esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 4 Methodology
### Final Result Ranking  
While it is important to classify the articles, for search engine applications, users also need the ability to understand the relative trustworthiness among the article results. Hence, we add an additional layer of trust scoring system to address this gap (see Layer 3 in Figure 1). We first convert our retraction severity scores to a 1-100 scale and divide them into 5 severity levels to create a more detailed ranking framework. Using these scores, we train a LightGBM ranking model with the lambdarank objective to optimize the ranking performance. The features include percentile ranks for impact factors, citation counts, and Altmetric scores, along with metrics showing relationships between these values.  
In our initial experiment results, citation rank was the most important feature (254.78), followed by relative citation impact (227.88) and impact factor rank (186.03). While the model achieved reasonably good NDCG scores (NDCG@5 of 0.8141, NDCG@10 of 0.8490), analysis of sample rankings revealed limitations. When comparing actual versus predicted ranks for 10 random papers, only 1 had a perfect rank match, and 4 were within one rank position of their true standing. The Spearman rank correlation of 0.443 showed only a moderate relationship between predicted and actual trustworthiness. Given these limitations, we decided to use the feature importance insights to create a simpler, more transparent trust score function for articles:  
\[\text{T(a)}=\text{w}_{1}\text{CR(a)}+\text{w}_{2}\text{RCI(a)}+\text{w}_{3} \text{IFR(a)}, \tag{3}\]  
This function (Equation (3)) incorporates the citation rank CR(a), relative citation impact RCI(a), and impact factor rank IFR(a). The weights \(\text{w}_{1}\) (0.25), \(\text{w}_{2}\) (0.23), and \(\text{w}_{3}\) (0.19) reflect the relative importance of each component based on our empirical analysis of feature importance scores from the LightGBM model. To create our final ranking mechanism, we create a score that merges relevance and trustworthiness:  
\[\text{R(a)}=\alpha\times\text{S(a)}+(1-\alpha)\times\text{T(a)}, \tag{4}\]  
In Equation (4), S(a) measures the query-article similarity, while \(\alpha\) balances relevance against trustworthiness. This approach proves particularly valuable for academic searches where both aspects matter significantly. When \(\alpha\) equals 0.5, relevance and trustworthiness contribute equally to the final score. Adjusting \(\alpha\) to be higher prioritizes relevance, while a lower value emphasizes trustworthiness. This flexibility helps prevent situations where highly relevant but questionable articles or trustworthy but less relevant papers dominate the results.
## 3 Results and Discussion
### Datasets  
The model's performance is assessed on two benchmark datasets, AEP and IHEPC [58, 59]. The AEP dataset was recorded in 4.5 months in a residential house in 10 min resolution. This dataset comprises 29 various parameters of weather information (wind speed, humidity, dew point, temperature, and pressure), light, and appliance energy consumption, as presented in Table 1. The data samples were collected from both indoor and outdoor environments through a wireless sensor network. The outdoor data are collected from nearby airport. The building includes 9 indoor and 1 outdoor temperature sensors, 9 humidity sensors in which 7 are integrated with indoor environment and one is in outdoor environment. The outdoor pressure, visibility, temperature, humidity, and dew point are recorded nearby airport region. The IHEPC dataset includes 9 parameters which are; date, time, voltage, global-active-power (GAP), intensity, global-reactive-power (GRP) and three sub-metering as shown in Table 2. The dataset was recorded in a residential house in France during 2006 and 2010 for one-minute resolution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**S.no** & **Data Features** & **Units** \\ \hline
1 & Appliances: total energy consumption by appliances. & Wh \\
2 & Light: total energy consumption by lights. & Wh \\
3 & T1: demonstrate the temperature of kitchen. & C \\
4 & RH1: demonstrate the humidity in kitchen. & \% \\
5 & T2: demonstrate the temperature of living room. & C \\
6 & RH2: demonstrate the humidity of living room. & \% \\
7 & T3: demonstrate the temperature of laundry room. & C \\
8 & RH3: demonstrate the humidity of laundry room. & \% \\
9 & T4: demonstrate the temperature of office room. & C \\
10 & RH4: demonstrate the humidity of office room. & \% \\
11 & T5: demonstrate the temperature of bathroom. & C \\
12 & RH5: demonstrate the humidity of bathroom. & \% \\
13 & RH6: demonstrate the outside temperature of building. & C \\
14 & RH6: demonstrate the outside humidity of building. & \% \\
15 & T7: demonstrate the temperature of ironing room. & C \\
16 & RH7: demonstrate the humidity in ironing room. & \% \\
17 & TS: demonstrate the temperature of teenager room. & C \\
18 & RH8: demonstrate the humidity of teenager room. & \% \\
19 & Tp: demonstrate the temperature of parent room. & C \\
20 & RH9: demonstrate the humidity of parent room. & \% \\
21 & To: demonstrate the outside temperature which are collected from Chievres Weather & C \\ Station (CWS). & & Mm Hg \\
22 & Pressure: outside pressure which are collected from CWS. & \% \\
23 & Rho: demonstrate the outside humidity from CWS. & \% \\
24 & Wind speed: outside wind speed which are collected from CWS. & m/s \\
25 & Visibility: outside visibility from CWS. & Km \\
26 & Tdewpoint: outside Tdewpoint from CWS. & C \\ \hline \hline \end{tabular}
\end{table}
Table 1: AEP dataset variables, a short description and its units.
## 2 Materials and Methods
**Physiological and Cardiometabolic Measurements**: Oxygen uptake (VO2) was continuously monitored during laboratory-based incremental exercise testing using the Cortex MetaMax 3B (Leipzig, Germany) mobile ergospirometer, a reliable device for breath-by-breath gas exchange analysis. Heart rate (HR) was recorded with the Polar RS400 (Kempele, Finland) heart rate monitor, which provided beat-by-beat data throughout all laboratory and field tests. The anaerobic threshold (ANT) was determined through fingertip blood lactate analysis after each exercise load, with lactate samples collected using standard capillary procedures and interpreted using the method described by [PERSON] et al. (2022) [9]. The progressive treadmill protocol began at 8 km/h and increased by 2 km/h every 3 min until volitional exhaustion, following a fixed incremental load design [11]. **BDNF and Blood Biochemical Analysis**: Venous blood samples were collected to assess brain-derived neurotrophic factor (BDNF) concentrations, both before and after the treadmill tests, and additionally after the 14 km/h load. BDNF analysis was performed using standard ELISA protocols. Additional venous blood draws captured the complete blood count and platelet levels, while fingertip samples tracked dynamic lactate fluctuations across each stage. Post-test blood sampling occurred 10 min after the final load to assess recovery-phase BDNF response [18,19]. **Cognitive and Psychomotor Testing**: Cognitive and neuromotor fatigue were quantified through choice reaction time (CRT) testing using the APR Reaction Meter (UNI-PAR, Poland). CRT tasks were administered during the final minute of each 3 min treadmill stage and involved responding to 25 audiovisual stimuli, thereby allowing precise identification of the psychomotor fatigue threshold (PFT) [14,15]. A familiarization phase ensured procedural consistency and minimized learning effects. **Field-Based Endurance Testing**: The on-field performance capacity was assessed via a Critical Power Test (CPT) conducted on a 200 m oval track. Athletes completed two-time trials (2600 m and 1000 m), performed in sequence on the same day with 30 min of passive recovery in between. The tests were preceded by a standardized 20 min warm-up. Running times were measured using electronic timing, and the critical speed (CS) and D\({}^{\prime}\) were calculated using the linear distance-time model described by [PERSON] et al. (2011) [20]. Figure 1: A diagram of the exercise protocol used by the HIIT group lasting a total of 30 min.
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 2 SCL Decoding and Its Operational Complexity  
This section introduces the basics of polar coding and the conventional SCL decoding [12; 13], and also defines the operational complexity of the SCL decoding with regard to hardware implementation.
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
## 2 Materials and Methods  
### Principle of Measurement and Algorithm  
The schematic diagram of the proposed large-sized-pipe-diameter-parameter-measurement system is shown in Figure 1, which includes a digital image acquisition unit composed of three sets of binocular cameras, a marker projection unit composed of multiple lasers, a computer responsible for data processing, and a bracket for equipment installation.  
The bracket spans the pipe under test and is kept as perpendicular to the central axis of the pipe as possible. Multiple lasers fixed on the bracket project a circle of laser markers on the surface of the pipe from different angles as the feature points of the cross section. At the same time, multiple sets of binocular cameras capture images of the pipe from different positions and communicate with the computer in real time through USB cables. After the feature points are extracted, a specific matching algorithm is used to achieve feature point matching and 3D reconstruction, and multiple sets of point clouds are obtained. Next, based on the implicit relationship between each group of point clouds, the corresponding matching points are matched and the rigid-body transformation is calculated to realize point-cloud registration. Finally, the spatial-circle-fitting algorithm is used for the registered point clouds to estimate the diameter of the measured pipe cross section.  
The measurement algorithm flow is shown in Figure 2. Raw images captured by multiple sets of binocular cameras are used as input. After image preprocessing, 3D reconstruction, point-cloud registration, and parameter estimation, the pipe diameter is finally obtained as output.  
Image preprocessing includes steps such as camera calibration, image denoising and stereo correction. Its main function is to convert the raw images into clear images aligned horizontally with affine lines, which is the basis of the entire measurement algorithm.  
In our study, feature points are extracted by identifying the center of the blob feature in the images with DoG (difference of Gaussian), and the corresponding algorithm flow is not further explained here. On the basis of image preprocessing, parameters, such as Gaussian standard deviation range, elliptical interval, and judgment threshold, can be set according to a priori knowledge so as to accurately and efficiently extract laser marks as feature points.
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 5 Results and Discussions
### Effects of the Depth of the GNN and Window Sizes  
We explored the relationship between model performance and the depth of ERMC-MHGNN. According to Figure 3, the best values of \(\{L^{SHG}\), \(L^{NSHG}\}\) were \(\{1,1\}\) and \(\{4,6\}\) on the MELD and EmoryNLP datasets, and a \(66.4\%\) weighted-average F1 and \(40.1\%\) weighted-average F1, respectively, were obtained. Note that the convolution on the EmoryNLP dataset required more NSHG layers. This may have been related to the number of labels in the conversation and the length of the conversation. The proportion of each label in the EmoryNLP dataset was more balanced than that in the MELD dataset; the proportion of emotional shift was relatively larger, and the conversation length was also larger. Therefore, the EmoryNLP dataset needed more NSHG layers for convolution to deal with interdependency. The proportion of neutral labels in the MELD dataset was relatively large, and the conversation length was relatively small. Therefore, the MELD dataset did not require too many convolution layers. In general, the use of two types of hypergraphs was beneficial for understanding contextual cues and speaker dependencies and for enhancing the recognition ability of the model.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **MELD** & **EmoryNLP** \\ \hline Full model & 66.4 & 40.1 \\ \hline w/o \(\alpha^{pos}\) & 65.61 (\(\downarrow\)0.79) & 39.15 (\(\downarrow\)0.95) \\ w/o \(\alpha^{att}\) & 65.64 (\(\downarrow\)0.76) & 39.05 (\(\downarrow\)1.05) \\ \hline w/o \(SSHG\) & 65.3 (\(\downarrow\)1.1) & 38.93 (\(\downarrow\)1.17) \\ w/o \(NSHG\) & 65.19 (\(\downarrow\)1.21) & 38.9 (\(\downarrow\)1.2) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the ablation study. ’\(\downarrow\)’ represents the reduced performance compared with the ’Full model’.  
We also experimented with both datasets by increasing the window size of the past and future. The experimental results are shown in Figure 4. In the figure, we can see that the window size of the context had a relatively small effect on the two datasets, but the context window sizes for obtaining relatively good results for the different datasets were not the same. In the MELD dataset, there was a relatively certain number of conversations with less than three utterances, while in the EmoryNLP dataset, the length of the conversations was generally greater than five utterances. Therefore, the MELD dataset had better results when the window size of the past and future was 1, while the EmoryNLP dataset required a relatively large context window.
# On the Benefits of Listen before Talk Scheme for NB-Fi Networks
###### Abstract  
NB-Fi (Narrow Band Fidelity) is a promising protocol for low-power wide-area networks. NB-Fi networks use license-exempt Industrial, Scientific, and Medical (ISM) bands and, thus, NB-Fi devices can work in two modes: with and without Listen Before Talk (LBT). This paper compares these modes with different implementations of LBT in terms of packet loss rate (PLR), delay, energy consumption, and throughput. Interestingly, in some scenarios, the results contradict expectations from the classic papers on channel access because of the peculiarities of the NB-Fi technology. These contradictions are explained in the paper. The results show that LBT can significantly improve all the considered performance indicators when the network load exceeds 40 packets per second. With extensive simulation, we show that in a small NB-Fi network, the optimal PLR, delay, and energy consumption are obtained with the implementation of LBT that corresponds to non-persistent CSMA. In a large NB-Fi network, where some devices can be hidden from others, the best strategy to improve PLR, delay, throughput, and energy consumption is to use the implementation of LBT that corresponds to p-persistent CSMA.  
Keywords:NB-Fi; ultra-narrow band; Carrier Sense Multiple Access; Listen Before Talk; performance evaluation; energy consumption +
Footnote †: journal:
# Integration of BIM Tools for the Facility Management of Railway Bridges
## 5 Case Study
### Tool's Implementation Process  
To address the lack of modernization in traditional inspection methods, which result in information loss and potential economic, structural, and social losses, the integration of automation between a parameterized BIM model and an automated data spreadsheet is proposed, aiming towards a digital twin of the railway bridge. This integration seeks to manage its inspection more effectively by consolidating information from these activities, preventing data loss and dispersion, and achieving preventive monitoring of the asset. This BIM/data automation is based on a parameterized BIM model of the bridge and an automated spreadsheet containing information about the elements to be inspected. This will facilitate the visualization of the components and their structural condition during inspections and subsequent maintenance tasks. Both the BIM model and the spreadsheet categorize each part of the bridge according to the types of elements, allowing comments to be added for each element to record repair activities, additional anomalies, or any relevant notes.  
Various tools can be employed to develop the proposed workflow and achieve the set objectives. For bridge modeling, different software options such as Autodesk Revit, OpenBridge and MicroStation from Bentley, and TEKLA are available. Autodesk Revit is widely recognized for its extensive use in the industry, offering a large user base and an active community that facilitates the exchange of knowledge and experiences. Its ease of connection with Excel through Dynamo allows for smooth, bidirectional, and efficient data integration, which is crucial for managing large volumes of information. On the  
Figure 3: Tabolango bridge.  
other hand, OpenBridge and MicroStation from Bentley are specialized tools for transport infrastructure, standing out for their advanced capabilities in the design and analysis of bridges. TEKLA, known for its precision in structural modeling, primarily used for steel structures, offers robust tools for detailed construction, although its focus is more on the execution and manufacturing phase than on the integrated management of facilities. Therefore, considering these factors, Autodesk Revit was selected for this study due to its versatility, widespread adoption in the industry, and ease of data integration, making it an optimal choice for the objectives outlined in this paper. However, it is important to note that the proposed workflow can be replicated by users in various software tools, with the corresponding programming and integrations. Figure 4 shows the software used to implement the tool and the main actions developed within them.
## 2 Related Work
### Deep Learning Approaches  
#### 2.2.1 Two-Stage Object Detection Algorithms  
The two-stage detectors first propose regions of interest (ROI), which are then fed to a regressor and a classifier. The most notable algorithms in this category are the ones from the R-CNN family. The first of its kind is R-CNN [2], which uses selective search [38] for ROI proposal given an input image, and a pre-trained CNN that is used to extract the features of each ROI. Finally, an SVM and a regressor are used to output the bounding boxes and the probabilities of them containing an object. Figure 3a shows the flow of this algorithm.  
The main drawback of R-CNN is that it has a very high inference time, as each ROI is fed to the CNN independently. To overcome this aspect, [PERSON] [4] proposed an improved version called Fast R-CNN. Instead of feeding each ROI to the CNN, Fast R-CNN extracts the feature maps of the entire input image at once. The ROI proposal method remains the same, but a new ROI pooling layer is introduced, which divides the ROI into a grid of fixed size (the number of cells in the grid is independent of the size of the ROI) and then applies max pooling for each cell. The output of this layer is then sent to Fully Connected (FC) layers to predict the bounding box and the class of the object. Figure 3b shows the flow of this algorithm.  
Faster R-CNN [5] further improved the performance of this method by introducing the Region Proposal Network (RPN), which uses anchor boxes (see Appendix A.1) and a sliding window to predict ROIs. The advantage of this method is that it can be trained to  
Figure 3: Network flows schematics (**a**) R-CNN, (**b**) Fast R-CNN, (**c**) Faster R-CNN.  
predict more accurate bounding boxes, thus resulting in fewer low-quality predictions and a lower inference time per image. Figure 3c shows the flow of this algorithm.
## 4 Prediction of Radiated Sound Power based on the DRSMs
### Other Examples  
As we know, the inherent modes of the same model with different boundary conditions are different. For the cylindrical shell model described above, the fixed boundary at both ends was changed to a free boundary. The analysis of the DRSMs was performed in the same way, and the prediction result of the free model was examined. First, the sound radiation efficiency of each mode was calculated, as shown in Figure 10. Unlike the fixed cylindrical shell, the mode with the highest sound radiation efficiency is the mode (1,0), which is a translational mode and the lowest-order mode of the free cylindrical shell. Through precalculation, the contribution of each mode to the total radiated sound power is analyzed, as presented in Figure 11. The DRSMs in the low frequency band were found, including mode (1,0), mode (2,1), and mode (3,1).  
Figure 10: Modal sound radiation efficiency of different order modes.  
Figure 9: Comparison of the superposed curve of the radiated sound power from the DRSMs, the prediction curve of the boundary element method (BEM), and the reference prediction results.  
Then, for the cylindrical shell with free ends, the vibration displacement and radiated sound power of the cylindrical shell were recalculated after the position of the excitation point was changed. On the basis of the vibration displacement data, the prediction result of the radiated sound power from the mode (1,0) uses 220 (\(10\times 22\)) measuring points, the mode (3,1) uses eight measuring points, and the mode (2,1) uses random 20 measuring points. The prediction result is compared with the reference radiated sound power curve of the same model directly calculated by the FEM, as shown in Figure 12. The DRSM method can acquire a good prediction result.  
For a cylindrical shell with mass rings at both ends, DRSM-based sound radiation prediction was also performed. The length of the cylindrical shell model is 1 m, the radius is 0.25 m, and the thickness is 0.005 m. The material of the model is steel, and its structural material parameters are the same as those of the previous cylindrical shell. Mass rings with a thickness of 0.05 m and a width of 0.605 m are located at both ends of this cylindrical shell, both sides of the model are free, and an excited point is added on the surface. The cylindrical shell model with mass rings is shown in Figure 13.  
Figure 11: Radiated sound power of several order modes.  
Figure 12: Comparison between superposed curve of the radiated sound power from the DRSMs and the reference prediction result.  
The DRSM method is used to predict the radiated sound field. The final prediction results are shown in Figure 14. The forecast curve is in good agreement with the actual curve directly calculated by the FEM.
## 4 Discussion
### Surface Roughness vs. Biomaterial Hardness  
It was observed that the roughness due to CD and MK surface were higher than those due to the LU and EN surface, whatever the tool used and the roughness parameter chosen. Although the milling mode and the machining parameters tested lacked a precise description in their article, [PERSON] et al. also found that EN and LU biomaterials each exhibited a lower roughness than the others after milling [33].  
In our study, Figure 7 shows that arithmetic roughness depends on the biomaterial's Vickers hardness (Hv). In the range 107.3 (LU) to 254.9 Hv (EN), arithmetic roughness parameters fluctuate,  
Figure 9: Geometric model of the 2D roughness obtained with the 2 tools tested in end milling mode.  
and no trend can be found. Then, from 254.9 (EN) to 640 Hv (MK) a smooth roughness increase is observed. Finally, up to 737 Hv (CD) a more abrupt increase is observed. In conclusion, the arithmetic roughness is influenced by the biomaterial milled, more specifically its hardness.