esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data
## 1 Introduction  
The conveyor that is pulled by a scraper chain and transports bulk materials in the tank is called a scraper conveyor, as shown in Figure 1. In the process of coal mine production, scraper conveyors are used to transport newly mined irregular coal bodies. Scraper conveyors have been widely used in coal mine production because of their long transportation distance, large and continuous transportation volume, easy to realize automatic control and centralized control, and relatively simple structure.  
Figure 1: Scraper Conveyor.  
At present, scraper conveyors are running at full speed, and there is no effective means to adjust the motor speed according to the load. When the load is light or no load, the high-speed operation of the belt conveyor system will seriously wear the mechanical transmission system, shorten the service life of the equipment, and cause a lot of waste of electric energy. When the conveyor is overloaded, it will not only accelerate the aging of the conveyor system, but also cause the coal outlet of the scraper conveyor to be congested [1; 2; 3; 4]. Figure 2 shows the coal flow on the scraper conveyor.  
In recent years, due to the advantages of frequency conversion technology with stable speed regulation, high transient stability, and energy saving, researchers have proposed frequency conversion speed regulation control methods for belt conveyors based on the real-time load of the conveyor belt [5; 6; 7; 8]. To make the conveyor run in the best load state, the instantaneous flow of coal becomes an important indicator for adjusting the running speed of the belt conveyor. As the amount of coal conveyed by the scraper conveyor system is not uniform, it is difficult to accurately measure the instantaneous flow of coal on the belt conveyor [9]. Therefore, the detection of coal flow of the scraper conveyor is one of the hotspots in the current research of relevant scholars.  
To meet the needs of high-precision monitoring of coal flow in mine scraper conveyors, this paper proposes an accurate and economical monitoring method based on binocular speckle camera data. The method monitors the abnormal coal quantity of the scraper conveyor by simplifying the collected speckle data, and has the advantages of low cost, convenient installation, and easy popularization. The rest of this paper is organized as follows: Section 2 reviews the previous literature; Section 3 introduces the technical route studied in this paper; in Section 4, the proposed point cloud data simplification algorithm for speckle structured light data and the proposed coal flow monitoring method are introduced; Section 5 presents a case study and analysis of the proposed method in Yujialiang Coal Mine; finally, Section 6 summarizes and looks forward to the work of this paper.
## 3 Materials and Methods
### Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)  
Quantitative real-time PCR (qRT-PCR) was performed according to protocols described previously [73]. cDNA samples were used as the templates for amplification by using SYBR Premix Ex TaqMan Kit (Tli RNaseH Plus) (TaKaRa, Dalian, China) in Applied Biosystems StepOnePlusTM Real-Time PCR System (Thermo Scientific, Waltham, MA, USA). Primers were designed on the Sangon website ([[http://www.sangon.com/](http://www.sangon.com/)]([http://www.sangon.com/](http://www.sangon.com/)), 14 January 2018) and listed in Table S4. The following PCR program was used: 95 \({}^{\circ}\)C for 30 s, followed by 40 cycles of 95 \({}^{\circ}\)C for 5 s, 60 \({}^{\circ}\)C for 30 s. A heat dissociation curve (60-95 \({}^{\circ}\)C) was checked after the final PCR cycle to determine the specificity of the PCR amplification. The gene expression levels of derivatives of US6-1 relative to the wild-type strain were analyzed using the 2\({}^{-\Delta\Delta\)Ct analysis method [74] (\(\Delta\)_rsh_ vs. US6-1 or the complementary strain vs. US6-1). The 168 rRNA gene was used as internal references to normalize cDNA templates. Negative controls with nuclease-free water as templates for each primer set were included in each run. The expression profiles obtained from the RNA-seq were validated by qRT-PCR of 39 representative genes involved in functional groups including AHL production, sigma factor synthesis, RNA degradation, EPS biosynthesis, PAH degradation, sphingolipid production, cell division and shape, GTP synthesis and other functions. The RNA-seq data were considered valid if they met both two requirements. One was that the gene expression (\(\Delta\)_rsh_ vs. US6-1) was regulated in the same direction (up or down) in both RNA-seq data and qRT-PCR, and the fold change of genes tested by qRT-PCR was log\({}_{2}\)(x) \(>\) 1 or log\({}_{2}\)(1/x) \(<\)\(-\)1 (i.e., a plain fold change \(>\)2) with statistical differences (\(p\) value \(<\) 0.05) [75]. Another was that the gene expression (the complementary strain vs. US6-1) by qRT-PCR was not different significantly or was regulated in the opposite direction in the RNA-seq data. The above valid DEGs were chosen to be clustered. The heat map of their log\({}_{2}\) expression ratios in the RNA-seq data in strain US6-1 and \(\Delta\)_rsh_ was drawn by the software HemI [43].
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 3 Experimental Results and Discussion
### Summary of Experimental Results  
In the case of ST-50, post-cracking stiffness decreased, but the reduction degree was slight, and crack behavior, strain distribution, and failure mode were similar to that of the ST-100. Even though the underside of the rebar was exposed and the half of interlock area was not available, no large bond loss was deemed to occur. On the other hand, in the PL-50 the smaller number of cracks and uniform strain distribution as shown in the result of the PL-0 clearly indicated bond loss. The detachment of main rebar suggested the interlock loss was caused by contact loss between lug and concrete. However, in the case of the ST-50, the main rebar kept contact with upper concrete because of the suppression of relative displacement between the main rebar and concrete by stirrups. Therefore, interlock was maintained.  
Stirrups affected crack location through the confinement effects on the main rebar. Crack location was concentrated around stirrups because interlock between the main rebar and stirrup remained solid, and it kept local bond between concrete and main rebar.  
In total, a sound bond could be maintained as long as contact between lugs and concrete was kept even if the interlock area was half. It was also confirmed that stirrups contributed to keep contact and bond between the concrete and main rebar.
## 2 Related Work and Problem Description  
In this section, the Low Earth Orbit satellite constellation scenario, parallel subnet architecture, and key technologies for time-sensitive business transmission data used in this paper will be introduced.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna
### Comparison with Other Designs  
A comparison between the proposed RFID tag and several tag designs for blood and liquid monitoring is provided in Table 2. As it can be seen in Table 2, only one tag is applied to the monitoring of blood tubes [19], while the others are used for blood bags and different liquids. The tag for the monitoring of blood tubes is of capacitive dipole type, like the proposed tag antenna. However, it is not flexible, and it is applied to a near field RFID reader system (therefore the reading range distance is very small). Among the other tags presented in Table 2, five flexible tags can be used in far-field applications [21; 22; 23; 24; 25]. The first four tags are of inductive dipole type [21; 22; 23; 24] and the last one is of capacitive type [25]. The high permittivity and dielectric loss of blood and water were considered in the design of these tags, which reach reading ranges above 0.79 m. However, unlike the proposed tag, their sizes are not suitable to be attached to the smallest tube (75 mm \(\times\) 13 mm). The design of last tag is circular, and it has a diameter of \(\emptyset\) = 29 mm [25]. Therefore, its size could be adapted to the smallest tube. However, this tag is only tested on flat surfaces in [25], and the effects of the strong bending required by blood tubes are unknown.
## 4 Case Studies
### Power System Reliability Evaluation  
The reliability indexes of the power system at different gas supply volumes are calculated through Monte Carlo simulation and presented in Table 5. To prove the correctness of the model developed, the Monte Carlo sampling method (MCS) and Latin hypercube sampling (LHS) method are applied to simulate the natural gas-transmission-loss event (when the gas supply volume is 10,800 m\({}^{3}\)) and obtain the corresponding reliability-assessment results of the power system, respectively, which is presented in Table 6.  
The results in Table 5 show that when there is an equal reduction in the gas supply volume, \(EDNS\) increases, leading to an increase in the power-load removal due to the fault. When the gas supply volume equals 18,000 m\({}^{3}\), which means no gas supply loss event occurs, no power-load loss in the power system, \(EDNS\), \(SI\), and \(AENS\) equal 0, and \(ASAI\) equals 1. Then when the gas supply is 14,400 m\({}^{3}\), that is, the gas supply is 80% of the original, \(EDNS\) equals 53.35 MW, \(SI\) increases to 0.21, \(ASAI\) decreases to 0.79, and \(AENS\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(EDNS\)/MW & \(SI\) & \(ASAI\) & \(AENS\) \\ \hline
**MCS** & 57.19 & 0.22 & 0.78 & 4.09 \\
**LHS** & 57.48 & 0.22 & 0.78 & 4.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of reliability-assessment results using different sampling methods.  
Figure 6: Electric load loss under the different gas supplies (loads at nodes 2, 3 and 6).  
increases to 3.81. A gas supply reduction leads to an increase in \(SI\) and \(AENS\), indicating that the severity of system faults gradually increases. Additionally, \(ASAI\) decreases, indicating that the power supply availability gradually decreases. These results demonstrate that the reliability of the power system decreases with the gas supply of the gas source, and the power system may not have enough energy to ensure the power balance of electricity.  
Table 6 shows that by using both Monte Carlo sampling (MCS) and Latin hypercube sampling (LHS) to simulate power system faults caused by fluctuations in the natural gas supply, the reliability-assessment results of the power system are similar. The \(EDNS\) obtained with the two methods is 57.19 MW and 57.48 MW, respectively, with only a slight difference of 0.52%. For the results of Average Energy Not Supplied (\(AENS\)), there is only a negligible difference of 0.47% between the results obtained with the two sampling methods. The results of the Severity Index (\(SI\)) and Average Service Availability Index (\(ASAI\)) are consistent, indicating that the model proposed in this paper has a certain degree of universality under different algorithmic solutions.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis
### Comparison between Different Prediction Models  
To further verify the prediction performance of the hybrid model proposed in this study, a comparative experiment was conducted using the ConvLSTM-DSConvGRU model  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Conv-** & **Conv-** & **DSConv-** & **Number of** & **Runtime** & **RMSE1** & **MAPE1** & **RMSE2** & **MAPE2** \\  & **LSTM** & **GRU** & **GRU** & **Parameters** & **(s)** & **(m/s)** & **(\%)** & **(k)** & **(\%)** \\ \hline
1 & \(\surd\) & \(\times\) & \(\times\) & 5.804 M & 0.968 & 1.509 & 22.1643 & 0.494 & 0.109 \\
2 & \(\times\) & \(\surd\) & \(\times\) & 4.354 M & 0.768 & 1.449 & 20.2898 & 0.471 & 0.105 \\
3 & \(\times\) & \(\times\) & \(\surd\) & 0.494 M & 0.353 & 1.475 & 21.5941 & 0.480 & 0.107 \\
4 & \(\surd\) & \(\surd\) & \(\times\) & 5.243 M & 0.859 & 1.386 & 19.2707 & 0.470 & 0.104 \\
5 & \(\surd\) & \(\times\) & \(\surd\) & 3.767 M & 0.735 & 1.384 & 18.3817 & 0.465 & 0.102 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the ablation experiment test set.  
Figure 11: RMSE as the results of the ablation experiment. (**a**) Speed component dataset; (**b**) temperature variable dataset.  
and commonly used spatiotemporal prediction models such as 2D-CNN, 3D-CNN, 3D-ConvLSTM, and SA-ConvLSTM [34]. In this experiment, the structures and parameters of the 2D-CNN and SA-ConvLSTM models remained consistent with those of the proposed model, while the 3D convolution kernel size of the 3D-CNN and 3D-ConvLSTM models were optimised according to [PERSON] et al. [9], with the remaining parameters being consistent with those of the proposed model. The experimental results are shown in Table 5:  
As shown in the table, among the five spatiotemporal prediction models, 2D-CNN showed the poorest prediction performance using the two datasets. 3D-CNN showed some improvement over 2D-CNN, possibly due to its better ability to capture temporal information compared with two-dimensional convolution [35]. 3D-ConvLSTM outperformed the other three commonly used prediction methods and 2D-ConvLSTM in the ablation experiment, and it also showed a slightly lower RMSE and MAPE with the temperature dataset compared with the proposed algorithm. However, its number of parameters was more than three times that of the proposed algorithm, and the training time was much longer, which indicates that the model has excessive algorithm complexity. The SA-ConvLSTM method, which incorporates a self-attention mechanism memory module, performed worse than the traditional ConvLSTM using the two datasets. Therefore, considering all factors, the proposed algorithm not only ensures superior prediction performance but also maintains a relatively small number of parameters, making it more practical.
## 3 Proposed Scheduler to integrate the MES and AIV
### Scheduler Development
#### 3.3.8 Send Product-Drop Confirmation to MES  
This step involves communication between the scheduler and '_Webservice_D' webservice of MES. Like previous steps, this webservice is also imported in LabVIEW, and its equivalent VI is edited and configured accordingly, as mentioned in Section 3.1. The operation of this step is shown in Figure 17. This webservice is used to send the confirmation signal to the MES about the successful product-drop operation, so that the MES can add the entry of relevant product (which has been dropped) at the drop. The scheduler sends a request to the MES (through '_Webservice_D') based on different conditions, which are checked by Boolean '_AND_' logic, as shown in Figure 17. In fact, there is a total of nine conditions out of which three are mentioned here. Remember that here only one product will be ready at a time for operation and this is the product, which product-pick confirmation (given in Section 3.3.4) has been received by the MES. The response of the MES for each case is also shown in Figure 17. After the  
Figure 14: Check status of drop-off locations.  
successful addition of requested product entry at a drop-off location, the MES will send the response to the scheduler. The scheduler will check the '_resultText_' field of the MES responses. If this field response is equal to '_Successful_', then the scheduler will update the number of products at drop-off locations, and this is the completion of one cycle of the scheduler. Now, the next cycle will again start from step-1 (Section 3.3.1). An optional feature of the timer is also added in this step, in which function is the same as the timer function mentioned in Section 3.3.4.  
Figure 16: Drop product.  
Figure 15: Check status of drop-off locations.  
In case of low battery, AIV sends 'low battery' signal to the scheduler. The scheduler checks battery status of AIV before start of the above-mentioned step-1 of Figure 9, in each cycle of operation. The scheduler also supports data logging of all steps of scheduler operation.  
Although the scheduler development explained here was for a specific industrial scenario, the authors believe that this work provided a strong foundation to develop sustainable, high performing customized solutions to fulfil the needs of an industry that want to integrate an MES and AIVs.  
The next section provides testing details of the scheduler presented here.
# A \(3/2\)-Approximation Algorithm for the Graph Balancing Problem with Two Weights
## 2 Algorithm  
Similar to the algorithm by [PERSON] _et al._[2], our approximation algorithm uses an estimation \(T\) of the maximum load of a vertex in an optimal orientation as a parameter. The algorithm GB2W presented below as Algorithm 1 finds a solution of value at most \(3T/2\) if a solution of value at most \(T\) exists. We combine this algorithm with a binary search procedure to find the smallest value for \(T\) for which an orientation with load at most \(3T/2\) for the graph balancing problem with two weights is found: if for a given \(T\) the algorithm does not find a solution of value at most \(3T/2\) then the value of \(T\) is increased in the binary search; otherwise it is decreased. By the above property of our algorithm this smallest value of \(T\) must be less than or equal to the value of an optimum solution, hence our algorithm has approximation ratio 3/2.  
The remainder of this section gives Lemmas 1-4 which provide the subroutines used by our algorithm, then in Theorem 5 we prove our algorithm is a 3/2-approximation algorithm for GBP2W. First, we present Lemma 1 which covers Step 4 of our algorithm. Note that in Step 1 the algorithm scales the weights so the estimation for the optimum load is \(T=1\). We define a _big edge_\(e\) to be an edge with weight \(p_{e}>1/2\); otherwise, we call an edge _small_. Let the value for an optimum solution for the problem be denoted as \(OPT\). From this point forward, we assume that as edges are oriented in Steps 4-5.3, they are removed from \(G\)```
Input: Multigraph \(G\), value \(T\). Output: An orientation \(\gamma\) for the edges in \(E\) with maximum vertex load at most \(3T/2\) or FAIL. If FAIL is returned there is no orientation for \(E\) with maximum vertex load \(T\).
1. Divide edge weights and vertex dedicated loads by \(T\); set \(T=1\).
2. If\(s>1\) or \(q_{v}>1\) for any \(v\in V\)thenreturn FAIL.
3. If\(r,s\in(0,1/2]\)then apply the algorithm by [PERSON] et al. [2].
4. If\(r,s\in(1/2,1]\)then apply the algorithm given in Lemma 1.
5. If\(r\in(0,1/2]\) and \(s\in(1/2,1]\) we consider three subcases. Let \(k=\lfloor 1/r\rfloor\), so \(1/(k+1)<r\leq 1/k\). 5.1. If\(k/(
# A Hybrid Approach to Literature-Based Discovery: Combining Traditional Methods with LLMs
## 2 Related Work
### Relation Extraction
#### 2.2.1 SemRep and Refinements  
SemRep is a widely used rule-based approach to semantic relation extraction tuned to the biomedical domain. Built upon MetaMap, it incorporates a step for mapping concepts to UMLS CUIs. For example, for the sentence in list 1, SemRep extracts the semantic relations in list 2 (CUIs have been mapped to their term representation).  
1. _[PERSON]'s phenomenon (RP) is commonly observed in fingers and tos of patients with connective tissue diseases (CTDs)._
2. _Connective Tissue Diseases process_of Patients._  
_Toes part_of Patients._  
_Fingers_location_of _Raynaud Phenomenon_.  
_Toes_location_of _Raynaud Phenomenon_.  
The tool extracts approximately 70 different predications, with roughly half being negative (such as neg_treats). A database of SemRep processed abstracts from MEDLINE, SemMedDB [26], is publicly available and widely used for biomedical data analysis, including LBD (e.g., [33] or [42]). However, a recent evaluation of SemRep (version 1.8) on the SemRep test collection [43] revealed limitations in its performance, with 0.55 precision, 0.34 recall, and 0.41 \(F_{1}\)[44]. Even after accounting for test collection and evaluation setup issues, the recall remained low at 0.42 (with an \(F_{1}\) of 0.52), suggesting that the technique requires enhancement for effective data mining.  
To address the recall limitations of SemRep, machine learning approaches, such as classification, have been explored [45]. This involved fine-tuning language models such as PubMedBERT (now BiomedBERT) [46] on entities extracted by SemRep following a pre-training step using contrastive learning. This approach was found to be complementary to SemRep's annotations, with 0.81 recall, 0.62 precision and 0.70 \(F_{1}\).  
Beyond rule-based systems such as Public Knowledge Discovery Engine for Java (PKDE4) [47] and its transformer-based refinement BioPREP [48], other applications of transformer techniques to relation extraction, often focusing on specific subsets of relations, have also emerged (e.g., [49; 50]). However, fine-tuning pre-trained transformer models typically requires a substantial annotated datasets (e.g., [3]), and the resulting relations are not always optimally suited for LBD.  
Recently, there has been a shift towards the use of large language models (LLMs) for relation extraction. Given the enormous quantity of data used to pre-train LLMs, relations can be extracted in a zero-shot setting--with no examples provided in the prompt [51]-- or in a few-shot setting, with a small number of examples included in the prompt [52]. However, the specific focus of each work (e.g., clinical trials only) and varied different evaluation datasets make direct performance comparisons difficult across different LLM-based approaches.