esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Accuracy Verification of the Analytical Solution
### Discussion of Analytical Solution in FSPR  
From the parameter selection of each group in Table 1, the smaller the dislocation angle \(\beta\) value and the larger the frozen tube radius \(R_{0}\), the lower the temperature of the freezing tube area. In the FSPR method adopted in the Gongbei Tunnel, the approximate circle radius of the freezing tube ring is about 9 m, and the diameter of the jacking pipe is 1.62 m. The distance between adjacent jacking pipes is about 0.3 m, and the dislocation angle is about 2\({}^{\circ}\). Taking the freezing parameters \(R_{1}\) = 7.9 m, \(R_{2}\) = 9 m, \(R_{3}\) = 10 m, and \(\xi_{1}/\xi_{2}\) = 1.1 at the end of the freezing stage, respectively, calculate the temperature difference of the three sections of the frozen soil curtain at the axial surface \(R_{2}\) of the freezing tube, as shown in Table 2:  
At the end of freezing, the temperature difference between the main section and Section 1 is \(\Delta\)T1 = \(-1.49\)\({}^{\circ}\)C, while the temperature difference between sections 1 and 2 is \(\Delta\)T2 = \(-12.43\)\({}^{\circ}\)C. Section 2 is located at the axis of symmetry of the steel jacking pipe, which can provide effective water sealing capacity, therefore, only Section 1 should be considered. The temperature for Section 1 can drop to a lower temperature value at the end, which is very close to the temperature of the freezing tubes in the main section. Notably, frozen soils can be formed with sufficient strength and water sealing performance between the jacking pipes. Then, an effective frozen curtain is formed as a whole, which provides safe and reliable support for tunnel excavation.  
Finally, the steady-state temperature field distribution cloud map of the overall model in FSPR is calculated and drawn in line with the analytical solution formula, (Figure 14). The analytical solution obtained in this article has sufficient accuracy to meet the calculation needs of different positions (pole radius \(r\)) in the FSPR model. The temperature field cloud map, drawn according to the calculation results, can intuitively reveal the overall temperature distribution and thickness of the frozen soil curtain, and realize visual processing. It is also proven that when the temperature field tends to steady-state in the latter stage of freezing, the shapes of the inner and outer boundaries of the frozen soil in this model can be considered to be a circular ring, and the wavy frozen soil boundary can be ignored.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Main Section** & **Section 1** & **Section 2** \\ \hline Temperature/\({}^{\circ}\)C & \(-30\) & \(-28.51\) & \(-16.08\) \\ \(\Delta T_{1}/^{\circ}\)C & \(-1.49\) & & — \\ \(\Delta T_{2}/^{\circ}\)C & — & & \(-12.43\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temperature differences of each section at the end of freezing.
###### Abstract  
Evaluating the implant site immediately after implant removal is crucial for assessing its condition and ensuring morphological stability. Immediate reconstruction at the time of implant removal has been proposed as a strategy to preserve alveolar ridge width. This study aims to evaluate whether immediate alveolar bone reconstruction at the time of implant removal provides comparable or superior dimensional stability of the alveolar ridge compared to staged reconstruction approaches. The null hypothesis of this study is that there is no significant difference in alveolar bone dimensions between immediate and staged reconstructions following implant removal. This retrospective study included seven participants, consisting of six males and one female. The participants were categorized into three groups based on the treatment approach following implant removal. In Group 1, no bone grafting was performed after implant removal. In Group 2, bone grafting was conducted following implant removal, with an adequate healing period before implant placement. In Group 3, bone grafting was performed simultaneously with implant removal. Cone-beam computed tomography (CBCT) imaging was conducted before implant removal (T0), after implant removal or bone grafting (T1), and after implant placement (T2). All removed implants were successfully replaced with new ones, regardless of bone grafting. In terms of alveolar ridge width at 1 mm below the crest, Group 1 exhibited the greatest reduction (AT1 - T0 = -5.1 \(\pm\) 3.7 mm), while Group 2 showed a mild increase (+1.1 \(\pm\) 2.6 mm), and Group 3 had a moderate decrease (\(-\)1.3 \(\pm\) 1.0 mm). This suggests that delayed bone grafting can better preserve or enhance bone volume during healing. A reduction in buccal ridge height between T1 and T0 (AT1 - T0) was observed, particularly in Group 1. In contrast, an increase in buccal ridge height was most pronounced in Group 2. Although immediate reconstruction (Group 3) did not result in statistically significant gains, it achieved successful implant placement without complications and reduced the total treatment duration, which might be beneficial from a clinical efficiency and patient satisfaction standpoint. Therefore, staged bone grafting (Group 2) appears to offer greater dimensional stability, particularly in maintaining ridge height, whereas immediate reconstruction (Group 3) remains a clinically viable alternative for stable healing in select cases, especially when shorter treatment timelines are prioritized.  
d
# 0.5-V High-Order Universal Filter for Bio-Signal Processing Applications
## 2 Proposed Circuit
### Proposed High-Order Universal Filter
Variants of the filter functions LPF, HPF, BPF, BSP, and APF can be obtained by appropriate use of the input signals as shown in Table 2.  
The natural frequency (\(\omega_{o}\)) and the quality factor (\(Q\)) can be given respectively by  
\[\omega_{o}=\sqrt{\frac{g_{\text{m1}}g_{\text{m2}}}{C_{1}C_{2}}} \tag{20}\]  
\[Q=\sqrt{\frac{C_{1}g_{\text{m2}}}{C_{2}g_{m1}}} \tag{21}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Filtering Function**} & \multicolumn{1}{c}{**Input**} & \multicolumn{1}{c}{**Transfer Function**} \\ \hline \multirow{2}{*}{HPF} & Non-inverting & \(V_{in31}=V_{in}\) & \(H(s)=\frac{s^{3}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in32}=V_{in}\) & \(H(s)=-\frac{s^{3}}{D(s)}\) \\ \hline \multirow{2}{*}{BPF} & Non-inverting & \(V_{in21}=V_{in}\) & \(H(s)=\frac{s\frac{s_{\text{m1}}}{C_{1}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in22}=V_{in}\) & \(H(s)=-\frac{s\frac{s_{\text{m1}}}{C_{1}}}{D(s)}\) \\ \hline \multirow{2}{*}{LPF} & Non-inverting & \(V_{in11}=V_{in}\) & \(H(s)=\frac{\frac{s_{\text{m1}}g_{\text{m2}}}{C_{1}C_{2}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in12}=V_{in}\) & \(H(s)=-\frac{\frac{s_{\text{m1}}g_{\text{m2}}}{C_{1}C_{2}}}{D(s)}\) \\ \hline \multirow{2}{*}{BSF} & Non-inverting & \(V_{in31}=V_{in11}=V_{in}\) & \(H(s)=\frac{s^{2}+\frac{s_{\text{m1}}g_{\text{m2}}}{C_{1}C_{2}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in32}=V_{in12}=V_{in}\) & \(H(s)=-\frac{s^{2}-\frac{s_{\text{m1}}g_{\text{m2}}}{C_{1}C_{2}}}{D(s)}\) \\ \hline \multirow{2}{*}{APF} & Non-inverting & \(V_{in31}=V_{in22}=V_{in11}=V_{in}\) & \(H(s)=\frac{s^{2}-s\frac{s_{\text{m2}}}{C_{2}}+\frac{s_{\text{m1}}g_{\text{m2}} }{C_{1}C_{2}}}{D(s)}\) \\ \cline{2-4}  & Inverting & \(V_{in32}=V_{in21}=V_{in12}=V_{in}\) & \(H(s)=\frac{-s^{2}+\frac{s_{\text{m2}}g_{\text{m2}}}{C_{1}C_{2}}}{D(s)}\) \\ \hline \hline \end{tabular} Note: \(D(s)=s^{2}+s\frac{g_{\text{m1}}}{C_{1}}+\frac{s_{\text{m1}}g_{\text{m2}}}{C_{ 1}C_{2}}\), \(H(s)=\frac{V_{in31}}{V_{in}}\).  
\end{table} 
Table 2: Obtaining variant filtering functions of the second-order universal filter. Figure 7: Second-order universal filter. The third-order universal filter that employs four OTAs and three grounded capacitors is shown in Figure 8. The output voltage can be expressed by  
\[V_{out(3 rd)}=\frac{ \begin{cases}s^{3}\big{(}V_{in41}-V_{in42}\big{)}+s^{2}\big{(} \frac{s_{m2}}{\text{C}_{2}}\big{)}\big{(}V_{in31}-V_{in32}\big{)}\\ \qquad+s\Big{(}\frac{s_{m1}s_{m2}}{\text{C}_{1}\text{C}_{2}}\big{)}\big{(}V_{ in21}-V_{in22}\big{)}\\ \qquad+\Big{(}\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}\text{C}_{3}} \big{)}\big{(}V_{in11}-V_{in12}\big{)}\end{cases} }{s^{3}+s^{2}\frac{s_{m2}}{ \text{C}_{2}}+s\frac{s_{m1}s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}}+\frac{s_{m1 }s_{m2}s_{m3}}{\text{C}_{1}\text{C}_{2}\text{C}_{3}}} \tag{22}\]  
Variants of the LPF, HPF, BPF, and APF filter functions can be obtained by appropriate use of the input signals as shown in Table 3.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Observability of Parameters
#### 4.4.2 Observability of Parameters in SEE Tests  
Contrary to TID tests, SEE test measurements need to be performed in-situ. This may lead to significant limitations due to facility mechanical constraints, such as length and/or number and type of available cable connections to monitor electrical and functional parameters (this limitation was met during each SEE test campaign of the PoL, but was particularly cumbersome during test campaigns at CHARM facility, see [6; 24; 25]).
## 3 Case Studies
### Rocker Arm  
The first case study considered was the redesign of the rocker arm, depicted in the following figure (Figure 2), mounted on the race car developed for the Formula SAE by the Unical Racing Team of the University of Calabria. In particular, Figure 2 shows the assembly of the right rear rocker, also known as bell crank or linkage, that is located low in the chassis and connected with the upper side of the wheel, where the upper suspension arms meet the upright. The rocker also mounts an antiroll bar. The function of this component is to translate the pull rod motion into the rotary force and the up/down motion on the suspension system.  
The following figure (Figure 3) shows the geometry of the rocker designed according to a traditional method. This component was made in Ergal and worked with conventional machine tools. In particular, it was fabricated by means of a 5-axis machining center Yamazaki Mazak, VARIAXIS j-600/5X. However, this geometry could also be fabricated with a 3-axis machine.  
As abovementioned, the final design was achieved by means of an iterative trial and error approach based on the adoption of CAD systems for the modeling and FEM tools to assess and verify the geometry. In particular, as depicted in Figure 4a, the boundary conditions, adopted both for the FEM simulation and the optimization studies, were defined taking into consideration the two most critical cases: acceleration in corners and acceleration in straight lines. In the first case, there is a distribution of the maximum load (2000 N) between the shock absorber (1300 N) and the antiroll bar's tie rod (700 N). In the second case, the antiroll bar is not subjected to stresses, therefore, the 2000 N load is discharged only on the spring-shock absorber group.  
Figure 3: Rocker designed through a traditional design approach oriented to conventional manufacturing technology.  
Figure 2: Assembly view of the right rear rocker.  
As depicted in Figure 4b, the component features a weight of 226 g, the maximum displacement of 0.52 mm, and a minimum factor of safety (FoS), based on the yield strength stress values, of 2.4. As abovementioned, the redesign of this component, by means of the adoption of TO and GD tools, was carried out with the aim to improve its performance by improving its stiffness and reducing its weight.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 4 Results  
In this section, we showcase the results of employing advanced deep learning techniques, specifically UNet, Linknet, Attention UNet, UNet 3+, and TransUNet architectures, for 3D projection reconstruction in 2D lung infection diagnosis. Our focus lies on visualizing the intricate lung structures affected by COVID-19 through segmentation experiments conducted on CT-scans. To assess the effectiveness of these architectures, we utilize various evaluation metrics including specificity, sensitivity F1 score, accuracy dice score, and IoU score. Additionally, we analyze the training process by comparing the loss graphs of UNet, Linknet, Attention UNet, UNet 3+, and TransUNet to gain further insights into their respective convergence behaviors.
## 3 Coordinate Transformation of Structural Plane Information
### Image Preprocessing and Structural Feature Extraction  
To automate the extraction of coordinate and structural plane information from tunnel cross-section drawings, a color-based recognition procedure was developed. As illustrated in Figure 5a, which is a simulated schematic based on actual tunnel design drawings created using AutoCAD 2017 software and exported as a PNG file, each cross-section diagram includes a yellow marker representing the origin, a green segment indicating the X-axis direction with a known length, and structural planes displayed in distinct RGB values to enhance recognition and facilitate geometric sampling:  
* A yellow marker representing the coordinate origin;
* A green segment indicating the X-axis direction, with known physical length;
* Structural planes displayed in distinct RGB values for easy identification.  
Using Python and the OpenCV library, the coordinate origin is automatically detected based on its yellow color, and the physical-to-pixel ratio is calculated from the known length of the green segment. This conversion factor is then used to map pixel coordinates into real-world spatial coordinates.  
According to the structural layout, two horizontal structural planes (Plane 1 and Plane 3) and two vertical ones (Plane 2 and Plane 4) were selected for this study. Their boundaries were identified and extracted, as shown in Figure 5b.
## 4 Mathematical Analysis of Handover Delay and Packet Loss of CoMP
### Analysis of Handover Delay
\(\mathrm{t}_{\mathrm{RR}}\) is the time delay caused by executing a return routability procedure. For MIPv6, \(\mathrm{t}_{\mathrm{BU}}\) is equal to 2 (\(\mathrm{t}_{\mathrm{CN\_B,\ HA}}+\mathrm{t}_{\mathrm{CN\_B,\ CN}}\)), and \(\mathrm{t}_{\mathrm{RR}}\) is equal to 2 (\(\mathrm{t}_{\mathrm{CN\_B,\ CN}}+\mathrm{t}_{\mathrm{CN\_B,\ HA}}+\mathrm{t}_{ \mathrm{HA,\ CN}}\)). MIPv6 uses a bi-directional tunnel between the HA and CN_B. Because HMIPv6 is only used for local mobility management, a BU for either the HA or CN, _i.e._, CN_A, is not necessary. However, instead of HA/CN, it requires a BU for the mobility anchor point (MAP), and thus, binding update delay, _i.e._, \(\mathrm{t}_{\mathrm{BU}}\) incurs when sending signaling messages back and forth between CN_B and the MAP. It creates a handover delay of 2\(\mathrm{cn\_B,\ MAP}\). In the case of CoMP, the handover latency is composed of \(\mathrm{t}_{\mathrm{L2}}\), \(\mathrm{t}_{\mathrm{MD}}\), \(\mathrm{t}_{\mathrm{AC}}\), and \(\mathrm{t}_{\mathrm{BU}}\). Here, \(\mathrm{t}_{\mathrm{BU}}\) represents a binding update signaling message delay, _i.e._, a PUT binding update request message and an ACK binding update response message. Table 3 shows a summary of total handover delay for MIPv6, HMIPv6, and CoMP. Figure 13: Handover delay timeline of CoMP. \begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Total Handover Latency** \\ \hline \(D_{MNP^{\circ}6}\) & \(\mathrm{t}_{\mathrm{L2}}\)+\(\mathrm{t}_{\mathrm{MD}}\)+\(\mathrm{t}_{\mathrm{AC}}\)+\(4(\mathrm{t}_{\mathrm{CN\_B,\ BA}}+\mathrm{t}_{\mathrm{CN\_B,\ CN}})\)+\(2\mathrm{t}_{\mathrm{HA,\ CN}}\) \\ \(D_{HMIDv6}\) & \(\mathrm{t}_{\mathrm{L2}}\)+\(\mathrm{t}_{\mathrm{MD}}\)+\(\mathrm{t}_{\
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit
#### 4.4.4 Reliability and Security  
Partly due to the implementation of network slicing, 5G is regarded as a reliable communication method and is generally secure. Network slicing is a paradigm that enables the shared use of the same infrastructure to deliver differentiated 5G services. This differentiation can be observed in terms of functionality (e.g., mobility, security, and control) and performance (e.g., latency, throughput, error rate, reliability, and availability) [108]. This ensures that critical time-sensitive communication is isolated from other traffic, protecting privacy and improving reliability and accuracy.  
Figure 12: Time synchronization message transportation in 5G-TSN network.  
But, like PTP, there are some attacks targeting the time signal. Much of the literature provides corresponding solutions.  
There are other concerns in terms of time synchronization in power grids with 5G technology [109], such as resilience, network congestion, and signal interference. Accurate synchronization could be affected by natural disasters and severe weather. Any downtime in the 5G network could impact real-time grid operations.
## 2 Literature Review
However, in many parts of the world, the process of SVC development is not so straightforward. In developed countries, the strong dominance of distributors often hampers the orchestration of value chains since a sustainable value chain requires different stakeholders to cooperate and exchange information [52]. In developing countries, smallholder farmers are often the weakest link in the chains as they tend to continue operating in a traditional system [53]. Farmers often have limited resources which lock them in low productivity, inconsistent quality, limited education, and restricted access to market information. These hinder the farmers from meeting the demand of high-value markets. Not only that, those barriers hold back their participation in terms of value chain governance and value addition. A combination of all these factors caused smallholder farmers to have \"limited power, dependent relationships, and are marginalized from more profitable markets\" [53], creating a form of productive exclusion. This literature review highlights that there are different challenges in the implementation of sustainable practices in agriculture. These challenges vary from high audit and accreditation costs, shrinking cropland and complex land tenure regimes, slow return and lack of financial support to productive exclusion. However, these challenges are mostly specific to the individual approaches and might not necessarily cover the challenges affecting the integration of SCP practices by agricultural micro-, small-, and medium-sized enterprises (MSMEs). This article aims to further contribute to the knowledge relating to factors that affect the implementation of sustainable agricultural practices by smallholder farmers and MSMEs by gaining insights into the challenges and opportunities surrounding the integration of SCP practices in the horticulture sector. Findings will draw upon the learning of MSMEs at the Lake Navasha Basin, Kenya, that were beneficiaries and non-beneficiaries of the GOALAN project. By looking into several showcases, this article expects to identify common hindrances influencing the integration of SCP practices in the horticultural sector and offer practical recommendations for the facilitation and uptake of SCP practices by farmers and MSMEs.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques  
Advances in digital image processing algorithms have been equally crucial for automated pavement distress detection. The field has progressed from conventional image processing to machine learning and deep learning approaches, representing a shift from simple segmentation to advanced intelligent recognition systems.
###### Abstract  
Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.  
h _Article_
## 2 Material and Methods
### Data Preparation  
#### 2.2.1 Landslide Inventory Map  
The initial step of LSM is to produce an accurate and reliable inventory map, which is always obtained by field survey, image interpretation from remote sensing data and historical landslide records. In this work, the landslide inventory map of Anyuan County with 315 landslide locations provided by the Department of Land and Resources of the Jiangxi Province and the Jiangxi Meteorological Bureau. In this map, the smallest and largest landslides are 2.4 m\({}^{2}\) and 7,840 m\({}^{2}\), respectively. The distribution and impact of landslides with different sizes are greatly different. For instance, more than two-thirds of the total landslides are small-scale (\(<\)200 m\({}^{2}\)) landslides, which have influenced 2352 people and cause the economic loss of 0.1 million US dollars, followed by medium-scale (200-1000 m\({}^{2}\)) and large-scale (\(>\)1000 m\({}^{2}\)) landslides, which have influenced 479 and 55 people and caused the economic loss of 0.3 and 1 million US dollars, respectively. Meanwhile, heavy rainfall is another key factor for landslide occurrence.
# Numerical Simulation of Micro-Bubbles Dispersion by Surface Waves
## 4 Results and Discussion  
DNS were performed for the surface wave length \(\lambda\) = 15 cm and wave slope \(ka\) = 0.1 and 0.2 (amplitude \(a\approx\) 0.25 cm and 0.5 cm). For the chosen length, the wave celerity computed from the linear dispersion relation for surface gravity waves [1] equals \(c\approx\) 49 cm/s, and the Reynolds number, Equation (2), Re \(\approx\) 73019. Both wind-driven case (with \(q=0.05c\) and finite surface drift velocity, \(U_{d}\), Equation (21)) and zero surface-stress case, \(U_{d}=0\), were considered. Bubble diameter, \(d\), was varied from 200 to 400 microns, the total number of bubbles in each simulation was maintained constant (up to \(N_{d}=10^{6}\) for the smallest, \(d\) = 200 \(\upmu\)m, bubble-size cases) corresponding to a mean (reference) void fraction (concentration, \(C_{0}\)) of about 5 \(\times\) 10\({}^{-5}\).