esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Operational Mechanisms and Traffic Flow Characteristics of Non-Conventional Lanes
### Macroscopic Operational Patterns  
At the macroscopic level, non-conventional lanes primarily influence intersection capacity and delay. [PERSON] and [PERSON] [54] studied the optimal control performance of a single signal-controlled intersection, discussing two existing methods for analyzing this control problem: one combines various approaches to generate all possible control structures, including traffic flow groupings allowed to proceed together, and the sequence for granting right-of-way; the other method optimizes existing control structures through convex programming techniques. Their research revealed incompatibilities between these two methods, indicating they cannot be satisfactorily combined. [PERSON] and [PERSON] [55] proposed a binary mixed-integer linear programming model for intersection control system design. Their research introduced a new method for evaluating signal settings at individual road intersections, allowing all regulatory variables to be incorporated into a binary mixed-integer linear programming model. This universal model eliminates some restrictive assumptions present in other problem formulations based on stage matrices. The model can be solved efficiently, enabling rapid calculation of globally optimal control system designs.  
[PERSON] et al. [56] conducted safety analyses of corridor-level signalized intersections using Bayesian hierarchical models to evaluate intersection safety. The impact of non-conventional lanes on intersection capacity is complex. According to research, contraflow left-turn lanes can increase left-turn movement capacity by 20-30%, though this improvement is influenced by various factors, including opposing traffic flow, pre-signal timing, and driver compliance rates. While outside left-turn lanes may reduce the saturation flow rate of left-turn movements, they can still improve overall intersection capacity by reducing internal conflicts. The capacity improvement effect of variable lanes depends on the rationality and timeliness of lane function switching, with inappropriate switching strategies potentially leading to decreased capacity. Regarding delays, the influence of non-conventional lanes also exhibits diversity. Tandem intersections, though increasing the number of stops at pre-signals, may reduce overall delay through better traffic flow organization. U-turn designs, while increasing travel distance for left-turning vehicles, may reduce total intersection delay by eliminating direct left-turn conflicts.  
Future research should establish a more comprehensive evaluation indicator system and develop corresponding analytical models. Additionally, considering the characteristic differences between various types of non-conventional lanes, it is necessary to establish classified evaluation standards. Furthermore, the correlation mechanisms between macroscopic operational patterns, microscopic driving behaviors, and mesoscopic traffic flow characteristics require further in-depth research to achieve multi-scale integrated analysis.
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
## 5 Perspectives  
The editorial contributions that promoted the use of magnetic nanoparticles doped with zinc and manganese (Mn\({}_{\rm x}\)Zn\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\)) for application in the biomedical field have been mainly published in the last 10 years. This material has several peculiarities, in terms of magnetic characteristics, for application in diagnostics (e.g., by MRI) or even as an actuator agent in magnetic hyperthermia, showing higher SAR levels than the respective iron oxide nanoparticles. The main concern of the use of this material could lie in its possible toxicity, but the studies reported in this review did not report acute events or a cytotoxicity different from that which can usually be observed for iron oxide-based magnetic nanoparticles. It should also be considered that the release of metal ions, due to the normal in vivo degradation of the nanoparticles, is a slow process; therefore, the adverse effects due to the leakage of zinc and manganese are negligible. Certainly, the use of the nanoparticle is subject to a general assessment of the risks due to the administration of a nanomaterial, due to its size and clearance from the organism, which for Mn-Zn ferrite nanoparticles could be considered as comparable to that of iron oxide nanoparticles. The use of ferrites is, however, still at a low Technology Readiness Level (TRL). Thus, to the best of our knowledge, no formulations are still approved for clinical use in humans. Furthermore, the improved magnetic properties could also help to decrease the amount of particles used in the medical application, with fewer side effects due to the introduction of inorganic nanomaterial in a living system. The few studies that reached the stage of therapeutic exploitation on small animals confirmed the high potential of this nanomaterial. Certainly, there are still several aspects to investigate, especially at the level of the control of the elemental composition of the nanoparticle, since different doping percentages produce drastic changes in the magnetic properties of the material. This elemental control is also different according to the selected synthetic method. Therefore, in the design of an experiment involving the use of these nanoparticles, fine control over the final stoichiometry of the material, and over the magnetic properties, becomes fundamental.  
Conceptualization, R.D.C.; writing--original draft preparation, R.D.C. and L.M.S.; writing--review and editing, R.D.C. and R.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by BIO-D project (ARS01_00876) co-financed by European Union--ERDF--ESF--PON Research and Innovation 2014-2020 and by Italian PON \"Dottorati Innovativi a Caratterizzazione Industriale\" (project n\({}^{\circ}\)1 DOT1312457).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Porosity Correction Factor  
When CFD simulations were conducted using the flow resistance obtained from Equation (3) based on the porosity calculated using Equation (2), a significant discrepancy was observed with respect to the experimental results. As shown in Figure 5, the CFD-predicted leakage flow rate was underestimated relative to the experimental data of [PERSON] et al. [25]. Notably, a significant error was observed, with the maximum relative error reaching approximately 50% in the section with a pressure ratio of 2.75. Therefore, we established a formula for a porosity correction factor that could be utilized in CFD calculations for each brush seal geometry. Equation (4) considers variables such as bristle pack thickness [40], bristle diameter [47], and fence height [48], which are key factors affecting the flow in brush seals. This formula is designed under the assumptions that the porosity of the bristle pack decreases as the pressure ratio increases [47], and that if a clearance is present, the blow-down effect reduces the bristle porosity further [49]. The correction factor calculated using Equation (3) is multiplied by the porosity obtained from Equation (2). Equation (3) is applicable within a pressure ratio range of 1.5-6, and it provides predictions with a coefficient of determination (R\({}^{2}\)) of 0.98 or higher.  
\[\alpha=0.4581\cdot PR^{-0.0789}\cdot\left(\frac{H}{d}\right)^{0.8468}\cdot \left(\frac{t}{d}\right)^{-0.6604}-0.2686\cdot\left(\frac{CL}{d}\right)^{0.5297} \tag{4}\]  
Figure 4: Grid-sensitivity test results: (**a**) brush seal without clearance ([PERSON] et al. [25]); (**b**) brush seal with clearance ([PERSON] et al. [27], clearance = 0.27 mm).
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Channel Coding
#### 5.1.1 Cyclic Redundancy Check  
Considering the fact that the DMRS symbols generation based on the initial parameters of the given transmission and one uplink control information (UCI) data packet in the NPUSCH format 2 contains one bit of control data only, the NPUSCH format 1 is crucial  
Figure 1: Proposed structures of the software components and their dependencies.  
in the context of the sensor data acquisition and transmission. The forwarded data is divided and allocated to the transport blocks, for which transport block size (TBS) size is predetermined [38]. The given transport blocks are further conveyed consecutively to the first processing module of the channel coding section, i.e., cyclic redundancy check (CRC), providing error detection at the receiver side.  
The CRC block extends an input vector for the 24 additional parity bits. The CRC polynomial is the following [21]:  
\[\bar{g}_{CRC24A}=[D^{24}+D^{23}+D^{18}+D^{17}+D^{14}+D^{11}+D^{10}+D^{7}+D^{6}+ D^{5}+D^{4}+D^{3}+D+1]. \tag{1}\]  
Figure 2: NB-IoT uplink modular procedures [2; 21].
## 3 Results
### Final Location Error  
This test involved using two types of phantoms: a professionally made radiological one with imitation lesions located at different places, and a deformable one made from cyberskin with embedded aluminium spinal cord and inserted wooden balls to simulate lesions. The phantoms underwent CT scanning to perform superimposition with the obtained imaging data. Subsequently, a biopsy procedure was performed to hit the surface of an imitation lesion using the developed system. The phantoms then underwent another round of CT scanning to measure the distances between the injected needle positions and designated target locations in the data. The results of the measurements are presented in Table 4. As can be observed, while the location error increases when the lesion is located deeper, it remains appropriate. It is important to note that some needle deflection occurred, as it was tailored for softer materials. The results of performed measurement no. 3 are presented in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**Exam No.** & **Puncture Depth [mm]** & **Distance to Lesion [mm]** \\ \hline
1 & 62.39 & 0 \\
2 & 109.15 & 9.74 \\
3 & 165.96 & 8.17 \\ \hline \end{tabular}
\end{table}
Table 4: Results of the final location error test.  
Figure 8: Measurement of puncture depth and distance to imitation lesion for examination no. 3. The target was at a depth of 16 cm and the measurement was validated using CT. The **left-hand** image presents a volumetric rendering of the phantom’s CT scan and the performed measurements, while the **right-hand** image presents three 2D slices in perpendicular planes with marked measurements between the needle tip and the lesion. On the **right-hand** side image, lines indicate the intersection between anatomical planes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Distance from**} & \multicolumn{2}{c}{**Needle 120 mm**} & \multicolumn{2}{c}{**Needle 160 mm**} \\ \cline{2-5}  & **Needle Base** & **Needle Tip** & **Needle Base** & **Needle Tip** \\  & **Error [mm]** & **Error [mm]** & **Error [mm]** & **Error [mm]** \\ \hline
30 & 1.08 \(\pm\) 0.25 & 1.25 \(\pm\) 0.75 & 1.04 \(\pm\) 0.37 & 1.76 \(\pm\) 0.82 \\
40 & 0.88 \(\pm\) 0.23 & 1.77 \(\pm\) 0.64 & 0.87 \(\pm\) 0.24 & 2.56 \(\pm\) 0.96 \\
50 & 1.63 \(\pm\) 0.52 & 3.36 \(\pm\) 1.37 & 1.65 \(\pm\) 0.48 & 3.55 \(\pm\) 1.07 \\
60 & 3.12 \(\pm\) 1.04 & 2.05 \(\pm\) 1.01 & 4.23 \(\pm\) 0.75 & 3.21 \(\pm\) 2.19 \\ \hline \end{tabular}
\end{table}
Table 3: Results of the needle pose estimation test when the tool is rotated 15° around the needle axis. The error is provided by the mean value \(\pm\) standard deviation of ten measurements.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.9. MT3D/MT3 DMS  
MT3D is a modular 3D model for simulating the transport of pollutants in groundwater [675]. MT3D is capable of simulating flow in both unconfined and confined aquifers, including variable layer thicknesses and different hydraulic and chemical boundary conditions. The MT3D code has been used extensively for contaminant transport modelling and remedial assessment studies. Some examples can be found in the articles [676; 677; 678; 679; 680]. The next generation product, MT3 DMS [681], improves several aspects of the transport model. The adaptation of a structure that allows the addition of user-defined reactions could be useful in modelling complex biological and geochemical processes. MT3 DMS, like its predecessor MT3D, has been tested in a number of environmental studies. Recent examples can be found in references [682; 683; 684; 685]. MT3D and MT3 DMS can be downloaded for free, with the latter embedded in other software such as Visual Modflow and GMS. Gao et al. [685] simulated the migration of organic pollutants from a trench filled with a tank of liquid waste with a volume of 600 m\({}^{3}\). The numerical simulation includes fluctuations in the groundwater level, which were crucial for the migration of pollutants, adsorption in the soil, and biodegradation.
## 3 Results and Discussion
### Landslide Susceptibility Mapping and Comparison of Models  
Figure 18 presents the earthquake-induced landslide susceptibility maps generated using LR, RF, and XGBoost models. The continuous probability outputs from each model were classified into five susceptibility zones: Very Low, Low, Medium, High, and Very High, using the [PERSON] natural breaks algorithm to ensure optimal class separation. A comparative spatial analysis reveals that RF and XGB yield highly consistent susceptibility patterns, delineating concentrated zones of High and Very High risk, particularly along steep terrain and fault-aligned valleys. These zones are prominently clustered near the earthquake epicenter and the active faults, underscoring the dominant role of seismic ground motion and structural discontinuities in the occurrence of landslides. In contrast, the LR model produces more generalized and spatially diffused susceptibility patterns, reflecting the limitations of linear modeling in capturing complex terrain interactions. Nevertheless, all three models consistently identify central and southeastern sectors of the study area as prone to landslides.  
Figure 18: Earthquake-induced landslide susceptibility maps developed by (**a**) LR model, (**b**) RF model, and (**c**) XGBoost model.  
3.8.1. Area Distribution of Susceptibility Classes  
Figure 19 compares the spatial distribution of landslide susceptibility zones across the five classification levels for the three models. Each model displays distinct patterns that reflect their internal decision-making processes. The LR model assigned 46.56% of the study area to the Very Low class and only 5.13% to the Very High class, indicating a conservative estimation approach with broader allocation to lower-risk zones. The RF model, while more refined, allocated 65.94% to the Very Low class and 4.24% to the Very High class, reflecting a slightly more balanced but still cautious delineation. In contrast, XGBoost displayed the most aggressive zoning behavior, allocating 75.87% of the area to the Very Low class and 7.69% to the Very High class, with notably narrow assignments to the Low (6.84%) and Medium (4.88%) categories. This pattern indicates heightened model sensitivity to terrain and seismic variability.  
Despite the relatively small proportion of areas falling within High and Very High zones (ranging from 9.37% in RF to 12.4% in XGBoost), these zones spatially coincide with observed landslide clusters, particularly near fault lines and the epicenter. This spatial alignment affirms the credibility of the models in identifying geologically unstable regions. Overall, the comparative area distributions suggest that LR is conservative, RF strikes a balance between caution and specificity, and XGBoost maximizes detection sensitivity by capturing complex geomorphological patterns.
# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 4 Pre-Processing
### Geometric Transformations  
Even if the faces are detected in an input image, it does not mean that they are in proper conditions to be analyzed. Some problems that can arise from these detected ROIs are rotation, scale, and noise [72]. One has to guarantee that the face to be classified is as geometrically similar as possible to the faces used when training the classifier. That way, the classifier is apter to produce more trustworthy results.  
Some reviewed works applied geometric transformations to faces that were not detected in the best conditions. Starting with the rotation, the most popular way to correct it is by using the facial landmarks outputted by the face detector. Typically, the reviewed works considered two facial landmarks that form an angle of zero in the horizontal axis, when a face is aligned. To perform face alignment of a rotated face, a rotation transformation is applied to align those two facial landmarks with the horizontal axis, until the angle formed by them is zero. Figure 8 shows a rotation correction on a face from the CK+ database.  
As for the scale problem, it is because of the different distances in which faces can be detected: closer detected faces have bigger ROIs than farther ones. Since it is crucial to feed the next stages of the FER system with the same size of ROIs, the technique used in the reviewed works is resizing every ROI to a predetermined size (spatial normalization).  
The noise problem is mainly the background in the detected ROIs. It is important to remove the background from the original ROIs since it can decrease the accuracy of the classifier by adding one more variable to the problem: distinguishing between foreground and background. Most works seemed to overlook this kind of noise, but there are a few that tried to crop the ROI even more in order to filter the background. The most popular approach to do this is to use the facial landmarks and the bounding box outputted by the face detector. By calculating the distances between relevant facial landmarks, it is possible to reduce the ROI dimension obtained from the face detector and filter the background noise. Figure 9 shows an approach of background removal on a face from the CK+ database.
## 3 Data Analysis Methodology
### Performance Metrics  
Since the goal of all DNNs discussed in this paper is binary classification, their performance is best represented using a confusion matrix [36] that visualizes the capability of the classifier in accurately predicting a healthy or a cracked signal. In general, the confusion matrix helps in computing three quantifiable metrics, viz., the sensitivity (true positive rate), specificity (true negative rate), and accuracy (average of sensitivity and specificity). As a corollary to the typical positive-negative terminology used in machine learning literature, a positive occurrence in this situation is equivalent to the cracked state, and a negative occurrence corresponds to a healthy state. Accordingly, sensitivity is the percentage of correctly diagnosing the data labeled as cracked, and specificity is the percentage of recognizing healthy data. It is imperative for all DNNs to have high sensitivity in this damage detection problem, particularly in safety-critical environments. This ensures the reliable detection of cracked components. High overall accuracy is indicative of a good all-round performance in identifying both classes of data.
## 5 Results and Analysis
### Statistical Test: Two-Tailed \(t\)-Test
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Lag-Based** & **ELM** & **RBF-ELM** & **PSO-ELM** & **EOELM** & **DNN** & **SVM** & **ANN** & **GBM** \\ \hline Difference in mean abs. & 0.35 & 0.34 & 0.31 & 0.29 & 0.38 & 0.35 & 0.34 & 0.66 \\ error & 0.04 & 0.29 & 0.42 & 1.43 & 1.02 & 2.46 & 0.56 & \(-\)2.13 \\ P(T \(\leq\) t) two-tail & 0.97 & 0.77 & 0.67 & 0.67 & 0.31 & 0.01 & 0.58 & 0.03 \\ t Critical two-tail & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 \\ Ho (accept if, \(\mid\)t Stat \(\mid\) \(<\) t Critical) & Accept & Accept & Accept & Accept & Reject & Accept & Accept \\ \hline Wavelet-based & WELM & WBRF-ELM & WPSO-ELM & WEOELM & WDNN & SVM & ANN & WGBM \\ \hline Difference in mean abs. & 0.72 & 0.30 & 0.53 & 0.29 & 0.30 & 0.55 & 0.40 & 0.78 \\ error & 2.88 & 0.91 & 1.82 & 1.84 & 1.89 & 1.29 & \(-\)0.22 & 1.44 \\ P(T \(\leq\) t) two-tail & 0.00 & 0.36 & 0.07 & 0.92 & 0.14 & 0.20 & 0.82 & 0.15 \\ t Critical two-tail & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 & 1.96 \\ Ho (accept if, \(\mid\)t Stat \(\mid\) \(<\) t Critical) & Reject & Accept & Accept & Accept & Accept & Accept & Accept \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Two-tailed \(t\)-test for comparing models’ performance for Fal at Tragony.
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 4 Results and Analysis
### BDS-3-Based POD  
Using the spaceborne BDS-2/3 measurements and GFZ multi-GNSS rapid products, the Tianping1-B orbit is solved with different strategies, as described in Section 3.2.  
Figure 4 shows the BDS-based POD residuals with different strategies. For BDS-2-based POD, the IF code residual of BDS-2 is about 1.06 m, and the IF phase residual is about 0.88 cm. For BDS-3-based POD, the IF code and phase residuals are 0.64 m and 0.82 cm, respectively. For BDS-2/3-based POD, the IF code and phase residuals of BDS-2 are 2.16 m and 1.1 cm and that of BDS-3 are 1.85 m and 1.0 cm, respectively. Compared with BDS-2-based and BDS-2/3-based POD, the IF code and phase residuals of both BDS-2 and BDS-3 are larger. For BDS-2/3-B-based POD, the IF code and phase residuals of BDS-2 are 1.07 m and 0.99 cm and that of BDS-3 are 0.64 m and 0.89 cm, respectively. The IF code residual is equivalent to that of BDS-2-based and BDS-3-based POD compared with BDS-2/3-based POD.  
Figure 5 shows the BDS-based orbit RMS difference results compared with the GPS-based orbits, and the RMS difference statistics results are listed in Table 3. As illustrated in Table 3 and Figure 5, the BDS-3-based POD orbit 3D RMS difference is 6.57 cm and is reduced by 56% compared to BDS-2-based POD. This result indicates that the orbit accuracy of LEO POD based on BDS-3 has been greatly improved, compared with BDS-2-based POD.  
For BDS-2 and BDS-3 combined POD, the BDS-2/3-B-based orbit 3D RMS difference of 5.37 cm is the lowest. It reduces 64% to be compared to the BDS-2-based orbit and 18% to be compared to the BDS-3-based orbit, which are significant compared to BDS-2/3-based POD. The orbit RMS difference between BDS-2/3-B-based POD and GPS-based POD is 1.96 cm in the radial direction, less than that of BDS-3-based POD and BDS-2/3-based POD. These results show that the receiver ISB estimated in the BDS-2 and BDS-3 combined POD could improve accuracy for the LEO satellite.  
Figure 4: The BDS-based POD residuals with different strategies.