esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### IOTA-Based Trust
#### 4.4.1 Transaction Confirmation Rate  
Transaction Confirmation Rate (TCR) measures the proportion of a node's transactions that are successfully confirmed in the Tangle. A higher TCR indicates a more reliable node. It is defined as follows:  
\[TCR=\frac{T_{c}}{T_{t}} \tag{9}\]  
where \(T_{c}\) is the number of transactions successfully confirmed, and \(T_{t}\) is the total number of transactions issued by the node.  
The TCR value is normalized between 0 and 1, where a value close to 1 signifies that the node's transactions are reliably confirmed.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Tissue Regeneration  
AM has been established as an effective tool for production of implants that promote bone regeneration in vitro and in vivo [107; 108; 109; 10]. Management of critical-size bone defects is highly challenging, often resulting in significant morbidity [110]. Simply filling a large bone defect with bone graft may not guarantee bone integration due to an absent coherent blood supply to the graft [110; 111]. Current surgical management options include vascularizing a bone graft, which is difficult and highly technical, or performing a Masquelet procedure, which is effective at restoring blood supply, but requires multiple operations and therefore increases morbidity [110]. AM offers potential implant solutions that could promote both vascularization and bone regeneration [109; 112]. Porosity is an important property for promotion of bone ingrowth. 3D printing provides the technical capability to create high-resolution, porous scaffolds from a wide variety of materials including metals, ceramics and biodegradable polymers [113]. Other regenerative capabilities of AM-fabricated scaffolds aside from bone have also been studied. A study by [PERSON] et al. [114] demonstrated the printing and implantation of an acellular hydroxyapatite/PCL scaffold doped with transforming growth factor \(\beta\)3 (TGF\(\beta\)3) into rabbits. It resulted in full regrowth of the articular surface of the proximal humeral joint, and the regenerated cartilage had comparable histological, compressive and shear properties to that of native rabbit articular cartilage [114]. Similar findings with hyaline cartilage were observed by [PERSON] et al. [115], where an FDM-printed PCL trachea, coated with MSCs and fibrin, demonstrated production of neocartilage, as well as integration with native tracheal tissue. Tissue regeneration using FDM-printed scaffolds have also been reported with hydroxyapatite-PCL scaffolds in the periodonotin [116].
# Performance Evaluation of Steel Slag Asphal Mixtures for Sustainable Road Placement Rehabilitation
## 3 Results and Discussion  
### Affinity between Aggregate and Binder  
Figure 4 represents the evolution of the binder coverage of the aggregates during the affinity test. In this case, the limestone aggregate, which typically has a good affinity with bitumen, presented the best results. However, the SSA also exhibited good performance.  
These results were in good agreement with the findings of other authors [45], that found the binder coverage of the aggregate, after 24 h and using the rolling bottle test, to be 75% in limestone aggregate, 40% in greywacke, 30% in basalt and 10% in granite. In a previous work [46], conducted by the same laboratory, the binder coverage of granitic aggregate was 73% and 40%, after 6 h and 24 h of rolling time, respectively. Thus, the SSA had better behavior than granite and worse than limestone. Generally, the affinity between  
Figure 4: Evolution of the binder coverage of the aggregates during rolling time.  
aggregate and binder is driven by their chemical compatibility. Limestone is composed of calcium carbonate and magnesium carbonate, while granite is mainly composed of silica and alumina. Generally, the SSA is mainly composed of calcium oxide, silica, magnesium oxide, and alumina [31; 47]. In the case of SSA, the presence of calcium oxide will induce some alkaline behavior that promotes adhesion with bitumen [48].  
The performance of the aggregate in the affinity test can help to predict future problems for the asphalt pavement in service, e.g., its susceptibility to stripping. Thus, these results indicate no major concerns regarding the use of SSA in asphalt mixtures.
## 4 Results
When considering the order of magnitude of the differences, we have indeed to keep in mind that the TLS mesh can also be affected by measurement errors or interpolation errors due to lower point density during mesh creation. Furthermore, it is interesting to note that the errors we measured during this study are lower than the accuracy expected for RTK-GNSS measurements (around 2 to 4 cm). Paradoxically, when visually comparing both SfM point clouds (Figure 8a,b), we can notice that the rendering of the Smartphone point cloud seems noisier than the rendering of the Reflex camera  
Figure 5: Comparison (in top view) of the cliff face representation by point clouds respectively measured by the Terrestrial Laser Scanner (TLS) (in red) or reconstructed by SfM photogrammetry using the Geotag of the photographs (in light blue). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**TLS Point Cloud Density**} & \multicolumn{2}{c}{**84.6 pts/m\({}^{2}\)**} \\ \cline{2-3}  & **Reflex Camera–Nikon D800** & **Smartphone—Huawei Y5 2016** \\ \hline
**Tiepoint density** & 0.80 pts/m\({}^{2}\) & 1.99 pts/m\({}^{2}\) \\
**Dense point cloud density** & 476.9 pts/m\({}^{2}\) & 388.3 pts/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of point density for TLS and SfM reconstructions. point cloud. Nevertheless, this difference in aspect is, in all likelihood, due to contrast, luminosity, and radiometric differences in the images, independently of the geometric reconstruction. A vertical topographical profile (Figure 8c) confirms that the SfM point clouds are very coherent with each other (with the Nikon camera point cloud slightly ahead of the Smartphone one) and that they can differ from the TLS point cloud over vegetated areas. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison to TLS Mesh** & **Mean Error (m)** & **Standard Deviation (m)** \\ \hline Reflex camera—Nikon D800 & 0.003 & 0.047 \\ Smartphone—Huawei Y5 2016 & 0.002 & 0.038 \\ Comparison Reflex camera vs. Smartphone & 0.005 & 0.061 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cloud-to-mesh distances between the TLS mesh (used as reference) and RTK-GNSS assisted SfM dense point clouds. Figure 6: Comparison of point density for the TLS point cloud (**a**) and RTK-GNSS assisted SfM dense point clouds obtained respectively with Nikon Reflex camera (**b**) and Huawei Y5 Smartphone camera (**c**). The colormap was adjusted in the case of the TLS point cloud for the sake of clarity. Figure 8: Visual assessment of SfM point cloud quality for the photographs collected using the Nikon D800 Reflex camera (**a**) and the Huawei Y5 Smartphone camera (**b**). (**c**) Vertical topographic profile of the TLS point cloud and SfM point clouds. Figure 7: (**a**) Distances between the TLS mesh (used as reference) and the Nikon Reflex camera RTK GNSS-assisted SfM point cloud. (**b**) Distances between the TLS mesh and the Huawei Smartphone camera RTK GNSS-assisted SfM point cloud. (**c**) Inter-comparison of the SfM point clouds produced using the RTK GNSS-assisted terrestrial photogrammetry method.
# Recent Progress and Challenges Regarding Magnetic-Based Nanoparticles for Targeted Drug Delivery
## 3.3. **Magnetite-Based Nanomaterials for Specific Cancer Treatment**
### Treatment of Other Cancers
Again, the targeting ability of FA resulted in a higher uptake of PTX-loaded nanoformulation compared to free PTX and, subsequently, higher anticancer activity at the same dose. Additionally, tumor growth and volume were significantly reduced compared to free PTX. Polic acid was also included in aspartic acid-modified magnetite nanoparticles for DOX loading and potential application against skin cancer cell lines (B16-F1) [95]. The aspartic acid, a highly biocompatible molecule, contains two carboxylic acid groups facilitating DOX and Fe\({}_{3}\)O\({}_{4}\) surface conjugation and an amine group for FA anchoring. Unlike the previously mentioned compounds for binding FA (natural/synthetic polymers), it is a relatively small particle without a negative impact on reducing the magnetization of magnetite nanoparticles. The DOX-loaded nanocarrier demonstrated cytotoxicity against the human skin cancer cell model. However, pure DOX was more toxic in lower concentrations due to the fast entry into the cell matrix. Among other anticancer drug delivery systems, pH-responsive chitosan-coated magnetic nanoparticles for telmisartan (TEL) delivery to prostate cancer cells [96] or silibinin (SIL) encapsulated in poly (lactic-_co_-glycolic) acid with magnetic core [97] for sustained drug delivery in renal cancer treatment can be mentioned. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
###### Abstract  
Ubiquitous connectivity is envisioned through the integration of terrestrial (TNs) and non-terrestrial networks (NTNs). However, NTNs face multiple routing and Quality of Service (QoS) provisioning challenges due to the mobility of network nodes. Distributed Software-Defined Networking (SDN) combined with Multi-Agent Deep Reinforcement Learning (MADRL) is widely used to introduce programmability and intelligent Traffic Engineering (TE) in TNs, yet applying DRL to NTNs is hindered by frequently changing state sizes, model scalability, and coordination issues. This paper introduces 3 DQR, a novel TE framework that combines hierarchical multi-controller SDN, hierarchical MADRL based on Graph Neural Networks (GNNs), and network topology predictions for QoS path provisioning, effective load distribution, and flow rejection minimisation in future 3D networks. To enhance SDN scalability, introduced are metrics and path operations abstractions to facilitate domain agents coordination by the global agent. To the best of the authors' knowledge, 3 DQR is the first routing scheme to integrate MADRL and GNNs for optimising centralised routing and path allocation in SDN-based 3D mobile networks. The evaluations show up to a 14% reduction in flow rejection rate, a 50% improvement in traffic distribution, and effective QoS class prioritisation compared to baseline techniques. 3 DQR also exhibits strong transfer capabilities, giving consistent performance gains in previously unseen environments.  
Keywords:6G; AI; MADRL; SDN; traffic engineering; GNN; user plane; 3D networks +
Footnote †: journal: Electronics
## 5 Case Study: An AHP/TOPSIS-Based Optimal Gas Station Site Selection
### Dataset Analysis and Prediction Criteria
#### 5.1.4. Parking Lot Datasets and the Vehicle-Owner Criterion  
The NYC Open Data [39] also provides New York City parking lot data. A total of 20,715 parking lots in New York City are contained in this dataset along with their geographical location and the size (in terms of area). In Figure 5, polygons are used to show the location and the area of the parking lots. The criterion vehicle owners, i.e., the number of people who own a vehicle in the surroundings of the candidate gas stations are estimated by employing this dataset.  
Figure 3. NYC traffic estimate.  
It is obvious that a gas station is accessed by vehicle owners only. Therefore, this criterion plays a significant role in this research. Although it is challenging to determine the actual number of people who own vehicles in New York City, the NYC Open Data provides parking lots details that can be processed for estimating this factor. An estimated number of parking slots per area can be obtained by dividing the area according to the parking standards [43; 44]. Suppose that \(V\) represents the set of all parking lots; then, the function  
Figure 4: NYC FourSquare check-ins.  
Figure 5: NYC parking lots.  
given in Equation (4) is employed to determine the approximate number of owned vehicles that are in close proximity to a candidate location \(l\in L\) with the help of the approximate number of parking slots that are located within distance \(r_{v}\) of \(l\).  
\[|\{v\in V:dist(v,l)<r_{v}\}| \tag{4}\]
# Microwave Liquid Crystal Technology
## 3 Material Properties and Characterization
Together with a \(\lambda/4\)-wave plate, a beam splitting polarizer and a set of balanced photodiodes, the THz beam's amplitude can be mapped by monitoring the rotation of the probe beam's polarization at a variety of delay times. Therefore, the LC properties can be determined by a differential measurement method, comparable to the resonator-based characterization. Moreover, it is a highly interesting feature of THz TDS, to be able to get full information of the electric field, i.e., amplitude and delay. One of the characterized LCs was the mixture GT3-23001, which shows an almost constant permittivity over a wide frequency range between 200 GHz to 1.5 THz, see Figure 9. The permittivities of \(\varepsilon_{\perp}=2.34\) and \(\varepsilon_{\parallel}=3.19\) fit well (5.1% deviation for the perpendicular state and 2.8 % for the \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**LC Mixture**} & \multicolumn{4}{c}{**19 GHz**} & \multicolumn{4}{c}{**1 kHz**} & \multicolumn{2}{c}{**\(T_{C}\)**} & \multicolumn{1}{c}{\(\gamma_{1}\)**} \\ \cline{2-9}  & \(\epsilon_{r,\parallel}\) & \(\tan\delta_{\parallel}\) & \(\epsilon_{r,\perp}\) & \(\tan\delta_{\perp}\) & \(\tau_{\ell}\) (\%) & \(\epsilon_{r,\parallel}\) & \(\epsilon_{r,\perp}\) & (\({}^{\circ}\)C) & **(mPa s)** \\ \hline E7 & 2.98 & 0.009 & 2.53 & 0.022 & 15 & 19.6 & 5.2 & 58 & 252 \\ BL111 & 3.25 & 0.0084 & 2.64 & 0.0218 & 18.77 & 22.0 & 5.5 & 85 & 283 \\ CLHS-1 & 3.2 & 0.0033 & 2.46 & 0.0131 & 23.1 & & & \\ GT3-23001 & 3.28 & 0.0038 & 2.46 & 0.0143 & 25.2 & 8.0 & 4.0 & 173.5 & 727 \\ GT5-26001 & 3.27 & 0.0022 & 2.39 & 0.007 & 27 & 3.7 & 2.7 & 146 & 1958 \\ GT5-28004 & 3.32 & 0.0014 & 2.4 & 0.0043 & 27.7 & 3.5 & 2.7 & 151 & 5953 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties of different LC mixtures at room temperature. Figure 8: Schematic of a time-domain spectroscopy THz measurement system as used in [10] for LC characterization. By comparing the measurement results of an empty and a LC-filled sample holder, the dielectric properties of LC can be determined. parallel state) with the values measured with the previously mentioned resonator setup at 19 GHz. The dielectric losses are in the expected order of magnitude, but could not be determined precisely, due to the low dynamic range of the TDS system. Further information and a more detailed description can be found in [11]. Therefore, LC has been proven as well suitable material for the realization of continuously tunable components not only in the microwave- but also in the (sub-)millimeter wave and THz range.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 1  
In this case study, the proposed FRA6\(\sigma\) methodology to evaluate transformer stability by comparing the \(X_{latest}\) measurement against the factory-tested baseline fingerprint is applied. Each frequency range is assessed individually, considering its physical significance, fault indicators, and process capability under 6\(\sigma\) control. The methodology ensures a structured and quantitative assessment based on computed statistical control limits, range variability analysis, and process capability indices (\(C_{p}\), \(C_{pk}\)).  
Table 4 of the methodology provides the \(\sigma\) levels for process stability assessment, which will be referred to when interpreting stability thresholds across frequency bands. Additionally, Table 7 serves as the primary reference for linking frequency deviations to known failure mechanisms in transformers. To ensure measurement accuracy, the FRA data were collected using an M5500 Sweep Frequency Response Analyzer, with strict adherence to calibration, temperature control, and electromagnetic shielding to prevent measurement noise. The baseline fingerprint was acquired under factory acceptance conditions, ensuring a controlled reference for comparison. The \(X_{latest}\) measurement captures post-installation effects, which could include mechanical stress, core shifting, insulation aging, or winding displacement. The FRA response in Figure 4 indicates general coherence between the fingerprint and measured response, but deviations emerge at distinct frequency ranges. These require segmented analysis across the identified regions in Table 7 to determine mechanical or electrical abnormalities.
# Enhancing E-Grocery-Delivery-Network Resilience with Autonomous Delivery Robots
## 5 Discussion  
One of the central observations from the study is the sensitivity of route decisions in a stochastic two-echelon delivery network to both the RSAV and DL. This sensitivity is critical to understanding how the delivery network performs under various conditions. The study systematically varied the RSAV and DL to observe their combined impact on the percentage of unmet demand. As expected, the results showed that when deadlines were tight, the deterministic model outperformed the chance-constraint model. This is because the deterministic model prioritizes on-time delivery, resulting in a lower percentage of unmet demand. Conversely, the chance-constraint model, designed to manage uncertainties, occasionally led to a higher percentage of unmet demand, particularly when the values of RSAV were small. This sensitivity analysis highlights the trade-off between risk and reliability in logistics operations. Tighter deadlines improve reliability but can lead to higher costs or unmet demand, especially when faced with slower vehicle speeds.  
This study also explored the impact of confidence levels \(\varepsilon_{\rho}^{cum},\varepsilon_{\tau}^{ADR},and\ \varepsilon_{f}\) on route decisions while keeping the RSAV and DL fixed. The study assumed that ADRs are more  
Figure 4: Percentage of the unmet demand based on different levels of \(\varepsilon_{\rho}^{cum},\varepsilon_{f}\).  
reliable than vans due to factors like the lack of truck drivers and uncertainties associated with van deliveries. Under the chance-constraint model, different values of confidence levels were tested, leading to varying percentages of unmet demand. The results show that balancing risk and reliability is crucial. Overly conservative confidence levels result in low unmet demand but may lead to inefficiencies. Conversely, overly optimistic confidence levels can result in missed deadlines and higher unmet demand.  
These findings have practical implications for enterprise risk management within E-grocery delivery operations. It underscores the importance of understanding how different factors, such as travel speed, deadlines, and confidence levels, interact and influence the reliability and risk profiles of delivery networks. For enterprises, this study suggests the need to strike a balance between risk and reliability when designing delivery routes and operations. Tight deadlines may improve customer satisfaction but increase the risk of unmet demand, especially in scenarios with slower vehicle speeds. Additionally, the choice of confidence levels in chance-constraint models should be carefully considered to optimize both efficiency and risk mitigation.  
In summary, the chance-constraint programming model of 2E-VRP-SSSD, as explored in this study, offers valuable insights into the complexities of logistics and risk management. The findings underscore the importance of adopting a nuanced approach that carefully balances risk, reliability, and resilience to optimize logistics operations in an uncertain and dynamic environment.
# Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management
## 5 Engineering Applications
### Analytical Results and Discussion  
The performance of the proposed classification system was evaluated in terms of accuracy, precision, sensitivity, specificity and AUC. High values of these indices revealed favorable performance and vice versa. However, accuracy is the most commonly used for comparison. Table 7 summarizes the values of the performance metrics in case studies 1-3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Names of Attributes in the Dataset** & **Source of Information of the Segments** \\ \hline BrdLndx: border index & Shape \\ Area: area in m\({}^{2}\) & Size \\ Round: roundness & Shape \\ Bright: brightness & Spectral \\ Compact: compactness & Shape \\ ShpIndx: shape index & Shape \\ Mean\_G: green & Spectral \\ Mean\_R: red & Spectral \\ Mean\_NIR: near Infrared & Spectral \\ SD\_G: standard deviation of green & Texture \\ SD\_R: standard deviation of red & Texture \\ SD\_NIR: standard deviation of near infrared & Texture \\ LW: length/width & Shape \\ GLCM1: gray-level co-occurrence matrix & Texture \\ Rect: rectangularity & Shape \\ GLCM2: another gray-level co-occurrence matrix attribute & Texture \\ Dens: density & Shape \\ Assym: asymmetry & Shape \\ NDVI: normalized difference vegetation index & Spectral \\ BordLength: border length & Shape \\ GLCM3: another gray-level co-occurrence matrix attribute & Texture \\ \hline \hline \end{tabular} Note: These attributes are repeated for each coarse scale (i.e., variable\_20, variable\_40..., variable\_140).  
\end{table}
Table 10: Attribute information in the urban land cover dataset.  
The applicability and efficiency of the proposed system were confirmed by comparing its performance with other single multi-class and previous models.  
Data preprocessing, such as data cleansing and transformation, is essential to improving the results of data analysis [84]. The user can decide whether or not to normalize data to the (0, 1) range. Normalizing a dataset can minimize the effect of scaling. Table 12 presents the results of applying the proposed system in the three case studies with the original data and the data after feature scaling. In Table 12, better predictive accuracies were obtained with the original steel plates fault and land cover datasets (91.085% and 87.274%, respectively), whereas better results were obtained with the reservoir water quality dataset after feature scaling (93.650%).