esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
We analyzed a bus route with six stations using historical and real-time data from Google Maps Service. The congestion statuses for each station are in Table 2.  
Of the six bus stations, two exhibit \"light congestion\", two show \"moderate congestion\", one experiences \"heavier-than-usual congestion\", and one encounters \"heavy congestion\". Hence, the _SDCF_ computes to 2.16, which denotes the comprehensive congestion level across the route. This factor offers a statistical overview regarding congestion levels relative to typical public usage patterns of the respective transportation modes, contextualized by the time of day. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Station No** & **Congestion Status** & **Congestion Factor** \\ \hline Station 1 & Light & 1 \\ Station 2 & Moderate & 2 \\ Station 3 & Heavier-than-usual & 3 \\ Station 4 & Moderate & 2 \\ Station 5 & Heavy & 4 \\ Station 6 & Light & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The SDC component example. Table 3 presents all 100 tests conducted, showing the number of identified versus actual individuals in the images and the _LAR_. The trusted factor was set to 0.5 for the tests presented in Table 3.  
For instance, the first test indicates that the Computer Vision service correctly recognized three persons out of five. Therefore, the _LAR_ value is 60% (_LAR_\({}_{1}\) = 60%). This way, the recognition percentage was calculated for each test. Finally, by summing all the percentages and dividing by the total number of tests (100), the overall recognition rate (_GAR_) was obtained. For the tests presented in Table 3, the _GAR_ is 89.81%. This value indicates a general algorithm's precision in identifying individuals in various image scenarios and conditions. The Computer Vision service correctly recognizes the number of people in images with an accuracy of 73%. This value was calculated as the number of images where it correctly identified the number of people divided by the total number of images used in the tests. Therefore, the service correctly recognized the number of people in 73 images out of a total of 100 images. Next, we analyzed the possible combinations of scenarios for the PUTSS algorithm and explanations for each scenario. **Scenario 1. Normal Traffic Conditions**  
_SDCF_: 2 (moderate congestion). _RTCVCF_: 20 (20 passengers at the station). Bus Capacity (_k_): up to 50 passengers. Decision Output: 0 (bus departs as scheduled). In this scenario, the _SDCF_ indicates moderate congestion, while the _RTCVCF_ shows an expected number of passengers at the station. Since the number of passengers is below the bus capacity, the bus departs as scheduled, maximizing transport efficiency without making passengers wait excessively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Test No. ** & **Result** & **LAR** & **Test No. ** & **Result** & **LAR** & **Test No. ** & **Result** & **LAR** & **Test No. ** & **Result** & _LAR_ \\ \hline
1. & 3
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Physical Properties and Their Characterization
#### 3.1.2 Scanning Electron Microscopy (SEM)  
SEM is a powerful imaging method that uses electrons to examine the surface morphology of a sample at high resolution, enabling the detailed examination of individual nanoparticles [96].  
In the case of MNPs, SEM provides quantitative information regarding their size, shape, and surface. SEM allows the visualization of surface modifications and functionalization, providing insights into their stability and potential applications [97]. SEM can reveal the agglomeration state of magnetic nanoparticles, which is important for understanding their behavior in different environments [98; 99]. However, this technique does not directly provide information about the magnetic properties of MNPs which can be evaluated by means of magnetometry methods.  
The SEM of MNPs is crucial for understanding their physical characteristics, which is vital for applications in drug delivery, magnetic hyperthermia, magnetic resonance imaging, and other biomedical and technological fields [97; 98; 99].
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 2 SAR Image Recognition with Sparse Weighting Spatial Pyramid Pooling  
The method proposed herein simultaneously utilizes the SPM model and SR to deal with SAR image recognition. Enlightened by the idea that different parts of an image play different roles, a sparse weighting spatial pyramid pooling method is proposed to extract a new type of feature. The main objective of utilizing this method is to reduce the influence of background clutter and enhance the target. Figure 1 exhibits the flowchart of the proposed SAR image target recognition method. Firstly, an image was divided into gradual fine sub-regions; then, the dense local features were calculated, followed by coding and pooling according to SPM, in order to obtain feature vectors of the sub-regions, respectively. Additionally, the pooling feature vector of the sub-regions at each pyramid level was weighted based on the dependability, which was determined according to the residuals obtained by the SR. Finally, the representation for SAR images was built by systematically concatenating the weighted feature vectors. With sparse representation classification, the method is robust, in particular, in dealing with the speckle noise and large clutter background.
## 3 Materials and Methods
### Data Analysis  
During SCPTu tests, tip resistance (q\({}_{\text{t}}\)), lateral friction (f\({}_{\text{s}}\)) and pore pressure (u\({}_{2}\)) were continuously measured. Pore pressure ratio (B\({}_{\text{q}}\)) (Equation (2)) and material classification index (I\({}_{\text{c}}\)) (Equation (6)) were also calculated after piezocone test results. The latter parameter can be calculated as follows [39]:  
\[\text{I}_{\text{c}}=\sqrt{\left(3.47-\text{log}\text{Q}_{\text{t}}\right)^{2}+ \left(\text{log}\text{F}_{\text{r}}+1.22\right)^{2}} \tag{6}\]  
where Q\({}_{\text{t}}\) is the normalized tip resistance, and F\({}_{\text{r}}\) is the normalized friction ratio. Figure 4 illustrates depth-dependent profiles of q\({}_{\text{t}}\), f\({}_{\text{s}}\), u\({}_{2}\), B\({}_{\text{q}}\), and I\({}_{\text{c}}\) in a standard SCPTu result for Structure 3.  
Seismic tests were performed at specific depths in all SCPTu tests. The small strain shear modulus (G\({}_{0}\)) was obtained considering the shear wave velocities (V\({}_{\text{s}}\)) recorded during field tests and the specific gravity (\(\rho\)) of the tailings obtained from the laboratory tests executed, as shown in Equation (7), proposed by [PERSON] and [PERSON] [40]:  
\[\text{G}_{0}=\rho\times\text{V}_{\text{s}}{}^{2} \tag{7}\]  
Dissipation tests were additionally conducted on specific boreholes. The termination criterion for these tests was set at a minimum of 50% dissipation from the initially recorded pore pressure. Consequently, the equivalent time (t\({}_{50}\)) was employed to calculate the horizontal consolidation coefficient (c\({}_{\text{h}}\)), as per Equation (8), originally proposed by [PERSON] and [PERSON] [41]:  
\[\text{c}_{\text{h}}=\frac{\text{T}\times\text{r}^{2}\times\text{I}_{\text{r}} ^{0.5}}{\text{t}_{50}} \tag{8}\]  
Figure 4: Typical SPTu results from Structure 3 showing q\({}_{\text{r}}\), f\({}_{\text{s}}\), u\({}_{2}\), B\({}_{\text{q}}\) and I\({}_{\text{c}}\) plotted against depth. The layers from the I\({}_{\text{c}}\) graphic stand for: 1—Gravelly sand, 2—Sands: clean to silty, 3—Silty sands to sandy silts, 4—Clayey silt to silty clay, 5—Clays, and 6—Organic soils.  
where T is the time factor, r is the piezocone radius, and I\({}_{\text{r}}\) is the stiffness index. During these tests, data for c\({}_{\text{h}}\) were recorded, and subsequently, c\({}_{\text{v}}\) was computed using Equation (9):  
\[\text{c}_{\text{v}}=\frac{\text{c}_{\text{h}}}{\text{K}_{\text{h}}/\text{k}_{ \text{v}}} \tag{9}\]  
where the ratio (K\({}_{\text{h}}\)/k\({}_{\text{v}}\)) represents the anisotropy between vertical and horizontal permeabilities. The ratio (K\({}_{\text{h}}\)/k\({}_{\text{v}}\)) is set at a value of 2, following the approach by [PERSON] [42].  
Parameters such as G\({}_{0}\) (Equation (7)) and c\({}_{\text{v}}\) (Equation (9)) were exclusively calculated at specific depths corresponding to the locations of seismic or dissipations tests. Consequently, associated parameters, such as \(\Psi\) and V, were likewise computed at the same specific depths.
# Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques
###### Abstract  
Breast cancer is considered one of the major threats for women's health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.  
thermospram; homomorphic filtering; top-hat transform; adaptive histogram; equalization; K-mean; signature boundary; MLP; ELM Article
# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
## 5 Data Analysis and Results
### Assessment of Measurement Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Item Label** & **Factor Loading** & **Cronbach’s Alpha** & **rho\_A** & **Composite** & **Average Variance** \\
**Name** & & & & & **Reliability** & **Extracted (AVE)** \\ \hline Attitude Toward the Intention & & 0.849 & 0.856 & 0.892 & 0.624 \\ Q\_01 & 0.869 & & & & \\ Q\_02 & 0.806 & & & & \\ Q\_03 & 0.740 & & & & \\ Q\_04 & 0.789 & & & & \\ Q\_05 & 0.739 & & & & \\ Subjective Norms & & 0.814 & 0.818 & 0.864 & 0.516 \\ Q\_06 & 0.702 & & & & \\ Q\_07 & 0.696 & & & & \\ Q\_08 & 0.733 & & & & \\ Q\_09 & 0.796 & & & & \\ Q\_10 & 0.744 & & & & \\ Q\_11 & 0.630 & & & & \\ Perceived Behavioral Control & & 0.760 & 0.781 & 0.844 & 0.574 \\ Q\_12 & 0.773 & & & & \\ Q\_13 & 0.779 & & & & \\ Q\_14 & 0.728 & & & & \\ Q\_15 & 0.750 & & & & \\ Intention & & 0.752 & 0.772 & 0.844 & 0.577 \\ Q\_16 & 0.773 & & & & \\ Q\_17 & 0.838 & & & & \\ Q\_18 & 0.777 & & & & \\ Q\_19 & 0.634 & & & & \\ Pedestrian Behavior & & 0.807 & 0.807 & 0.866 & 0.566 \\ Q\_20 & 0.769 & & & & \\ Q\_21 & 0.758 & & & & \\ Q\_22 & 0.802 & & & & \\ Q\_23 & 0.783 & & & & \\ Q\_24 & 0.639 & & & & \\ \hline \hline \end{tabular} Note: ATI = Attitude Toward the Intention, I = Intention, PB = Pedestrian Behavior, PBC = Perceived Behavioral Control, SN = Subjective Norms.  
\end{table} 
Table 6: Evaluation of the Measurement Model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ATI** & **I** & **PB** & **PBC** & **SN** \\ \hline Attitude Toward the Intention & 0.790 & & & & \\ Intention & 0.269 & 0.759 & & & \\ Pedestrian Behavior & 0.234 & 0.372 & 0.752 & & \\ Perceived Behavioral Control & 0.284 & 0.342 & 0.441 & 0.758 & \\ Subjective Norms & 0.141 & 0.435 & 0.507 & 0.359 & 0.719 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation of the Discriminate Validity by Fornell and Lracker Criteria.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 3 Unified Soil Classification System (USCS)
### Grain Size Distribution Analysis  
The next phase involves analyzing the distribution of grain sizes, which is crucial for understanding soil composition. This examination initiates with sieve analysis, where the soil is sequentially sieved through a series of screens with decreasing mesh sizes. The quantity of soil retained on each sieve is then meticulously weighed to determine the distribution of grain sizes. Coarse-grained soils, such as sand and gravel, are subjected  
Figure 2: The procedure of USCS.  
to specific sieves like No. 4 (4.75 mm), No. 10 (2.0 mm), No. 40 (0.425 mm), and No. 200 (0.075 mm). Conversely, fine-grained soils like silt and clay undergo hydrometer analysis to accurately assess particle sizes smaller than 0.075 mm. This approach facilitates the precise measurement of the silt and clay fractions' distribution within the soil sample.  
In grain size distribution analysis, acquiring parameters like Cu and Cc is crucial for comprehending soil properties. Cu signifies the uniformity of grain sizes within a soil sample and is determined as  
\[\text{Cu}=\frac{\text{D}_{60}}{\text{D}_{10}}, \tag{1}\]  
where \(\text{D}_{60}\) and \(\text{D}_{10}\) represent the particle diameters at which 60% and 10% of the sample's mass is finer, respectively. This parameter is crucial as it indicates the soil's gradation: a higher Cu suggests a well graded soil with a wide range of particle sizes, which generally provides better compaction and stability, while a lower Cu indicates a poorly graded soil with a narrower range of particle sizes, potentially leading to weaker structural properties.  
The Cc is another important parameter that provides insight into the soil's gradation characteristics. It is calculated as  
\[\text{Cc}=\frac{(\text{D}_{30})^{2}}{\text{D}_{10}\times\text{D}_{60}}. \tag{2}\]  
In grain size distribution analysis, \(\text{D}_{30}\) represents the particle diameter at which 30% of the sample's mass is finer. The Cc parameter aids in interpreting the shape of the soil's gradation curve, particularly focusing on the curvature around the \(\text{D}_{30}\) value. Typically, values between 1 and 3 suggest a well graded soil, while those outside this range indicate a poorly graded soil. This insight is crucial for assessing the soil's appropriateness for engineering and construction endeavors. Well graded soils generally have a superior load-bearing capacity. In terms of drainage, poorly graded soils with uniformly sized particles tend to drain better due to the larger voids between the particles.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 3 Motivation  
The examined algorithms in the related works require higher computational resources like \(DCHGA\)[20], \(HMRC\)[21], \(VCACWSN\)[23], \(OPDCC\)[26], \(DCTC_{k}\)[16], and \(AAEECC\)[22]. We will present this part in Section 5. Applying the aforementioned algorithms in distributed environments to run locally on each device, drains the battery energy quickly. Consequently, they might not behave well in networks where devices are energy-constrained. Although \(ABSDC\)[24] presents an efficient algorithm and requires low energy consumption. However, authors adopted the triangular circular packing idea. This approach forces a fixed angle between the connected devices as well as a fixed distance between them. Every device should be \(r*\sqrt{3}\) apart from its neighbor devices where r is the device's range. This restricted the algorithm to work on homogeneous environments only. Also, the overlapping between devices is constant and is the same between all devices which is not suitable for heterogeneous environments.  
\(CMHN\)[27] is designed to maintain connectivity and maximize the coverage. It minimizes energy consumption by reducing the number of nodes' displacement in the network. However, it is a centralized algorithm. In some scenarios, the devices need to take decisions locally, without waiting to be told what to do by a central station, such as in harsh environments, wide forests, monitoring huge amounts of cattle. In such situations, the adoption of distributed algorithm is more appropriate. By investigating the behavior of this algorithm, we noticed the formation of gaps/holes in some scattered distributions. It occurs when a device \(D_{i}\) moves to its final position, it tries to find the closest device \(D_{j}\) that can intersect with (i.e., a device that has enough free arc on its circular range for connection).  
In this paper, our work is based on \(CMHN\). We propose a distributed version called \(MCCM\), for maintaining connectivity and maximizing coverage in disaster and harsh scenarios. The basic idea is that devices are deployed randomly and need to re-organize themselves. Every device takes the decision to move to a nearest location to maximize its coverage while maintaining connectivity with others. We aim to have fewer movements and energy drain. Another part we tackle in this work is that after the self-organizing process is done, disjoints groups of connected devices might appear in the network. We call them \"islands\" of devices. We bridge the islands to ensure more connectivity using alpha-shape technique to find the islands' boundaries and shortest path between adjacent boundary sets.
## 2 Materials and Methods
### Digital Subtraction Angiography Data Acquisition and Evaluation  
All DSA (6-vessel catheter angiogram) data were acquired during routine clinical examinations. All angiographic rating was performed by a neuroradiologist with extensive experience in neurovascular interventions.  
Similarly to [PERSON] et al., the modified Suzuki Score (mSS) was used to account for stenoses of the ICA, the MCA and the ACA and the presence or absence of lenticulostriate collaterals [17]. The mSS ranges from 0 to IV, with more severe disease represented by higher values [17]. Analogously to [PERSON] et al., for simplification, we considered the mSS impaired if it accounted for \(\geq\)II [17].  
Additionally, analogously to [PERSON] et al., regional collateralization was evaluated by dividing the DSA into 7 anatomical sites based on the ASPECTS-defined regional vascular territories (M1-M6 and basal ganglia) [17]. A vascular territory was defined as impaired if no or only peripheral collaterals supplying the ischemic site were visible [17]. A territory was considered not impaired if there was a normal anterograde flow or if the ischemic bed was completely irrigated by a collateral flow [17]. Analogously to [PERSON] et al., the total number of impaired territories (0-7) was counted for each MCA territory, and, for the simplification of the scoring system, the collateralization of the MCA was considered impaired if \(\geq\)2 territories were impaired [17].  
Since we intended to evaluate not only the ICA territories but also the MCA and ACA territories separately, we constructed a rating of the ACA territory, analogous to the one proposed above, namely the ASPECTS-based rating of the MCA. The arterial territories atlas [25] was adapted to divide the ACA territories further into the anterior part (orbitofrontal, frontopolar, anterior and middle frontal arteries) and posterior part (posterior frontal, precentral, superior and inferior parietal branches).
## 4 Security Analysis and Discussion
### Fairness  
Our quantum key agreement protocol achieves fairness--a critical property where no participant can unduly influence the final key. This is accomplished through symmetric architectural design and quantum mechanical principles. The protocol enforces fairness through balanced contribution mechanisms where both the Patient's Device (PD) and Healthcare Provider (HP) equally influence the final key. Each party contributes \(4n\) quantum states that determine \(2n\) bits of the cryptographic material, with the final key derived as \(K=K_{PD}\oplus K_{HP}\oplus f\). This ensures neither party can unilaterally control more than half the key bits.
## 2 Materials and Methods  
### Mobile Satellite-Based Measurement of the Track Axis  
This article focuses on a measurement method developed by the authors for determining the position and geometry of the railway track axis within a designated spatial reference frame, typically the officially adopted one [19]. To achieve this, the authors designed an integrated measurement and information system (illustrated in Figure 1) that leverages advanced GNSS (Global Navigation Satellite System) positioning techniques (Figure 1b-d), supported by an Inertial Navigation System (INS) (Figure 1a,f), photogrammetry (Figure 1g), and Terrestrial Laser Scanning (TLS) methods (Figure 1e). The measuring equipment was selected by the authors based on its commercial availability, ensuring that access to the necessary hardware would not impede future implementation, while maintaining a high-standard metrological performance.  
Each of these techniques plays a crucial role in addressing the diverse conditions encountered during continuous mobile measurements. The data recorded by each system are time-synchronized, ensuring their contributions are properly aligned during processing to enhance measurement reliability and reduce uncertainty.  
The GNSS is central to the proposed solution, thanks to advances in RTK/RTN (Real-Time Kinematic/Real-Time Network) technologies, which apply real-time corrections to raw measurements and yield position accuracies on the order of a few centimeters. However, continuous GNSS-based measurements must be supplemented by the INS, which provides platform tilt angles--both longitudinal slopes due to track gradients and transverse tilts from track irregularities or superelevation on curves.  
The INS further enhances the measurement process in situations where terrain obstructions severely degrade satellite signal reception. Photogrammetric techniques were used to compute corrections for minor wheelset displacements relative to the rail heads, which arise from the vehicle's dynamic behavior--such as yaw motions and curve negotiation. Terrestrial Laser Scanning (TLS) provides an alternative and complementary source of spatial information, which is particularly valuable during prolonged GNSS signal loss, such as in sections where even INS data must be spatially corrected [20].  
Figure 1: Railway track axis measurements using mobile GNSS (**b**–**d**), INS (**a**,**f**), TLS (**e**), and photogrammetry (**g**) techniques.