applications. A regular convolution operation is in the form \(\sum_{j}K_{j}x_{j}\), where \(K_{j}\) is the kernel, and \(x_{j}\) are the features of each point. However, this convolution is not invariant under point permutation, which is needed in point-cloud representations. The EdgeConv operation [31] connects each point to the \(k\)-nearest neighbouring points and, thanks to the edge function and the symmetric aggregation operation, permutationally symmetric operation on point clouds is obtained. Two different versions of the algorithm are used: _ParticleNet_ and _ParticleNet-lite_. The first one uses three EdgeConv blocks with \(k=16\) nearest neighbours, while the second one uses just two EdgeConv blocks and \(k=7\). The algorithm is used for top-tagging, i.e., to identify jets from hadronically decaying top quarks, and for quark-gluon tagging, i.e., discriminating between jets initiated by quarks and gluons. For the top-tagging algorithm, only jets with \(R=0.8\) and reconstructed with the anti-\(k_{t}\) algorithm are considered; for each jet, up to 100 constituents with high \(\mathrm{p_{T}}\) values are taken into account. Only kinematic information is used for each particle, and, compared to various pre-existing algorithms, it shows better performance, with an area under the curve \(AUC=0.9858\), as seen in Figure 7a.  
ParticleNet quark-gluon tagging is performed on anti-\(k_{t}\) jets with \(R=0.4\). Moreover, two different sets of variables for each particle are used: in the first one, only variables related to four-momentum are taken into account, while in the second one, there is also particle identification information (PID). PID information leads to better performance in jet tagging. As shown in Figure 7b, the ParticleNet algorithm with and without PID information leads to the best results with, respectively, \(AUC=0.840\) and \(AUC=0.828\).  
Figure 7: Performance comparison in terms of ROC curves on (a) top-tagger and (b) quark–gluon tagging.
## 4 Results and Discussion
### The Baseline Leveling Experiment
#### 4.1.3 Tilt Control  
To address the microgravity coupling caused by the baseline tilt, this study performed further tests on drag-free control using the inverted pendulum. In particular, the feedback signal from the 16-bit capacitive displacement sensor, sampled by the NI636 card, was utilized. The PID algorithm of the host computer controlled the extension and contraction of the piezoelectric ceramic based on the angle between the reference detected by the inverted pendulum and the gravity field. The position time series was analyzed using LTPDA [21], and the displacement ASD of the inverted pendulum before and after leveling is shown in Figure 15. After closed-loop compensation, the tilt drift of the inverted pendulum at 0.1 mHz was reduced from 5.7 \(\upmu\)m/Hz\({}^{1/2}\) to 0.28 \(\upmu\)m/Hz\({}^{1/2}\). The reason the ASD in the frequency range of 1 mHz to 1 Hz did not decrease is due to the limited sampling resolution  
Figure 13: Reference leveling mechanism based on piezoelectric actuators and lifting platform.  
of the position analog signal. Therefore, after improving the quantization resolution of the feedback, higher-precision baseline leveling can be achieved.  
In summary, compared to the displacement noise spectrum monitored by the inverted pendulum, the microgravity from the baseline tilt can be reduced by 1 to 2 orders of magnitude after active compensation using the piezoelectric ceramic, demonstrating the effectiveness of drag-free control.  
Figure 14: Torsion pendulum and inverted pendulum displacement signals modulated by piezoelectric actuator square wave.  
Figure 15: Comparison of displacement ASD of inverted pendulum before and after tilt control.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 5 Factors Affecting Acoustic Methods in Pipeline Localization
### In Pipe Medium and Pipe Material  
Previous work has shown that fluid-dominated wave can be selected as the target wave when applying the pipe excitation method to locate buried plastic water pipes [69]. However, the feasibility of this method may be different for various in-pipe medium and pipe materials. Studies on the feasibility of the pipe excitation method in locating buried gas pipe has been conducted in [127]. The numerical results revealed that vibro-acoustic detection may not work well in gas pipe detection because of the weak coupling between gas and pipe wall, making the gas-dominated wave not radiate into the surrounding soil as effectively as the water-dominated wave. In addition, plastic pipes are easier to excite than metal pipes as they possess less density and less inertia. Therefore, the rank of feasibility on locating buried pipes from high to low is plastic water pipes, metal water pipes, plastic gas pipes, and finally metal gas pipes. For detection techniques with rationales based on the property difference between the presence of buried pipe and surrounding soil, like point vibration measurements [90], gas pipes are easier to locate than water pipes, as the property of gas is more different from the surrounding soil.
## 5 Results and Discussions
Subsequently, in Figure 8, the resulting decision regions is shown, and over these regions, the set of samples that have been considered during the validation process are projected. On the other side, the associated diagnosis probability can also be estimated for those samples \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & \\ \cline{3-13}  & & \multicolumn{8}{c}{**Training**} & \multicolumn{8}{c}{**Validation**} \\ \cline{3-13}  & & **HC** & **S1** & **S2** & **S3** & **S4** & **S5** & **HC** & **S1** & **S2** & **S3** & **S4** & **S5** \\ \hline \multirow{4}{*}{**N-based classifier**} & **HC** & 360 & 0 & 0 & 1 & 0 & 0 & 89 & 0 & 0 & 0 & 0 & 0 \\  & **S1** & 0 & 351 & 1 & 0 & 0 & 0 & 0 & 89 & 1 & 0 & 0 & 0 \\  & **S2** & 0 & 9 & 359 & 0 & 0 & 0 & 1 & 1 & 89 & 0 & 0 & 0 \\  & **S3** & 0 & 0 & 0 & 352 & 0 & 5 & 0 & 0 & 0 & 88 & 0 & 2 \\  & **S4** & 0 & 0 & 1 & 1 & 360 & 0 & 0 & 0 & 0 & 0 & 90 & 0 \\  & **S5** & 0 & 0 & 0 & 6 & 0 & 355 & 0 & 0 & 0 & 2 & 0 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The confusion matrix achieved during the training and validation of the proposed NN-based structure. Figure 7: Two-dimensional PCA projection performed over all considered conditions: HC, S1, S2, S3, S4, and S5, and applied to the statistical features estimated from vibration signals in both analysis domains: (**a**) DT and (**b**) DF. that are associated with misclassification problems, and the analysis of such associated diagnosis probability may lead to a re-classification by assigning its appropriated true class.
###### Abstract
[6], which uses a concentration-based adaptive threshold detection method. In another similar work for adaptive threshold detection in molecular communications [14], the authors proposed a simple algorithm where in each step, the number of molecules that arrived is compared to the number of received molecules during the previous bit period. If in the previous step the number of molecules is less than the one in the current step, the algorithm outputs a message reception, and in contrast to fixed threshold detectors, it exploits the intersymbol interference in the threshold determination. Under the same rational, in this work a simple and applicable learning automaton-based algorithm is presented as a suitable tool to reach maximum transfer data rate in diffusion-based wireless BNs (the term wireless is defined as nanonodes not interacting via the established biological signaling pathways but mainly via released information molecules). The algorithm does not depend on predefined thresholds for the detection of the incoming messages, but keeps count of the times the concentration of the information molecules near the receptor increases and subsequently decreases. Via the learning automaton process, nodes rectify the required numbers of ups and downs for a message detection. Furthermore, in every successful iteration, the aforementioned criteria are amended so as to provide faster response and higher transfer data rates between transmitter and receiver. The proposed scheme, in comparison to other solutions, does not rely on prior knowledge of the diffusion coefficient of the signaling molecules or the quantity of transmitted molecules, as in [13]. Moreover, it does not need the presence of a time synchronization infrastructure, as in [14], compensating for the additional processing requirements. The organization of this paper is as follows: Section 2 demonstrates the proposed simulated system model and its theoretical foundations. In Section 3, the learning automaton is presented, and in Section 4 the performance of the protocol is evaluated. In Sections 4.1 and 4.2, major points from the results are discussed, along with possible future extensions. Finally, Section 5 concludes this work.