esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research on Anti-Skid Mechanisms of Asphalt Pavement
### Mechanical Mechanism Between Types and Road Surfaces
#### 2.1.3 Microcutting of Small-Sized Microconvex Bodies on Pavements  
When the contact stress between tyre and road surface is investigated, it can be observed that when a tyre of a highly loaded vehicle is in contact with the sharp raised particles on the road surface, the raised particles tend to lead to stress concentration on the surface of the tyre at a localised area [19]. When the concentrated stress is greater than the fracture strength of the rubber material, the microconvex body will have a microcutting effect on the tyre surface, a phenomenon similar to the ploughing action in metal tribology. Microcutting of the tyre surface by the microconvex body is subject to the resistance of  
Figure 2: Schematic of hysteresis force and energy dissipation calculation.  
the type surface [20], a process that is also part of the friction between the tyre and the road surface. The microcutting effect of the microconvex body on the tyre surface was also further confirmed by researchers using scanning electron microscopy to observe the surface of tyres that had undergone friction tests, as shown in Figure 3[21]. It has been found that the friction due to the microcutting action of the microconvex bodies of the road surface is related to the material properties of the road surface and the tyre, as well as the density, size, and distribution pattern of the microconvex bodies. The microcutting friction can be calculated by Equation (3).  
\[\mathrm{F_{c}}=\mathrm{K_{3}htg}(\delta)\mathrm{T_{max}}=\mathrm{K_{3}K_{4}} \left|\frac{\mathrm{P}}{\mathrm{tg}\delta^{2}}\right|^{\frac{1}{2}}\mathrm{tg }(\delta)\mathrm{T_{max}} \tag{3}\]  
where \(\mathrm{K_{3},K_{4}}\) are constants, \(\mathrm{N}\) is the load normal to the peak of the microconvex body, \(\mathrm{\theta}\) is the simplified cone top angle of the microconvex body, \(\mathrm{h}\) is the depth of the microconvex body piercing into the surface of the tyre, and \(\mathrm{tg}\delta\) is the tangent modulus of the rubber.  
The mechanical mechanism between tyre and pavement can be broadly categorised into three main aspects [22]:  
1. Van der Waals force action between tyre and pavement.
2. Adhesion between tyre and pavement.
3. Microcutting action of small-sized microconvex bodies on the pavement.
## 3 Results
### Availability of Five-System Combination  
With the number of the five-system satellites further increases, the number of visible satellites and the Position Dilution of Precision (PDOP) over the ground tracking station will also have a better performance. We evaluate the global availability of five-system (GPS+GLO+GAL+BDS-2+BDS-3+OZS) combination in terms of satellite numbers and PDOP values in this section. As the final precise orbit product from GFZ is one of the most comprehensive products in support of all the available satellite constellations, it is used to calculate the satellite positions. The sampling interval is 5 min, and the cut-off elevation angle is set to 10. The whole world is divided into 72.72 grids every 2.5 in latitude and 5 in longitude, respectively. The center point of each grid is regarded as a virtual ground tracking station with the geodetic height set to 25 m [38].  
The global maps for the maximum, minimum and average number of visible satellites for five-system combination on day of year (DOY) 122 of 2020 are illustrated in Figure 1. The distribution of satellite numbers has two concentrated areas. One is located in the Asia-Pacific region, where more satellites can be observed since it is also the service regions of BDS-2 and QZSS. The maximum value for the maximum satellite number and the minimum satellite number within this range can be 57 and 48, respectively. The other one is located in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Institutions** & **ID** & **Constellations** & **Orbit** & **Clock** \\ \hline CODE & CODOMGXFIN & GPS+GLO+GAL+BDS-2+OZS & 5 min & 30 s \\ GFZ & GBMOMGXRAP & GPS+GLO+GAL+BDS-2+BDS-3+OZS & 5 min & 30 s \\ CNES/CLS/GRGS & GRGOMGXFIN & GPS+GLO+GAL & 5 min & 30 s \\ IAC & IACOMGXFIN & GPS+GLO-GAL+BDS-2+BDS-3+OZS & 5 min & 30 s \\ JAXA & JAXOMGXFIN & GPS+GLO+OZS & 5 min & 30 s \\ SHAO & SHAOMGXRAP & GPS+GLO+GAL+BDS-2+BDS-3 & 5 min & 5 min/30 s \\ WHU & WUMOMGXFIN & GPS+GLO+GAL+BDS-2+BDS-3+OZS & 15 min & 30 s \\ ESA & ESAOMGNFIN & GPS+GLO+GAL+BDS-2+BDS-3+OZS & 5 min & 30 s \\ CNES & CNT & GPS+GLO+GAL+BDS-2+BDS-3 & 5 min & 5 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the precise satellite orbit and clock products from multi-system analysis centers as of January 2021.  
the Americas, and the minimum value for the maximum satellite number and the minimum satellite number over this region is 33 and 21, respectively. From a global perspective, we can observe 28.6-51.5 satellites on average. This means that the continuous positioning services for whole 24 h will be hardly restricted by insufficient satellites. Figure 2 depicts the global maps for the PDOP values. Corresponding to the satellite visibility distribution, the PDOP values of five-system integration also demonstrate the same regional difference. In the Asia-Pacific region, the minimum value for the maximum and minimum PDOP is 0.8 and 0.6, respectively, while the maximum value for the maximum and minimum PDOP in the Americas is 1.5 and 0.9, respectively. The average PDOP values change from 0.7 to 1.0 on a global scale.
# Prediction of Solvatochromic Polarity Parameters for Aqueous Mixed-Solvent Systems
## 2 Models and Methods
### Predictive Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Entry** & **Component 2** & \(\mu_{2}\) & \(CF^{\text{HBD}}\) & \(\beta^{\text{H}}\) & \(E_{\text{T}}^{0}\) & \multicolumn{2}{c}{**ARD (\%)**} & \multicolumn{2}{c}{**N**} & \multicolumn{1}{c}{**Ref.**} \\ \hline \hline \multicolumn{1}{c}{**(Symbol)**} & \multicolumn{2}{c}{**D**} & **(-)** & **(-)** & **(1)** & **(2)** & **Predict** & **Ideal** & & \\ \hline \multicolumn{10}{c}{Water (1)—HBA cosolololololololololumn{1}{c}{} \\ \(1\) (\(\blacksquare\)) & \(\text{ACN}\) & 3.92 & 1.00 & 4.7 & 63.10 & 46.00 & 6.64 & 3.36 & 11 & [15] \\ \(2\) (\(\blacksquare\)) & \(\text{THF}\) & 1.63 & 1.00 & 5.3 & 63.10 & 37.50 & 4.36 & 4.97 & 11 & [15] \\ \(3\) (\(\bullet\)) & \(\text{GBL}\) & 3.82 & 1.00 & 5.3 & 63.00 & 44.62 & 3.45 & 4.42 & 12 & [23] \\ \(4\) (\(\ast\)) & \(\text{GVL}\) & 5.30 & 1.00 & 5.3 & 63.00 & 47.85 & 2.61 & 6.27 & 12 & [23] \\ \(5\) (\(\bullet\)) & \(\text{PYR}\) & 3.10 & 1.00 & 8.3 & 63.00 & 47.90 & 1.49 & 5.24 & 11 & [24] \\ \(6\) (\(\bullet\)) & \(\text{NMP}\) & 4.09 & 1.00 & 8.3 & 63.00 & 42.20 & 1.85 & 9.85 & 10 & [24] \\ \(7\) (\(\,\)) & DMF & 3.86 & 1.00 & 8.3 & 63.10 & 43.80 & 1.37 & 4.77 & 11 & [15] \\ \(8\) (\(\blacksquare\)) & \(\text{DMSO}\) & \(\text{3.96}\) & 1.00 & 8.9 & 63.10 & 45.00 & 0.75 & 5.29 & 11 & [15] \\ \(9\) (\(\blacksquare\)) & \(\text{Pyridine}\) & 2.19 & 1.00 & 7.0 & 63.10 & 40.30 & 1.29 & 4.77 & 11 & [15] \\ \multicolumn{10}{c}{Overall (aqueous HBA mixtures)} & 2.65 & 5.44 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between electronic transition (\(E_{\text{T}}\), \(\text{kcal-mol}^{-1}\)) values calculated with the predictive model (Equation (2)) and ideal model (Equation (1)) for nine aqueous mixtures of hydrogen bond donor (HBA) cosololvents and nine aqueous mixtures of hydrogen bond donor (HBD) cosolvents at 25 \({}^{\circ}\)C that were obtained with Reichardt indicator. Average relative deviations (ARD) were calculated with Equation (8) along with dipole moment of component 2 (\(\mu_{2}\)), HBD contribution factor (\(CF^{\text{HBD}}\)), Hunter basicity (\(\beta^{H}\)) of component 2, pure properties of electronic transition energy (\(E_{\text{T,i}}^{0}\)) and the number of data used (N). Gray-shaded rows indicate the ARD results estimated from the calculated \(CF^{\text{HBD}}\) (Equation (7)). Figure 4: Flow chart for predicting electronic transition (\(E_{\text{T}}\)) of aqueous mixture with five steps.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.11. Teleophthalmology  
Teleophthalmology indicates a growing interest in remote eye care, particularly for diabetic retinopathy, with AI and telemedicine enhancing access to and diagnostics. Studies have evaluated diabetic retinopathy patients' attitudes towards telemedicine, showing positive reception of remote management [159]. Surveys of visually impaired patients demonstrated acceptance of telemedicine, highlighting the need for disability-specific adaptations [160]. An algorithm for detecting hard exudates in retinal images demonstrated the feasibility of automated screening [161]. Reviews examining AI's role of AI in diabetic retinopathy screening indicate its potential to improve accuracy and reduce specialist workload [162]. The analyses confirmed that home/remote optical coherence tomography (OCT) devices provide accurate retinal imaging outside clinics. Literature reviews have shown teleophthalmology's potential to expand eye care access in underserved populations through remote monitoring [163]. In Poland, teleophthalmology combines AI algorithms with home-based OCT to enhance the diagnostics. While customization remains crucial, patients show positive attitudes towards telemedicine. AI and teleophthalmology improve screening while reducing the specialist burden. Although some studies were clinical, others were survey-based, indicating a need for validation in Poland.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 3 Trade-offs in Wireless Network Design
### Reliability  
Reliability is determined by topology, MAC design and Modulation and Coding Scheme (MCS). One of the major setbacks of wireless technologies in terms of reliability, in comparison to their wired counterparts, is the inter- and intra-technology interference on air which can cause collisions and increase packet loss. Technologies that operate in licensed bands reserve a part of the spectrum for themselves, mitigating the issue. However, spectrum is a scarce resource and it comes at a high price. Private deployments are not possible in reserved spectrum, disabling the possibility of local control over a network. Shared spectrum, on the other hand, can be shared by any number of technologies which can try and mitigate interference by channel hopping or using some MAC layer mechanisms such as Listen Before Talk (LBT). Furthermore, in a single-hop networks, the success probability is entirely dependent on a single link, opposed to multiple links in multi-hop networks. Reliability can be improved by employing both retransmissions and repetitions at the MAC layer, which also add to the latency. To reduce the number of retransmissions, error control techniques such as Forward Error Correction (FEC) can be used. Coding schemes and modulation largely define reliability. Coding rates create extra error checking bits that make modulation more reliable. Modulation schemes are more reliable as they have fewer points on the constellation diagram but also slower. That makes Binary Phase Shift Keying (BPSK) the slowest and the most reliable modulation compared to Quadrature Amplitude Modulation (QAM) and Quadrature Phase Shift Keying (QPSK), given that it only accommodates two points (one bit per burst). QPSK uses four constellations, whereas QAM can have any number of points. Any increase in the number of points on the constellation diagram reduces the space between them, leaving fewer margins for error. This makes QAM the fastest modulation scheme but more unreliable over longer distances.
## 6 Conclusions  
In this work, a damage quantification method for cylindrical structures such as rods and pipes is proposed based on the theoretical prediction of the torsional wave interaction with defects. Theoretical analyses are first carried out to study the influence of the defect size on the reflections from the defect and end. Capitalizing on the wave-damage interaction characteristics, a damage quantification algorithm is designed based on a multi-frequency excitation scheme and the least square principle. Numerical simulations are carried out to validate the theoretical prediction of the wave-damage interaction first and then the damage quantification method. Experiments are finally conducted by the use of a DPC transducer to quantify the defect sizes on two representative steel rods.  
It is found that the defect width influences the reflection from the defect and end in a periodic pattern with the period of the half wavelength of the torsional wave (\(\delta\)/2). When the defect width is \((2n+1)\delta/4\), the reflection from the defect reaches the maximum while minimum reflection is induced by the end. The defect depth nonchronically affects the reflections. With the increase of the defect depth, the reflection from the defect increases  
Figure 14: (**a**) Variation of the \(\overline{SRC}\) with respect to frequency and (**b**) damage image result for the 40 mm-wide and 1.5 mm-deep defect.  
Figure 13: (**a**) The received signal and (**b**) its complex Morlet wavelet transform result for the second damage case.  
sharply first and then becomes saturated while the reflection from the end exhibits the opposite trend. Both numerical and experimental results demonstrate the efficacy of the proposed method for quantifying defect sizes. By the use of the DPC transducer, damage quantification is achieved with a simple measurement system so that the proposed method is significantly enhanced in terms of efficiency.  
The present work not only provides thorough understandings of the torsional wave interaction with defects but also develops an efficient method for damage quantification for cylindrical structures with the aid of the DPC transducer. While the proposed method is firmly validated considering axial-symmetric defects, it also allows quantifying the defects with complex cross-sections in principle. As an improvement, uncertainties in the measurements will be investigated in future work to facilitate the engineering applications of the proposed method.  
Conceptualization, S.S.; methodology, S.S.; software, S.S. and S.X.; validation, S.S. and S.X.; formal analysis, S.S.; investigation, S.S.; resources, Y.P.; data curation, S.S. and S.X.; writing--original draft preparation, S.S.; writing--review and editing, Y.P.; visualization, S.S.; supervision, Y.P.; project administration, Y.P. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundations of China (5202780089).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Data Acquisition and Processing
### EMG-to-Force Processing--Force Processor  
A generic neuromusculoskeletal model, including lower extremity skeletal structures plus all lower extremity muscles, was developed. The model consisted of kinematic descriptions of the ankle, knee, and hip, 3D representations of bones and muscle-tendon paths, and a nominal biomechanical model of each musculotendinous unit (MTU) [47; 48; 49]. A Hill-type muscle in series with an elastic tendon was scaled to estimate the force-generating properties of the MTU [50]. The model included a contractile element plus parallel and series elastic components. Power for each muscle was obtained by scaling the Model that included four parameters: (1) peak isometric force; (2) optimal muscle fiber length; (3) pennation angle, and (4) tendon slack length. Peak isometric force for each muscle was correlated with that muscle's physiological cross-sectional area [51]. Muscle fiber length is the length that matches peak active muscle force [52]. Pennation angle is the angle between the long axis of that muscle's associated tendon and its muscle fibers. Optimal fiber length and pennation angles were adapted from [PERSON], where available [53]; otherwise, values reported by [PERSON] were used [54]. Tendon slack length was the MTU length, beyond which, force develops with tendon elongation [51]. The Force Processor was scaled in a non-linear manner to reflect the force-velocity characteristics associated with muscle eccentric and/or concentric contractions [54; 55]. Muscle contraction type and history, plus muscle force-velocity relations, were considered [56; 4].
## 4 Modeling Methodology  
The methodology of the development and validation of the groundwater potential models and their outcomes (potential maps) are shown in Figure 3, which involves the following steps: (1) Collection and preparation of data. The data was collected from various government sources, meteorological  
Figure 2: Maps of the influencing factors used in this study: (**a**) aspect, (**b**) curvature, (**c**) elevation, (**d**) flow direction, (**e**) slope, (**f**) land use, (**g**) Topographic Wetness Index (TWI), (**h**) Sediment Transport Index (STI), (**i**) river density, (**j**) rainfall, (**k**) geology, and (**I**) soil.  
departments, and satellite images. (2) Factor selection: The OneR feature selection method was used to validate and select the important factors for modeling use. (3) Construction of models: four ensemble models, namely DLR, BLR, CGLR, and RSSLR, were developed using the training dataset. More specifically, DLR is a combination of the LR model and the dagging ensemble technique, of which dagging was used to optimize the training datasets for the construction of the ensemble DLR model. BLR is a combination of the LR model and the bagging ensemble technique, of which bagging was used to optimize the training datasets for the construction of the ensemble BLR model. RSSLR is a combination of the LR model and the RSS ensemble technique, of which RSS was used to optimize the training datasets for the construction of the ensemble RSSLR model. CGLR is a combination of the LR model and the CG ensemble technique, of which CG was used to optimize the training datasets for the construction of the ensemble RSSLR model. (4) Validation of models: various statistical measures were used for the validation of groundwater potential models. These measures were AUC, NPV, SST, RMSE, ACC, SPF, PPV, and Kappa. Thereafter, the generation of groundwater potential maps was carried out using GIS software.
# Engineering Vehicle Detection Based on Improved YOLOv6
## 4 Experiments
### Experimental Results
#### 4.4.1 Comparison between Different Models  
We contrast the results of experiments on engineering vehicle detection using the current popular YOLO series algorithm and a subset of two-stage algorithms. Detailed performances are displayed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(mAP_{50:95}\)**(\%)** & \(mAP_{50}\)**(\%)** & \(mAP_{75}\)**(\%)** \\ \hline YOLOv5-L [18] & 76.5 & 91.5 & 83.0 \\ YOLOv6-L [5] & 82.8 & 93.4 & 86.2 \\ YOLOv7-L [19] & 70.0 & 89.0 & 77.7 \\ YOLOv8-L [20] & 85.0 & 94.0 & 88.2 \\ YOLOv-L [21] & 71.8 & 91.5 & 80.8 \\ Faster R-CNN R50 [14] & 70.3 & 90.1 & 79.4 \\ Faster R-CNN R101 [14] & 68.8 & 89.9 & 78.3 \\ Casade R-CNN R50 [15] & 70.5 & 90.4 & 79.4 \\ Casade R-CNN R101 [15] & 70.4 & 89.2 & 79.4 \\ YOLOv6-L Swin-B (Ours) & 88.5 & 95.9 & 91.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: An analysis of the experimental outcomes among various models.  
As shown in Table 2, in contrast to the current mainstream YOLO series algorithms and two-stage models such as Faster R-CNN, the proposed improved YOLOv6 model has achieved the best results in \(mAP_{50:95}\) and other indicators, in which \(mAP_{50:95}\) reached 88.5% and \(mAP_{50}\) reached 95.9%.  
To further evaluate the accuracy and robustness of our model's predictions, we conducted a statistical analysis by calculating the mean and standard deviation of the IoU between the predicted results and the ground truth. This analysis allows us to assess the uniformity and consistency of the model's prediction accuracy. Additionally, we computed the mean and standard deviation of the IoU based on the ground truth to evaluate the uniformity and consistency of detection coverage. Furthermore, to demonstrate that our proposed algorithm is significantly better than other methods, we also calculated the significance level at which the mean IoU of our proposed method is greater than that of other algorithms. The results are detailed in Table 3.  
As shown in Table 3, our proposed method achieves the highest prediction accuracy among several common object detection methods, with only a slightly lower consistency compared to YOLOv5-L. Except for YOLOv8-L and YOLOv5-L, the significance levels are all below 0.05, indicating that our proposed method is significantly more accurate in detection than most other algorithms. This slight difference is attributable to YOLOv5-L detecting fewer targets, as it has stricter detection criteria. This stricter criterion results in YOLOv5-L having a lower mean IoU based on ground truth, indicating poorer detection coverage. In contrast, our proposed approach outperforms others in both detection coverage and consistency, with significance levels all below 0.05, demonstrating that our method provides a comprehensive and balanced detection for all ground truth.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.4 Mobile Number Portability  
Mobile number portability (MNP) is primarily deployed through intermediaries, such as roaming. In existing 5G and previous-generation networks, a clearing house is an intermediary in each country, where MNP is deployed as a semi-automated or fully automated process. Once regulatory requirements have been established, such a process can be efficiently designed using smart contracts as a self-executing autonomous process. Blockchain can easily facilitate number portability by enabling the secure and efficient transfer of phone numbers between different telecom operators using smart contracts and distributed immutable ledgers provided by blockchain technologies. With the addition of interoperability features, blockchain networks can ensure seamless porting processes while maintaining data integrity and security. In [32], [PERSON] et al. offered an MNP solution using a permissioned private blockchain based on Hyperledger and smart contracts. According to [32], automated, immutable processes with smart contracts increase trust, reduce the MNP processing time, and eliminate processing errors through a transparent, fully automated, and distributed process. The traditional mobile number portability process is illustrated in Figure 4.  
Figure 4: Mobile number portability (MNP) porting process.  
Figure 3: International roaming records processing flow for mobile networks.
## 4 Example Verification
### Experimental Results and Analysis
#### 4.3.2 Comparison Experiment of Different Detection Algorithms  
Considering the real-time performance and accuracy requirements for concrete bridge surface defect detection tasks, the two-stage object detection models of the RCNN series and the outdated SSD models were not included in the comparative experiments. Instead, more widely used and advanced models from the YOLO series were selected as the benchmark models. The results are presented in Table 4.  
Comparing the data in Table 4, it is evident that the model proposed in this paper exhibited more effective performance in terms of detection accuracy compared to the current state-of-the-art (SOTA) models. The mAP0.5 value was improved by 11.1%, 0.7%, and 1.7% compared to the classical YOLOv3-tiny, YOLOv5s, and YOLOv6s models, respectively. Compared to the baseline model YOLOv8s, the proposed model achieved a 2.0% and 4.4% improvement in average precision (mAP0.5 and mAP0.5:0.95, respectively), a 3.4% increase in recall rate, an increase of 3.5/f\(\cdot\)s\({}^{-1}\) in detection speed, and a reduction in model complexity by 1.2G. These results demonstrate that the proposed model had better detection performance in concrete bridge surface defect detection.