esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Shallow Models and Evaluation Metrics  
Among ML algorithms, shallow models such as support vector machines (SVM), random forests (RF), and K-nearest neighbours (KNN) strike a balance between interpretability and performance on the tabular datasets' standard in project management research. One study demonstrated SVM's efficacy in high-dimensional spaces [19], while another reported RF's robustness against overfitting in load-forecasting tasks [20]. Despite its increased memory demands, KNN's utility is where similarity-based reasoning is critical [21]. However, these models depend heavily on training data quality and may struggle with non-linear or sparse patterns [22].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application Area** & **ML Role/Benefit** \\ \hline Effort/Cost Estimation & More accurate predictions, reducing budget overruns \\ \hline Resource Allocation & Automated, data-driven assignment of tasks \\ \hline Risk Management & Early detection and mitigation of potential failures \\ \hline Decision Support & Insights from complex, multimodal project data \\ \hline Project Monitoring & Real-time tracking and adaptive control under uncertainty \\ \hline \hline \end{tabular}
\end{table}
Table 2: Machine learning’s role in project management.  
Researchers report accuracy, precision, and recall to quantify classifier performance in project contexts [23].  
\[Accuracy\ =\ \frac{TP+TN}{TP+TN+FP+FN}, \tag{1}\]  
\[Precision\ =\ \frac{TP}{TP+FP}, \tag{2}\]  
\[Recall\ =\ \frac{TP}{TP+FN}, \tag{3}\]  
TP, TN, FP, and FN denote true positives, true negatives, false positives, and false negatives, respectively. These metrics offer intuitive measures of a model's correctness, relevance, and completeness--qualities essential for a tool that recommends appropriate research methodologies [24, 25].  
While ML has revolutionised many facets of project execution and several rule-based tools assist in research-design guidance, no prior work has leveraged ML to recommend research methodologies tailored to specific project management scenarios. Our study fills this gap by training SVM, RF, and KNN classifiers on a curated dataset of 156 instances from over 100 peer-reviewed articles, offering the first adaptive, data-driven framework for methodology selection in project management research.
## 3 Results
Sharing this information with the engineering community is also important. From the comparison of the observed intensity maps, ref. [34] found that the most appropriate regressions are based on the maximum velocity vs intensity and peak acceleration vs intensity. This is consistent with the notion that low intensities are sensitive to the acceleration. Moderate-intensity damage usually occurs in rigid structures (masorny walls, chimneys, etc. ), which are also sensitive to high-frequency (acceleration) ground motions. As the intensity increases, the damage also occurs in flexible structures, for which the damage is proportional to the velocity of the ground and not to the acceleration. Relating the soil motions recorded to modified Mercalli intensities with this type of information could reduce the damage in a certain region during the occurrence of an earthquake. With the maximum acceleration estimated in the simulation of an \(M=7.1\) earthquake off the coast between Colima and Michoacan, intensities were estimated for the five stations used during the simulation. Table 3 shows the estimated intensities and their relations as proposed by [35]. Taking into account this table and considering the maximum values of Table 2, the model \((6,6)\) has the maximum acceleration at the station closest to the source (FMIR), it has an intensity MMI=V (\(A_{\mathrm{max}}=7.5\%\) g). Apatzingan station (APTZ) shows its maximum acceleration in the model \((10,1)\), with an intensity MMI = II-III (\(A_{\mathrm{max}}=1.2\%\) g). The maximum intensity for the station located in Patzcuaro (PATZ) was MMI = II-III (0.2% g) with the model \((1,6)\). The simulation of the intensity for the station located in the main city of Michoacan, Morelia (station MOIG) registered a maximum intensity of MMI = I for the model \((10,10)\). The model \((6,6)\) yielded a maximum intensity of MMI = I for the farthest station (MARM). With these results, it is observed that the maximum accelerations were obtained in the station closest to the source around 60 km, and the lowest were after 345 km from the source.
## 3 Results
### Imaging Performance with Limited Data
#### 3.2.2 Reconstructions of Small Target at an Offset Height Using a Headband
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case No.** & \(RMSE\) & \(RMSE\)-\(T\) & \(e_{max}\) & \(RE\) \\ \hline III.1, 3-D & 3.05 & 20.14 & 22.79 & 0.57 \\ III.1, 2-D bot & 2.44 & 21.35 & 21.49 & 0.71 \\ III.1, 2-D top & 2.43 & 21.27 & 21.40 & 0.69 \\ III.1, 2-D both & 2.59 & 21.40 & 21.48 & 0.75 \\ III.2, 3-D & 2.30 & 20.02 & 20.70 & 0.70 \\ III.2, 2-D bot & 2.44 & 21.49 & 21.44 & 0.70 \\ III.2, 2-D top & 2.41 & 21.23 & 21.15 & 0.70 \\ III.2, 2-D both & 2.40 & 21.25 & 20.92 & 0.72 \\ III.3, 3-D & 2.57 & 20.08 & 21.14 & 0.47 \\ III.3, 2-D bot & 2.43 & 21.17 & 21.59 & 0.70 \\ III.3, 2-D top & 2.43 & 21.36 & 21.40 & 0.70 \\ III.3, 2-D both & 2.59 & 21.40 & 21.48 & 1.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Reconstruction Errors for the cases considered in Section 3.2.2. Figure 20: 2-D reconstructions of \(\Re(\epsilon_{r})\) of Case III.3 for the x-y slice at 1.0 GHz using data from the: (a) bottom ring, (b) top ring, and (c) both rings. The headband of Figure 14 (with an increased distance between the array and the interface with air) is used to obtain the data.
###### Abstract  
This paper outlines a method based on the theory of artificial potential fields combined with sliding mode techniques for spacecraft maneuvers in the presence of obstacles. Guidance and control algorithms are validated with a six degree-of-freedom (dof) comorbid simulator. The idea of this paper is to provide computationally efficient algorithms for real time applications, in which the combination of Artificial potential field (APF) and sliding mode control shows the ability of plan trajectories, even in the presence of external disturbances and model uncertainties. A reduced frequency of the proposed controllers and a pulse width modulation (PWM) of the thrusters are considered to verify the performance of the system. The computational performance of APF as a guidance algorithm is discussed and the algorithms are verified by simulations of a complete rendezvous maneuver. The proposed algorithm appears suitable for the autonomous, real-time control of complex maneuvers with a minimum on-board computational effort.  
spacecraft rendezvous; artificial potential fields; GNC algorithms Article
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 4 Implications of Findings
### Framework Conceptualisation  
The findings from Section 4 aided in conceptualising a framework that highlights the infrastructure elements for smart campus development. The 15 cluster themes were categorised into four (4) main divisions. The four divisions are smart building construction or repurposing, technology, and IT network; continuous improvement; and smart learning and teaching systems. Smart building construction or repurposing encapsulates smart city clusters, IoTs and smart buildings, and energy management systems. Technology and IT network division comprise of smart campus network grid; cloud computing; inclusive smart technology; and smart campus applications.  
Continuous improvement has a similar IT infrastructure for evaluating the performance of smart campus features. The continuous improvement consists of deep learning architecture, data mining, auto-analysers, and performance measurement and forecasting. The final division in the framework contains a campus information portal, a learning management system; a campus equipment management service (CEMS); and an education management system to produce the smart learning and teaching systems infrastructure. Each of the four divisions will be explained in Section 4.2.
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 3 Materials and Methods
### Intersection Detection-Based Spatial Relationship Judgment between Arm and Product
#### 3.3.3 Intersection Detection Efficiency Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Objects to Be**} & \multicolumn{3}{c}{**Necessary Geometric Characteristic Data (mm)**} & **Hand** \\ \cline{2-4}  & **Position Class** & **Range Class** & **Direction Class** & **Coordinate (mm)** \\ \hline Object 1 & \multirow{2}{*}{(\(-\)304.78, 58.23, 2.84)} & \multirow{2}{*}{135.48} & \multirow{2}{*}{—} & \\ (Sphere) & & & & \\ (Object 2 & \multirow{2}{*}{(174.60, \(-\)5.64, 37.27)} & \multirow{2}{*}{148.65} & \multirow{2}{*}{—} & \\ (Sphere) & & & & \\ \hline Object 3 & \multirow{2}{*}{(\(-\)215.70, 383.47, 208.77)} & \multirow{2}{*}{102.64} & \multirow{2}{*}{—} & \\ (Cylinder) & & & & \\ (\(-\)198.60, 511.29, 55.55) & & & & \\ \hline Object 3 & \multirow{2}{*}{(85.13, 312.14, 361.81)} & \multirow{2}{*}{95.76} & \multirow{2}{*}{—} & \\ (Capsule) & & & & \\ (\(\-\)198.60, 511.29, 55.55) & & & & \\ \hline Object 3 & \multirow{2}{*}{(85.13, 312.14, 361.81)} & \multirow{2}{*}{95.76} & \multirow{2}{*}{—} & \\ (Capsule) & & & & \\ (\(\-\)198.60, 511.29, 55.55) & & & & \\ \hline Object 3 & \multirow{2}{*}{(85.13, 312.14, 361.81)} & \multirow{2}{*}{95.76} & \multirow{2}{*}{—} & \\ (Capsule) & & & & \\ (\(\-\)343.23, 258.90, 361.81) & & & & \\ \hline Object 5 & \multirow{2}{*}{(248.80, 457.52, 107.06)} & \multirow{2}{*}{[124.50, 82.90, 73.95]} & \multirow{2}{*}{v [\(-\)0.0847, \(-\)0.8964, \(-\)0.4352]} & \multirow{2}{*}{z = 60.80} \\ (OBB) & & & & \\ \hline Object 6 & \multirow{2}{*}{(\(-\)103.40, 263.99, \(-\)242.31)} & \multirow{2}{*}{[142.10, 170.63, 77.80]} & \multirow{2}{*}{v [\(-\)0.7518, \(-\)0.5029, \(-\)0.4266]} & \multirow{2}{*}{x = \(-\)14.76} \\ (OBB) & & & & \\ \hline Object 7 & \multirow{2}{*}{(490.63, 103.71, 30.84)} & \multirow{2}{*}{[87.15, 66.75, 90.91]} & \multirow{2}{*}{v [\(-\)0.7581, \(-\)0.5196, \(-\)0.3939]} & \multirow{2}{*}{v [\(-\)0.1930, 0.3982, \(-\)0.8968]} \\ (OBB) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Necessary Geometric Characteristic Data of the Object in Scene 3.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 2 Numerical Modeling and Parameter Selection
### Numerical Calculation and Analysis  
The primary focus of this paper is to study the dynamic response of three structural elements in the tunnel: the central pillar, the diaphragm wall, and the roof. To achieve this, vibration data were collected during blasting. For comparative analysis of tunnel blast vibrations, historical monitoring points were set on the central pillar and diaphragm wall in the model to record peak vibration velocity (PPV). Model points 1 to 5 are positioned on the diaphragm wall, while points 6 to 10 are adjacent to the central pillar, each spaced 10 m apart. The placement of these monitoring points in the numerical model is shown in Figure 6.  
In vibration monitoring, vertical displacement velocity is the easiest to measure, and PPV is used here to ensure consistency across directions. FLAC3D software simulated and recorded PPV values for the vibration measurement points, with the distribution on the diaphragm wall shown in Figure 7. At a distance of 30 m from the blast source, the highest recorded PPV on the diaphragm wall is 1.656 cm/s at measuring point 1.  
Figure 5: The diagram of a triangular shock wave load curve.  
As the distance from the blast source to the diaphragm wall increases, the PPV gradually decreases, reaching a minimum of 1.160 cm/s at point 5. Figure 8 illustrates the simulated peak vibration velocities at the base of the central pillar, where point 6 recorded a PPV of 1.763 cm/s, and point 10, 1.181 cm/s. The vibration velocity on the central pillar is generally higher than on the diaphragm wall, indicating a stronger impact on the pillar compared to the wall.  
Figure 6: Layout of the monitoring points.  
Figure 7: Simulation of the vibration speed of the diaphragm wall measurement point.
# The Influence of Filtration on the Results of Measurements Made with Optical Coordinate Systems
## 3 Results  
For each filter, 40 measurements of the hinge thickness were taken. For each filter used, a statistical analysis was performed in the Minitab 22.1 (64-bit) environment. The initial statistical analysis of the data included determining the basic statistical parameters presented in Table 1, as well as an analysis of variance planned later to verify the hypothesis of equality of variances for each of the analyzed factors, which were the filters used. A graph was developed from the data, presenting the results for all four analyzed filters.  
For each filter, a preliminary statistical analysis of the data was performed. Basic statistical parameters were determined. The results are presented in Table 1, and the graphical interpretation is shown in Figures 6 and 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Filter “0”** & **Filter “5”** & **Filter “10”** & **Filter “20”** \\ \hline Average & 0.1260 mm & 0.1246 mm & 0.1234 mm & 0.1231 mm \\ \hline Min Value & 0.1257 mm & 0.1243 mm & 0.1231 mm & 0.1228 mm \\ \hline Max Value & 0.1262 mm & 0.1249 mm & 0.1239 mm & 0.1234 mm \\ \hline Std Dev & 0.00011 mm & 0.00014 mm & 0.00020 mm & 0.00015 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preliminary statistical analysis of data. Results are in mm.  
Figure 5: Edge correction view with applied filters.  
The graph in Figure 6 shows the differences between the individual filters, which confirms that filtration affects the measurement results. By increasing the filter value, we obtain lower values from the measurements. Figure 7 leads to the same conclusion. This graph is a commonly known graph called a box with whiskers. It presents a comparison of data obtained from measurements for the filters used. From this graph, we can obtain information about the location and dispersion of the data. For the first box, i.e., for the zero filter, we observe values that are outliers from the rest, which are not visible for the remaining boxes corresponding to filters 10, 20, and 5. In addition, we can see that the  
Figure 6: Comparison of results for four filters used during measurements.  
Figure 7: Boxplot for applied filters. Stars indicate outliers.  
median for filters 0 and 5 is in the middle position, while for filters 10 and 20, the median is shifted towards the first quartile.
## 1 _Article_
### Efficiency of Flange-Bonded CFRP Sheets in Relocation of Plastic Hinge in RC Beam-Column Joints
###### Abstract  
Beam-column connection zones are high regions of interest in reinforced concrete (RC) structures, which are expected to respond elastically to seismic loads. Using carbon fiber-reinforced polymers (CFRP) to improve these connections, performance is critical in retrofitting deficient RC frames because existing slabs may pose numerous limitations in the design and wrapping of CFRP sheets in joints. The main aim of this research is to develop a new design for flange-bonded CFRP retrofit of frames, including slabs, for the relocation of plastic hinges of the connection area toward the beam and to develop beam-column joint capacity and building stability in cases of subjection to dynamic loads. The performance of these proposed retrofittings was explored both experimentally and numerically. Two full-scale fabricated interior RC joints of a real moment-resisting frame with moderate ductility were subjected to monotonic loads before and after retrofitting, and the results were used to detail the numerical progress and verify of the beam-column connection. Moreover, a parametric study was conducted on CFRP sheets' optimal thickness to examine its influence on plastic hinge relocation in the connection region. Results show that the retrofitting method can efficiently relocate the plastic hinge to the mid-span of the beam, which, in turn, leads to improved capacity and achievement of the RC frame and guarantees better structural safety a lower cost.  
seismic retrofitting; RC joints; CFRP sheets; non-linear static analysis; flange-bonded plastic hinge; safety Article
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
###### Abstract  
In wireless sensor networks (WSNs), communication links are typically error-prone and unreliable, so providing reliable and timely data routing for loss- and delay-sensitive applications in WSNs it is a challenge issue. Additionally, with specific thresholds in practical applications, the loss and delay sensitivity implies requirements for high reliability and low delay. Opportunistic Routing (OR) has been well studied in WSNs to improve reliability for error-prone and unreliable wireless communication links where the transmission power is assumed to be identical in the whole network. In this paper, a Cross-layer Optimized Opportunistic Routing (COOR) scheme is proposed to improve the communication link reliability and reduce delay for loss-and-delay sensitive WSNs. The main contribution of the COOR scheme is making full use of the remaining energy in networks to increase the transmission power of most nodes, which will provide a higher communication reliability or further transmission distance. Two optimization strategies referred to as COOR(R) and COOR(P) of the COOR scheme are proposed to improve network performance. In the case of increasing the transmission power, the COOR(R) strategy chooses a node that has a higher communication reliability with same distance in comparison to the traditional opportunistic routing when selecting the next hop candidate node. Since the reliability of data transmission is improved, the delay of the data reaching the sink is reduced by shortening the time of communication between candidate nodes. On the other hand, the COOR(P) strategy prefers a node that has the same communication reliability with longer distance. As a result, network performance can be improved for the following reasons: (a) the delay is reduced as fewer hops are needed while the packet reaches the sink in longer transmission distance circumstances; (b) the reliability can be improved since it is the product of the reliability of every hop of the routing path, and the count is reduced while the reliability of each hop is the same as the traditional method. After analyzing the energy consumption of the network in detail, the value of optimized transmission power in different areas is given. On the basis of a large number of experimental and theoretical analyses, the results show that the COOR scheme will increase communication reliability by 36.62-87.77%, decrease delay by 21.09-52.48%, and balance the energy consumption of 86.97% of the nodes in the WSNs.  
wireless sensor networks; opportunistic routing; transmission power; delay; reliability +
Footnote †: journal:  
_Article_  
A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
# Efficacy of High-Dose Polyclonal Intravenous Immunoglobulin in COVID-19: A Systematic Review
###### Abstract  
Background: Although several therapeutic strategies have been investigated, the optimal treatment approach for patients with coronavirus disease (COVID-19) remains to be elucidated. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of polyclonal intravenous immunoglobulin (IVIG) therapy in COVID-19. Methods: A systematic literature search using appropriate medical subject heading (MeSH) terms was performed through Medline (PubMed), EMBASE, SCOPUS, OVID and Cochrane Library electronic databases. The main outcomes considered were mortality and safety of IVIG versus placebo/standard of care. This review was carried out in accordance with Cochrane methodology including the risk bias assessment and grading of the quality of evidence. Measures of treatment effect were mean differences (MD) together with 95% confidence intervals (CIs) for continuous outcome measures and risk ratio (RR) or MD for binary outcomes. Two reviewers independently extracted data from individual studies, and disagreements were resolved by a third reviewer. Results: A total of 2401 COVID-19 patients from 10 studies (four randomized controlled trials (RCT) and six non-randomized controlled trials (non-RCTs)) were included in the analysis. Participants received IVIG or placebo/standard of care. The use of IVIG was not associated with a significantly reduced risk of death (RR 0.50, 95% CIs 0.18-1.36, \(p=0.17\) for RCTs; RR 0.95, 95% CIs 0.61-1.58, \(p=0.94\) for non-RCTs; low certainty of evidence). IVIG significantly reduced the length of hospital stay (MD \(-2.24\), 95% CIs \(-3.20/-1.27\); \(p=0.00001\); low certainty of evidence), although this difference was significant only for studies evaluating moderate COVID-19 patients. No significant difference was observed in the incidence of overall and serious adverse events between IVIG recipients and controls (very low certainty of evidence). Conclusions: The current evidence from the literature does not support the use of IVIG in COVID-19 patients.  
Keywords:intravenous immunoglobulins; polyclonal antibodies; immunosuppressants; COVID-19; SARS-CoV-2 +
Footnote †: journal: _vaccines_
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 2 Materials and Methods
### Data  
#### 2.2.1 Landsat Satellite Imagery  
This study utilizes Landsat 5/7/8 satellite imagery as the primary data source, spanning the period from 2003 to 2023. The imagery possesses a temporal resolution of 16 days and a spatial resolution of 30 m. Surface reflectance products for Landsat 5 and Landsat 7 data were generated by the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS) using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm. Landsat 8 surface reflectance products were generated using the Landsat Surface Reflectance Code (LASRC) algorithm [24]. All data have undergone radiometric correction and FLAASH atmospheric correction using USGS algorithms and are publicly accessible through the Google Earth Engine (GEE) platform.