esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Structural Displacement  
The deflection of observation point #15 of the main girder and the response value of horizontal displacement at the top of the cable tower were extracted, and the DIC and DAF\({}_{2}\) of the displacement after the cable breaking were calculated, as shown in Figure 12.  
As shown in Figure 12a, the dynamic deflection of the #S29 cable was smaller than that of the #M24 cable. The maximum dynamic deflection at the observation point of the main girder #15 was \(-\)0.183 m, which was less than the L/500 stiffness limit of 0.476 m required by the Chinese design standard [23]. The DIC of the maximum deflection of the main girder was 2.088, and DAF\({}_{2}\) was 1.159. It can be seen from Figure 12b that the top displacement of the tower changed sharply due to the breakage of the upstream cables, and changed sharply again due to the breakage of the downstream cables. The breakage of the #M24 cable reduced the displacement of the tower top, the maximum dynamic displacement was \(-\)0.019 m, and the dynamic amplification factor of displacement was 1.791. The breakage of the #S29 cable increased the displacement, and the maximum dynamic displacement was \(-\)0.081 m, which was 3.689 times that of the initial displacement, and the dynamic amplification factor of displacement was 1.103.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Broken**} & \multirow{2}{*}{**Observation**} & \multicolumn{4}{c}{**Stress (MPa)**} & \multirow{2}{*}{**DIC**} & \multirow{2}{*}{**DAF\({}_{2}\)**} & \multirow{2}{*}{**DCR**} \\ \cline{3-4} \cline{6-7}  & & \(\mathbf{S_{0}}\) & & & & & \\ \hline \multirow{4}{*}{\#M24} & a & \(-\)7.93 & \(-\)8.29 & \(-\)7.80 & 1.045 & 1.063 & 0.26 \\  & b & \(-\)13.40 & \(-\)13.55 & \(-\)13.15 & 1.011 & 1.030 & 0.42 \\  & c & \(-\)6.94
# Personalised High Tibal Osteotomy Surgery Is Accurate: An Assessment Using 3D Distance Mapping
###### Abstract  
Early-stage knee osteoarthritis is often suitable for treatment with high tibial osteotomy (HTO). This is an effective joint-preserving treatment, resulting in good postoperative outcomes. To overcome the limitations of traditional HTO, the surgical technique and correction accuracy can be enhanced by personalised procedures using three-dimensional digital planning and metal additive manufacturing. The purpose of this clinical trial study was to evaluate the three-dimensional accuracy of a new personalised HTO procedure, using modern imaging techniques, 3D modelling, and distance map analysis (DMA). Twenty-five patients were treated with the personalised HTO procedure. Before surgery and after 6 months, they underwent clinical evaluation scoring, radiographic imaging, and computed-tomography scanning to generate morphological models. Specifically, preoperative tibia models were used to plan the tibia correction and the design and position of the fixation plate. Preoperative, planned, and postoperative models were imported in computer-aided and designing software (Geomagic ControlTIM 2014, 3D Systems, Rock Hill, SC, USA) for DMA implementation to assess geometrical differences between model surfaces. A very good reproduction of the planned tibia morphology was achieved postoperatively (average differences between \(-0.9\) mm and \(1.4\) mm). DMA values associated with fixation-plate deformation were less than \(1\) mm, similar to those for plate-to-tibia surface-contour matching. Overall, personalised digitally planned HTO utilising three-dimensional printed surgical guides and plates enables accurate planned correction and plate placement.  
high tibial osteotomy; personalised orthopaedic treatment; 3D printing; distance map analysis; computational modelling; computational planning
## 3 Results and Discussion
### Nuclear Magnetic Resonance Measurements--T\({}_{2}\) Relaxation Time and Diffusion Coefficient
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Relaxation Time T\({}_{2}\) [s]**} \\ \cline{2-3}  & **O1** & **O2** \\ \hline Ox & 5.39\(\times\)10\({}^{-3}\) & 6.49\(\times\)10\({}^{-3}\) \\ R:Ox = 3:1 & 2.71\(\times\)10\({}^{-3}\) & 3.59\(\times\)10\({}^{-3}\) \\ R:Ox = 2:1 & 3.00\(\times\)10\({}^{-3}\) & 3.69\(\times\)10\({}^{-3}\) \\ R:Ox = 1:1 & 4.22\(\times\)10\({}^{-3}\) & 4.90\(\times\)10\({}^{-3}\) \\ R:Ox = 1:2 & 4.21\(\times\)10\({}^{-3}\) & 6.33\(\times\)10\({}^{-3}\) \\ R:Ox = 1:3 & 4.07\(\times\)10\({}^{-3}\) & 6.38\(\times\)10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: T\({}_{2}\) relaxation time of the O1, O2, and extenders. Figure 1: Trend of spin–spin relaxation time of oils and extenders: (**a**) O1 and O1-R blends; (**b**) O2 and O2-R blends. concerning the molecular dynamics and spatial dimensions of the particles and cavities in order to establish the particles' mobility and the microstructures of the samples [26]. The NMR spectra were obtained from Free Induction Decay by means of the Fourier transform. Path widths equal to \(\pi\)/2 and 16 scans were used to perform all of the NMR tests. The self-diffusion coefficient was determined directly by the use of a standard pulsed gradient stimulated echo (PGStE) sequence with a mono-exponential fitting [27]. The self-diffusion coefficient can be ascribed to the average diffusion phenomena of oil onto (external) and into (internal) the R particles. The self-diffusion coefficients of the extenders are shown in Figure 2. In theory, the self-diffusion coefficients should decrease with the addition of R because it reduces the particles' mobility. In fact, the extenders at ratios equal to 2:1 and 3:1 showed low values of the self-diffusion coefficient for both O1 and O2 samples. The oil self-diffusion coefficient jumped at the ratio of 1:1. Hence, the behavior was in agreement with the higher mobility of the oil when the maximum absorption was reached.
# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 6 Conclusions  
The present study presents a practical application of the YOLOv8m CNN in identifying eight distinct _Capsicum_ pepper varieties. Implementing data augmentation techniques significantly enhanced the model's precision, recall, and mean average precision (mAP)metrics. These findings underscore the potential of deep learning to revolutionize food quality practices, particularly in automating management tasks.  
Although this research identified the effectiveness of the YOLOv8m model for pepper identification, further research is essential to enhance the model's robustness in real-world scenarios by including images captured under diverse real-world conditions, such as varying lighting intensities, partial occlusions from overlapping produce, heterogeneous backgrounds, and differences in image resolution and camera angles. Exploring advanced techniques, such as transfer learning and alternative object detection models, could improve the model's generalization capacity and adaptability.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15137024/s1](https://www.mdpi.com/article/10.3390/app15137024/s1)]([https://www.mdpi.com/article/10.3390/app15137024/s1](https://www.mdpi.com/article/10.3390/app15137024/s1)). All results with and without the augmentation procedure.  
Conceptualization, M.d.O.B. and L.d.S.C.; methodology, M.d.O.B.; software, F.P.L.A.; validation, S.d.S.S., M.d.O.B. and F.P.L.A.; formal analysis, L.d.A.N.; investigation, M.d.O.B.; resources, M.d.O.B.; data curation, S.d.S.S.; writing--original draft preparation, M.d.O.B.; writing--review and editing, L.d.A.N.; visualization, L.d.S.C.; supervision, M.T.O. and L.d.A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data will be made available upon request.  
The authors thank the Coordination for the Advancement of Higher Education-CAPES for the scholarship and the cooperation between Universidade Paulista and CEAGESP, which made the images available.  
The authors declare that they have no conflicts of interest.  
The following abbreviations are used in this manuscript:  
YOLO & You only look once (real-time object detection framework)  
CNN & Convolutional neural network  
MobileNet & CNN architecture developed by Google, designed for mobile applications  
Squeeze-and-excitation network (a channel attention mechanism for  
improving CNN performance)  
CBAM & Convolutional block attention module (a lightweight attention module  
combining spatial and channel attention)  
YOLO-NAS & YOLO with neural architecture search (an optimized variant of YOLO using  
automated architecture search for improved accuracy and efficiency)
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 4 Evaluating Existing Networks  
In order to verify the proposed model, we consider multiple case studies--three that have already been completed and three that are in the design stage. The purpose of the completed studies is to evaluate the gamut of possibilities. Recommendations are then made for the studies in the design stage. The completed studies that are evaluated are:  
* High Performance Wireless Research and Education Network (HPWREN) is the first WiLDNet of which we are aware. It connects universities with research centers while providing connectivity to Native American Reservations [36]. It is a highly heterogeneous mixture of private and public networks that is commercially viable.
* A pilot project carried by an American university in Rwanda and in coordination with the Ministry of Health (MoH) of Rwanda. The purpose for establishing the network was to collect health data. The network operated on the commercial cell phone system.
* NapoNet is a network that spans the Napo River of Peru providing service to 15 remote health posts. The Napo Network was funded by the Global Fund at a time when there was no commercial service available in this sparsely populated portion of the Peruvian Amazon. The only option was the most inexpensive private network transmission technology available in 2007.  
The proposed framework presented in Section 3 will be used to evaluate the completed studies. The elements will include a different aspect of the business canvas such as value proposition and the choice of key partners.
## 3 Methodology
### Model Accuracy Evaluation and Feature Importance Analysis
#### 3.4.2 Feature Importance Analysis  
The CatBoost model is a black-box model, and its algorithm itself has some degree of unexplainability. To gain a more comprehensive understanding of the model's scientific validity and accuracy, we introduced the SHAP algorithm in this study for model interpretation. SHAP is a model explanation package for the python platform, and using this model interpreter, we can analyze the importance of each feature variable in the CatBoost model's predictions. This enhances the interpretability and visual representation of the feature variables, which helps to improve the accuracy of the CatBoost model and analyze its interpretability and visual expression.  
SHAP values are a method based on cooperative game theory for calculating the contributions of features to a model [46]; the principle is as follows: for a feature \"?\", we need to calculate the SHAP values for all possible feature combinations (including different orders of feature combinations) and then perform a weighted sum, expressed as follows:  
\[\phi_{i}=\sum_{S\subseteq N\backslash\{x_{j}\}}\frac{|S|!(n{-}|S|{-}1)!}{n!}[v (S\cup
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 4 WSN Design Challenges and Unique Characteristics
These issues are likely to complicate the design of security routing techniques [39]. * Medium- to large-scale networks of hundreds or thousands of nodes deployed randomly or uniformly throughout the network field presents a challenge when designing security algorithms that are sufficiently flexible to support different security-level requirements. * The scalability of WSNs implies handling large amounts of data that may have inconsistent, noisy, erroneous, redundant, and missing values, which requires designing intelligent security approaches that can correctly interpret data to drive intelligent decision-making. * Data transmission over multi-hop relaying creates a significant threat, as relays could be eavesdroppers [40], and communicated data may be breached, tampered with, or forged. * Time synchronization is an issue, as nodes are independently controlled in the field. Local clocks should be coordinated to avoid synchronization uncertainties, which could cause sensed data to become ambiguous and unreliable. * Unexpected and unusual sensor behavior patterns may arise during WSN deployment in unpredictable and hazardous environments, potentially changing the entire historical pattern of the sensed data. These characteristics render a completely secure WSN system almost impossible to establish, unlike its counterpart networks. The characteristics of WSN systems limit the available security options, including those similar to heavyweight classical security approaches such as spread spectrum, cryptography, and key management at either the device level or the overall network level. These options are demanding in terms of the resources required to protect the network. As existing security solutions for WSNs are insufficient due to these networks' unique characteristics, it is difficult to create lightweight and effective security mechanisms that can enable optimization of node resource usage while supporting network scalability and without compromising security, allow for a dynamic network topology with different possible configurations and node localization, and integrate heterogeneous hardware and software platforms for sensors to allow them to detect malfunctioning or faulty nodes.
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
## 1 Introduction
First, the technique entails making an upper envelope for the seismic data and computing a preliminary forward Radon transform in the time domain. Next, the second-forward Radon transform incorporates a priori information by computing the energy of each p trace in the preliminary transform. We can obtain the high-resolution Radon model by twice-forward Radon transform. To address the problem of lacking AVO, we modify the Radon function by incorporating the least-squares principle in the inversion Radon transform using polynomials to describe the AVO characteristics. The proposed method avoids calculating the time-variant kernel. Compared with CGRT, the proposed method has a high computation efficiency.
## 5 Results and Discussion
### Impact of Multiscale Pores  
Different from conventional reservoirs, the pores in the shale matrix are mainly nanoscales and microscales. If the effects of multiscale pore structure on gas flow capacity had not been considered, the mass flux for shale reservoirs will have been underestimated, especially under low reservoir pressure (\(<\)10 MPa) (Figure 7). This is due to the multiscale pores developed in shale reservoir. With the decrease of reservoir pressure, the proportion of the pores in the slip flow and Knudsen diffusion increase, resulting in an increase of the total mass flux. The apparent permeability is higher than the intrinsic permeability.  
The gas flow models for various flow regimes show that both slip flow and Knudsen diffusion are functions of pore size distribution, which means that pore size distribution has a significant influence on them. Pore size scales of various flow regimes are fixed for a giving reservoir pressure, and pore size distribution is determined by the pore fractal dimension. The mass flux in the slip flow and Knudsen diffusion was decreasing with the decreases in pore fractal dimensions (Figure 8). Because the frequency of pores with a certain pore size scale is exponential positive correlation with pore  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ Compressibility for permeability & 6.0 \(\times\) 10\({}^{-7}\) & Pa\({}^{-1}\) \\ Compressibility for porosity & 4.0 \(\times\) 10\({}^{-7}\) & Pa\({}^{-1}\) \\ Pore area fractal dimension & 1.8 & fraction \\ Langmuir pressure & 3.0 \(\times\) 10\({}^{6}\) & Pa \\ Langmuir volume & 2.5 & m\({}^{3}\)/t \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Relationships between mass flux in various flow regimes and reservoir pressure.  
fractal dimension (Equations (3)-(7)), which means that the larger the fractal dimension, the greater the number of pores in the slip flow and Knudson diffusion, and the larger the corresponding mass flux.
# Middle Rock Pillar Stability Criteria for a Bifurcated Small Clear-Distance Tunnel
## 1 Introduction
[58] established three-dimensional collapse mechanisms for tunnel roofs based on a modified Mohr-Coulomb criterion and analyzed how tensile strength reduction and geometric characteristics influence stability numbers and required supporting pressures. [PERSON] et al. [59] developed a combined rigid-block failure model for evaluating face stability in large cross-section tunnels excavated by the bench method, considering weak interlayers, excavation footage, and bench geometry to enhance stability assessment accuracy. These studies have focused primarily on the distribution of surrounding rock pressure, analysis of stability-influencing factors, and damage modes of small-clearance tunnels. However, scholars have not yet reached a consensus on the stability criteria of the MRP for small-clearance tunnels. Therefore, there is a strong motivation to develop a simple yet rigorous approach to determine the stability of the MRP for small-clearance tunnels. This study investigated the Xiamen Haicang Shugang evacuation channel underground interchange tunnels, focusing on ultrasmall clear-distance sections, with distances ranging from 1.03 to 5.0 m. To evaluate rock stability during the design phase, the SRM was used to determine the mechanical parameters for the limit state in small-spacing tunnels. Additionally, the SRM was combined with a detailed construction mechanics analysis, in which the arch bottom settlement and clearance convergence were used as independent variables to establish stability criteria. This method enables the assessment of various factors influencing the stability of the MRP. Extensive data analysis was conducted to explore the relationship between cross-sectional deformation in small clear-distancetunnels and the deformation and stress characteristics in different areas of the rock mass. On the basis of this analysis, stability criteria for the MRP were established, with a focus on displacement, stress, and plastic zone limits. These criteria provide a foundation for ensuring safe construction and guiding the implementation of reinforcement measures in tunnels.
## 2 Material and Methods
### Material Characterization  
Specific surface area and detailed porosity parameters (pore size distribution and pore volume) were determined using the Brunauer-Emmett-Teller (BET) and the Barrett-Joyner-Halenda plot methods. The nitrogen adsorption and desorption curves of FTWR were also obtained. Morphology was characterized by using a scanning electron microscope (SEM; S4800, Hitachi Corporation, Tokyo, Japan). An X-ray diffractometer (Thermo Fisher Scientific Co., Ltd., Waltham, MA, USA) was used to collect XRD data of RFTW with monochromatic Cu/K\(\alpha\) radiation (\(\lambda\) = 1.54056 A) at 36 kV and 20 mA in a 2 0 scanning range of 20-70\({}^{\circ}\). The types of functional groups in RFTW were analyzed by NEXUS870 Fourier Transform Infrared Spectrometer (FT-IR) (Nicolet 8700, Thermo Fisher Scientific, Waltham, USA). The scanning was done from 400 cm\({}^{-1}\) to 4000 cm\({}^{-1}\) at a scanning rate of 40 with the step size of 4 cm\({}^{-1}\) by adopting attenuated total reflectance. Gas chromatography-mass spectrometry (GC-MS) (Shimadzu Corporation, Tokyo, Japan) was used to analysis the components of samples. The capillary column used VF-5 MS (30 m \(\times\) 0.25 mm; 0.25 \(\mu\)m), the carrier gas was helium, and the flow rate was 0.8 mL/min. The analysis was performed in duplicate by injecting 1 \(\mu\)Li in splitless mode. The sampler was set to 280 \({}^{\circ}\)C, the transfer line was set to 280 \({}^{\circ}\)C, and the ion source was set to 300 \({}^{\circ}\)C. The column was initially set to 100 \({}^{\circ}\)C for 1 min, heated at 10 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C and 200 \({}^{\circ}\)C for 2 min, and finally reheated at 3.5 \({}^{\circ}\)C/min until 260 \({}^{\circ}\)C. The total analysis time was 40 min. The mass spectrum was collected every 0.5 s with a range of 32\(-\)380 \(m/z\). In this way, qualitative and quantitative analysis of RFTW was carried out.
## 7 Conclusions and Future Scope  
The research paper proposed a novel algorithm (CS-SR) that is based in D.M.S. for finding appropriate cloud service that meets user requirements. The proposed approach aims to lower down the total execution time in finding the service. The proposed approach CS-SR also reduced the unnecessary comparison using the filtration step. In addition, CS-SR used both the quantitative and qualitative parameters to find the result and turned out to be an efficient approach.  
The proposed approach finds the total fitness value by using qualitative and quantitative parameters. The research considers the feedback taken from an experienced user to calculate the fitness value for qualitative parameters. The user feedback can be biased sometimes, and in such cases, the result we obtain by using biased values will not provide an accurate result and can be considered the limitation of the proposed approach. Moreover, the proposed cloud service selection mechanism ensures energy conservation and facilitates reduced carbon emission at cloud data center levels, thus resulting in sustainable smart cities.  
The research work can be expanded by classifying the user requirements into essential and non-essential requirements [63; 64; 65]. The consideration of different parameters improves the accuracy level and improves the trust between the provider and service user. In addition, the research work can be extended to the optimal composition of service, which will further assist the user is not only in identifying a single service but the group of services.  
All authors contributed equally. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP, 2/25/42).This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Research Funding Program.  
Not applicable.  
The authors declare no conflict of interest.
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 4 Performance Assessment
### Percentage Error of the Success Probabilities  
As regards the cross-validation of the formulated model, the discrepancies between the simulated and the analytical success probabilities were quantified in terms of percentage error. This is reported in Tables 6 and 7, which show its mean value, as well as the 90 th and the 10 th percentiles. The reported results clearly highlight that the analytical model boasts a good overall accuracy, keeping the mean percentage error always below 6% and the 90 th percentile slightly above 10%.  
In particular, Configuration 1 outperforms, demonstrating a mean percentage error lower than 3.5%. Nonetheless, in general, the analytical model is more accurate when the first resource configuration is adopted, as also shown in the previous figures. This is mainly due to the fact that in Configuration 2 the average number of devices accessing RAOs is considerably lower. Further analysis could be conducted to demonstrate that the analytical model actually holds higher accuracy when the number of terminals accessing an RAO is relatively large.