esa-sceva/satcom-chunk-collection huggingface dataset:
# Ballasted Track Status Evaluation Based on Apparent Track Stiffness Index
## 4 Track Status Evaluation Based on the Track Stiffness Index  
### Track Status Evaluation Standard  
As described already, track irregularity management standards are proposed in the Railway Track Maintenance Guidelines of the Korea Rail Network Authority [1]. Among them, management standards for the standard deviation of vertical track irregularity are divided into three stages (see Table 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Management Stage** & **Limits** \\ \hline Construction value & Value required when a new track is constructed & N\({}_{\text{SD\_100m}}\)1\(\leq\) 1.0 \\ (CV) & & N\({}_{\text{SD\_20m}}\)2\(\leq\) 1.3 \\ \hline Target Value & Value required after maintenance work on a track & N\({}_{\text{SD\_10m}}\)1.3 \\ (TV) & (\(L<100\) m) & N\({}_{\text{SD\_20m}}\)1.7 \\ \hline Warning Value & Meaning of this stage: & N\({}_{\text{SD\_10m}}\)1.9 \\ (WV) & - To examine the cause of the defect and the associated characteristics & N\({}_{\text{SD\_10m}}\)2.6 \\ \hline \end{tabular}  
* N\({}_{\text{SD\_10m}}\) is the standard deviation of version for a 10 m chord over a 200 m long section.2 N\({}_{\text{SD\_20m}}\) is the standard deviation of version for a 20 m chord over a 200 m long section.  
\end{table}
Table 6: Track irregularity management standard.  
Figure 9: Track stiffness index (\(\alpha\) = 0.6) versus cumulative tamping length: (**a**) All; (**b**) Earthwork; (**c**) Bridge; (**d**) Tunnel.  
Track status can be classified based on the track stiffness index using the TQI standard presented in the Railway Track Maintenance Guidelines. For the TQI standard value, the limit values are presented only for the following three types: construction value, target value, and warning value. Most ballasted tracks typically in use for high-speed rails are considered to have TQIs between the target value and warning value. Therefore, the track status was divided into five levels for a deeper and more detailed classification of the section. Level 1 indicates significantly good status, with the score corresponding to the construction value. Level 2 indicates good status, with the score corresponding to the target value. This is the standard required after maintenance work. Level 3 indicates normal status, which allows for routine operations. Level 4 indicates bad status and is a level below the warning stage. This level requires continuous monitoring of the track status. Finally, Level 5 indicates significantly bad status of the track. It corresponds to the warning value. Therefore, Level 5 status of the track calls for immediate actions to be taken to restore the track status to the target level.  
In Section 3, it was reported that there was a significant relationship among the track stiffness index, TQI, and cumulative tamping length. Therefore, after deriving the track stiffness index value according to the TQI criteria, the track status evaluation standard based on the track stiffness index and cumulative tamping length is presented in Table 7.
## Numerical Analysis of the Influence of Empty Channels Design on Performance of Resin Flow  
in a Porous Plate  
**[PERSON] 1, [PERSON] 2,*,**[PERSON] 3,**[PERSON] 1,3,**[PERSON] 4,**[PERSON] 5,**[PERSON] 1,3,**R and [PERSON] 1,3**  
1 Graduate Program of Computational Modeling, Federal University of Rio Grande, Italia Avenue, km 8, Rio Grande 96201-900, Brazil; [EMAIL_ADDRESS] (G.M.C.M.); [PERSON]S] (L.A.I.); [EMAIL_ADDRESS] (J.A.S.); [EMAIL_ADDRESS] (E.D.G.S)  
2 Interdepartmental Center for Industrial Research on Advanced Applications in Mechanical Engineering and Materials Technology, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy  
3 Graduate Program of Ocean Engineering, Federal University of Rio Grande, Avenue, km 8, Rio Grande 96201-900, Brazil; [EMAIL_ADDRESS]  
4 Materials Engineering Graduate Program, University of Rio Grande do Sul, Bento Goncalves Av., 9500, Porto Alegre 91501-970, Brazil; [EMAIL_ADDRESS]  
5 Mechanical Engineering Graduate Program, University of Vale do Rio dos Sinos,  
Sao Leopoldo 93022-750, Brazil; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-347-697-4046  
Received: 19 March 2020; Accepted: 17 April 2020; Published: 11 June 2020  
**Featured Application: The influence of empty channel design on resin flow in a porous plate is studied. Constructal design is used for geometric investigation of I and T-shaped channels. The multiphase resin/air flow in the porous plate is solved numerically. T-shaped configurations with long branches lead to the best impregnation performance. Permanent air voids arise in the domain for very thin T-shaped channels.**  
This numerical study aims to investigate the influence of I and T-shaped empty channels' geometry on the filling time of resin in a rectangular porous enclosed mold, mimicking the main operating principle of a liquid resin infusion (LRI) process. Geometrical optimization was conducted with the constructal design (CD) and exhaustive search (ES) methods. The problem was subjected to two constraints (areas of porous mold and empty channels). In addition, the I and T-shaped channels were subjected to one and three degrees of freedom (DOF), respectively. Conservation equations of mass and momentum for modeling of resin/air mixture flow were numerically solved with the finite volume method (FVM). Interaction between the phases was considered with the volume of fluid method (VOF), and the effect of porous medium resistance in the resin flow was calculated with Darcy's law. For the studied conditions, the best T-shaped configuration resulted in a filling time nearly three times lower than that for optimal I-shaped geometry, showing that the complexity of the channels benefited the performance. Moreover, the best T-shaped configurations were achieved for long single and bifurcated branches, except for configurations with skinny channels, which saw the generation of permanent voids.  
**Keywords:** constructal design; resin flow; porous media; numerical simulation; filling time
## 4 Discussion of Results
### Regarding the Model Generation (Phase 1)
In contrast, the model generated with laser scanning clearly proved to be more accurate than the one generated from blueprints as it exhibited precise geometric similarity with the actual constructed building. When comparing the resulting model with others identified in the reviewed literature, the most notable 3D digital city environments include Helsinki, Finland [32; 33]; Cambridge, USA [34]; Digital Urban European Twin (Pizen, Czech Republic; Flanders, Belgium; Athens, Greece) [35]; Docklands, Dublin, Ireland [36]; and Singapore [37]. At the time of the literature review, all of these digital twins were characterized as 100% GIS platforms,  
lacking integration with BIM models. In this regard, while GIS entity attributes can be queried, access to building \"type parameters\" is not available. As a result, full access to the corresponding physical counterpart information is not achieved. This implies that the integrated GIS and BIM model offers improvements over existing models in several aspects, including the following: (1) The resulting model more accurately represents its real-world physical counterpart. (2) Obtaining a model with level of detail (LoD) 4 becomes a more streamlined process. (3) In GIS, by replacing mesh-type elements with BIM models, the model is lighter. (4) Access to all multiparametric information of the elements is provided. (5) Access to other platforms (and users) is facilitated via preconfigured links. (6) External users can update the model through external databases, such as _Excel_ spreadsheets, without requiring knowledge in GIS or BIM, making the model a more accessible and universal tool. (7) Life cycle management is enhanced through the document management of the 3D virtual library. As a result, dissemination of the built environment and its internal characteristics is improved.
# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 2 Materials and Methods
### Deep-Learning Algorithms for PAI Reconstruction  
The convolutional neural network EDSR [31] for super-resolution reconstruction was subjected to some modifications to enhance the efficiency and scalability of PAI reconstruction. Deep learning-based super-resolution reconstruction algorithms have been used to obtain high-resolution (HR) images from their low-resolution (LR) counterparts in various fields [38; 39]. The EDSR network, as a deep-learning model for image super-resolution reconstruction, employs techniques such as residual learning and dense connectivity by increasing the depth and the number of parameters of the network, which can efficiently improve the quality of image super-resolution reconstruction and accuracy. However, since the PAI reconstruction itself does not need to change the size of the output image, the existence of the upsampling layer increases the computational complexity and the number of parameters of the network, which may lead to the problem of model overfitting or unstable training. In addition, the upsampling layer may lead to blurring or distortion of the network in reconstructing the image, especially at the edge, and detail parts of the reconstruction may be poor, affecting the reconstruction of the image.  
To address this, an improved network EDSR-M was proposed by replacing the original upsampling layer with a convolutional layer, whose architecture is shown in Figure 2. The improved network still consists of a series of residual blocks, each of which contains multiple convolutional layers and activation functions inside, and sums up the input features with the output features through residual connections in order to efficiently learn the residual information of the image and increase the resolution of the image by employing appropriate convolutional operations in the final part of the network. Overall, the improved network structure improves the computational efficiency while maintaining the performance of the original network and avoids the problems that may be introduced by the traditional upsampling layer, such as artefacts and distortion. It also reduces the overfitting risk of the model, making the model more generalizable, robust, and better able to adapt to changes in different datasets and scenarios. Therefore, the improved network has significant advantages in improving model efficiency, reducing overfitting risk, and improving model interpretability.  
Figure 2: Proposed EDSR-M network architecture.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 3 Experimental Procedure
### Model Training and Optimization  
* Random Forest Classifier:  
The RFC model was configured with 300 decision trees and employed the'square root rule' for feature selection at each split. This setup balanced generalization performance and computational efficiency. The model was trained using parallel processing to maximize resource utilization.  
* Support Vector Classifier:  
An initial SVC model was trained with default parameters. To enhance performance, hyperparameter optimization was performed using GridSearchCV 1.3.0. The grid search focused on tuning the following parameters:  
1. C: regularization parameter (values: 1, 10, 45, 47, 49, 50, 51, 55, 100, 300, 500);
2. Gamma: kernel coefficient (values: 0.01, 0.05, 0.1, 0.5, 1, 5);
3. Kernel: radial basis function kernel ('rbf').  
A 10-fold cross-validation strategy was employed to ensure robust performance across different data splits. The best parameter combination was used to train the final model, which was evaluated on the test dataset.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT on the Ground
#### 3.2.6 Extensions with Applications in Agriculture  
Internet of Trees is a recent extension of the IoT that can be used to monitor the health of trees. Trees and plants can be outfitted with sensors, which allows farmers to decrease over-watering and improve the quality of the plants that are being produced. The authors of [176] propose a system that works with electromagnetic sensors that use spectral dispersion to measure moisture content. The sensors are linked together to better understand how a group of trees may need to be watered most effectively. The network of trees is combined through a representative network system based on a long-range (LoRa) protocol. The system is proven to be low-cost as energy is collected through solar and can reduce the amount of water used in the agriculture of trees.  
Security challenges and mechanisms of this extension have not been adequately studied in the literature.  
Internet of Agro Things (IoAT) has been studied in some recent research works. Crop production and yield is increasingly important in areas that have limited land for production. The ability to monitor crops and understand what can be done to increase output can be enhanced with networks of sensors in the IoAT. In [177] the authors propose a CNN that is able to perform analysis on crop images. The sensor nodes collect images of plants to send to the CNN and are solar powered, reducing energy consumption. When tested in a 3-month trial, it was shown to be 99% accurate and was able to sustain harsh weather.  
The authors are not aware of any reported research focusing on the security of this extension.
# A 0.3 V, Rail-to-Rail, Ultralow-Power, Non-Tailed, Body-Driven, Sub-Threshold Amplifier
## 3 Small Signal Analysis
### DC-Gain and Common Mode Rejection  
The replica bias stage contributes to enhance the common mode rejection ratio (CMRR) of the input stage that would otherwise be one (same gain for differential and common mode signals). Denoting with \(V_{id}\) and \(V_{01d}\) the input and the output differential voltage of the input stage, respectively, the differential gain can be easily derived referring to the circuit in Figure 1a:  
\[A_{d1}=\frac{V_{01d}}{V_{id}}=\frac{V_{\alpha 1p}-V_{\alpha 1m}}{V_{ip}-V_{im}}= \frac{G_{1}}{G_{\alpha 1}}=\frac{g_{mb_{1}}+g_{mb_{3}}}{g_{ds_{1}}+g_{ds_{3}}} \tag{2}\]  
where \(G_{1}\) and \(G_{\alpha 1}\) are the transconductance and the output conductance of the input stage respectively, \(g_{mb}\), \(g_{m}\) and \(g_{ds}\) denote the body transconductance, the gate transconductance and the output conductance of the different MOS transistors, as usual, and the subscripts 1 and 3 refer to PMOS and NMOS devices in Figure 1a, respectively.  
Denoting with \(V_{ic}=(V_{ip}+V_{im})/2\) and \(V_{oc}=(V_{\alpha 1p}+V_{\alpha 1m})/2\) the input and output common mode voltage of the input stage, respectively, the common mode gain, with the effect of the replica bias control loop, can be derived referring to the common mode equivalent circuit of the input stage shown in Figure 3:  
\[A_{c1}=\frac{V_{\alpha 1c}}{V_{ic}}=-\frac{g_{m_{1}}g_{mb_{3}}+\big{(}g_{mb_{1} }+g_{mb_{3}}\big{)}g_{ds_{3}}}{\big{(}g_{m_{3}}+g_{ds_{3}}\big{)}\big{(}g_{m_{ 1}}+g_{ds_{3}}+g_{ds_{1}}\big{)}}\approx-\frac{g_{mb_{1}}}{g_{m_{1}}} \tag{3}\]  
where the approximation holds with the usual assumtpions \(g_{m}>>g_{mb}\) and \(g_{m}>>g_{ds}\). The resulting CMRR can then be calculated as  
\[CMRR_{1}=\frac{A_{d1}}{A_{c1}}=\frac{g_{mb_{1}}+g_{mb_{3}}}{g_{mb_{1}}}\frac{ g_{m_{1}}}{g_{ds_{1}}+g_{ds_{3}}} \tag{4}\]  
and is proportional to the intrinsic gain \(g_{m}/g_{ds}\) of the devices.  
The CMRR is further improved by the second stage, since, with the dual path approach, the common mode components at the output of the first stage arrive to the output with opposite phases. The analysis of the circuit in Figure 1b provides:  
\[V_{o}=\left(g_{m_{8}}\frac{g_{m_{8}}}{g_{m_{7}}}V_{\alpha 1p}-g_{m_{9}}V_{ \alpha 1n}\right)\frac{1}{G_{o}} \tag{5}\]  
where \(V_{\alpha 1p}\) and \(V_{\alpha 1m}\) can be expressed as follows:  
\[V_{\alpha 1p,m}=\pm A_{d_{1}}\frac{V_{id}}{2}+A_{c_{1}}V_{ic} \tag{6}\]  
and  
\[G_{o}=g_{ds_{8}}+g_{ds_{9}} \tag{7}\]  
is the output conductance of the second stage.  
The overall CMRR is therefore given by  
\[CMRR=CMRR_{1}\cdot CMRR_{2} \tag{8}\]  
with,  
\[CMRR_{2}=\frac{1}{2}\frac{g_{m_{S}}g_{m_{S}}+g_{m_{\gamma}}g_{m_{\eta}}}{g_{m_{S} }g_{m_{\eta}}-g_{m_{\gamma}}g_{m_{\eta}}} \tag{9}\]  
It is clear from (9) that the CMRR can become infinite by choosing:  
\[g_{m_{S}}g_{m_{\eta}}=g_{m_{\gamma}}g_{m_{\eta}} \tag{10}\]
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### Influence of Various Factors on Mechanical Properties  
#### 3.2.1 Effect of Water Immersion Age on Mechanical Properties  
As shown in Figure 6, the peak strength of the specimens with the same mix ratio increased with the extension of curing age, reaching a maximum at 40 days. With extended curing age, the peak strength \(q_{\text{u}}\) improved correspondingly. For the R20C10, R20C15, R20C20, R40C20, and R60C20 specimens, \(q_{\text{u}}\) values were 811.8 kPa, 1181 kPa, 1407 kPa, 1149.8 kPa, and 844.2 kPa at 1 day, respectively. These values increased after 5 days and 20 days of curing and peak at 40 days, showing 1.45-, 1.05-, 2.2-, 2.16-, and 3.25-fold increases compared to the 1-day values.  
Discussion reveals that the addition of a stabilizer consumes internal moisture through hydration reactions. Water immersion curing effectively replenished this moisture, further facilitating hydration reactions to produce cementitious materials that agglomerate and bind red clay particles with recycled aggregate, forming stable clusters and increasing \(q_{\text{u}}\). During shorter curing periods (1 and 5 days), reactions remained incomplete. After 20 days, hydration reactions proceeded gradually, producing a denser internal structure. Consequently, UCS peaked at 40 days, indicating that the synergistic effect of recycled aggregate and soft soil stabilizer under full immersion conditions significantly improved the stability of high-water-content soft soil foundations, providing valuable references for engineering practices.
## 6 Discussion and Conclusions
### Practical Implications  
Teachers are challenged as they are no longer the only source of delivering knowledge to students. There are hundreds, if not thousands, of sources on YouTube. Such a challenge is not only for teachers but also educational institutes as well as libraries. Hence, the educational institutes are challenged to produce YouTube channels that deliver educational materials. Many educational institutes have taken major steps towards such a goal, while other educational institutes are delaying the inevitable or burying their heads in the sand. The quality of the presentation, as shown previously, is very important.  
Teachers are challenged in their delivery of knowledge methods. The number of views is a reflection of the teacher's performance as is the like/dislike button; therefore, teachers must prepare and plan their lessons. Moreover, the credibility of the source is very important, as shown previously, which influences the information usefulness. Furthermore, the credentials of the presenter are very important.  
The positive influence of Information usefulness (IU) on information adoption (IA) is very important, as shown previously; hence, the material delivered to students should reflect this.  
Students are adopting information for the purpose of academic achievement, which is evident from previous research; hence, the goal of any YouTube film should be to be clear and to the point.  
The student's age, gender, education level, and previous experience must be considered when presenting education materials on YouTube. In live lectures, a teacher can assess the audience and calibrate their presentation to the age, gender, education level, and previous experience, while such an option is not available on YouTube; therefore, the teacher must accommodate a rainbow of options. As such, meticulous preparation and planning is needed so that students with less experience can be accommodated as can older generations and females.  
The proposed model can be used by teachers, educational institutes, researchers, and practitioners to create YouTube content that meets the needs of students and increases the sharing intent.  
As such, teachers should be equipped with better cameras, better sound systems, a smart pad/phone, internet service provider and electronic pen and board. Long gone are the days of a regular pen and board. All this equipment will entail financial burdens on the teacher/institute, further added to by the demands from the teacher/institute for smart gadgets and the IoT. In addition, there is a need for training to deal with such technologies. In fact, the classroom environment will need to change to accommodate such a demand for YouTube as a learning tool.
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 3 System Model  
As depicted in Figure 1, the model illustrates a multi-hop network of FANET composed of multiple flying nodes and a ground base station. In this network, a specific node is responsible for sending messages, acting as the source node, while the remaining nodes act as relay nodes to assist in message forwarding and jointly assist in the transmission of information. These flying nodes can communicate directly with each other as well as establish communication links with the ground base station. The system model of the FANET is set as follows: where the network architecture can be represented by a dynamic graph \(G=(S,L)\), and where \(S\) and \(L\), respectively, represent the sets of vertices and edges in the graph \(G\). Each flying node is represented as \(L_{ij}\) (\(i=1,2,\ldots,n\)), corresponding to each vertex in the graph \(G\). \(L_{ij}\) represents the connection between nodes \(S_{i}\) and \(S_{j}\), with the spatial position of node \(S_{i}\) denoted as \((x_{i},y_{i},z_{i})\). Each node has a communication radius of \(R\), and they all have the same initial energy.