esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Unipolar Pulser Generator Analysis
#### 2.6.1 Impedance Controlled Routing  
Stack layer design is one of the most important and effective methods in controlling the impedance of printed circuit lines in a PCB's design. Figure 7 depicts the impedance and propagation delay with two different types of circuit lines, which are narrow microstrips (\(1\leq\varepsilon_{r}\leq 15\) and \(0.1<w/h<2.0\)) and narrow striplines (\(w/h<0.35\) and \(t/h<0.25\)).  
Microstrip layout impedance and propagation delay can be expressed as  
\[Z_{0}=\frac{87}{\sqrt{\varepsilon_{r}+1.41}}ln\left(\frac{5.98h}{0.8w+t}\right) \tag{1}\]  
Figure 6: Characteristics of the excitation pulser for the transducer.  
Figure 7: Characteristic impedance and propagation delay of a printed circuit board (PCB) trace.  
\[T_{PD}=85*\sqrt{0.475\epsilon_{r}+0.67} \tag{2}\]  
Stripline layout impedance and propagation delay ca be expressed as  
\[Z_{0}=\frac{60}{\sqrt{\varepsilon_{r}}}ln\Bigg{(}\frac{1.9h}{0.8w+t}\Bigg{)} \tag{3}\]  
\[T_{PD}=85*\sqrt{\varepsilon_{r}} \tag{4}\]  
Another method to reduce the reflection of signals on the transmission line uses termination techniques. There are two broad kinds: series termination and parallel termination. Thevenin termination is one of the parallel terminations, shown in Figure 8. The resistance formula of a Thevenin termination is R1 + \(Z_{0}\) = R2. Proper termination can reduce overshoots, undershoots and ringing, as well as the amplitude of crosstalk [38].  
To analyze the design, a common rule of thumb is the one-third rise time rule, which states that if the route is more than one third of a rise time long, reflections can occur.  
The one-third rise time rule states that the speed at which electrical energy can travel along a route is known as the propagation velocity and can be defined as  
\[V_{p}=\frac{C}{\sqrt{\varepsilon_{r}}} \tag{5}\]  
where \(V_{p}\) is the propagation velocity, \(C\) is the speed of light (299.792458 mm/ns) and \(\varepsilon_{r}\) is the dielectric constant.  
Applying the one-third rise time rule, the transmission line effects will begin when  
\[L_{R}\geq\frac{T_{R}}{3}\times V_{p} \tag{6}\]  
where \(L_{R}\) is the length of a route and \(T_{R}\) is the signal Rise time.  
Assume a simple wire require the following calculation:  
\[L_{max}\leq\frac{T_{R}}{2T_{DP}}\text{ or }delay\text{ }_{max}\leq\frac{T_{R}}{2} \tag{7}\]  
where \(T_{R}\) is the signal rise time, \(T_{DP}\) is the delay per unit of length, \(L_{max}\) is the length of the wire and \(delay_{max}\) is the wire delay, which is equal to \(T_{DP}*L_{max}\).  
The PCB design usually applies two rules to reduce the noise of the circuit board: the 20H rule and the 3W rule. Applying the 20H and 3W rules can reduce the circuit field's radiation by 70%. The 3W rule represents the approximate 70% flux boundary at logic currents [39]. In this design, to reduce the impact of crosstalk noise on the pulse signals from the FPGA with the STHV800, we applied the 3W rule. Table 2 describes the signal connection between the XC6 XLX9 and the STHV800.  
Figure 8: A Thevenin termination.
## 4 Results and Discussion
### VCCD Damage Curve Results
In contrast, the 5% WCO sample (Figure 15) shows a limited damage range and irregular behavior, suggesting poorer fatigue resistance and possible instability at higher concentrations. This may be due to WCO's role in enhancing flexibility rather than rigidity, allowing for energy  
Figure 15: VECD damage curve from amplitude sweep for modified samples (5% WCO). dissipation without effectively reinforcing the bitumen against micro-damage. Thus, WCO-modified bitumen may be more suitable for applications requiring moderate durability and flexibility rather than extreme resistance to repetitive stress. In summary, the VECD damage curve analysis from Figures 7-15 suggests that SBS and AB are the most effective additives for enhancing bitumen's resistance to cumulative damage. SBS at a 5% concentration (Figure 9) and AB at 6% (Figure 12) exhibit the most gradual damage curves, reflecting superior performance in resisting micro-damage. This increased resistance is advantageous for high-stress applications, such as pavements in heavily trafficked areas, as it extends the operational life of the material. While WCO modifications provide flexibility and moderate durability benefits, their effectiveness in resisting cumulative damage is limited compared to SBS and AB. Among WCO-modified samples, the 4% WCO sample (Figure 14) demonstrates the best performance, with a broader damage intensity range and smoother curve. In contrast, the 5% WCO sample (Figure 15) shows less favorable behavior, with a shorter damage range and irregular pattern, indicating potential instability. This makes WCO a suitable choice where flexibility is prioritized over extreme durability.
## 2 Materials and Methods
### Television Background
#### 2.1.1 Quality of Experience  
The quality of experience (QoE) measure has been proposed to more accurately estimate perceived quality and reflect viewers' perception of multimedia content more precisely than the quality of service (QoS) metrics. The QoE assessment can be subjective or objective. The first approach calculates the arithmetic mean of assessments collected from the subjects. The subjective assessment might be difficult to conduct on a large scale. Furthermore, such judgment is not considered when the assessment has to be gathered continually. The objective QoE can be computed by a reference algorithm adjusted to service provider infrastructure [2]. The QoE algorithms in IPTV services will be based on the other parameters in pure OTT applications or OTT-based television services deployed on telecom operator-managed networks. The proposed solution employs the reduced-reference approach. Applied reference is represented by the highest available bitrate for a given video adaptation set. The feature is extracted from the configuration available on the CDN server.
## 2 Materials and Methods
### Chromatographic Method Validation  
For determination of recovery of OGNs, serum samples were spiked to a concentration of 5 \(\upmu\)M of each analyte (DNA-16, DNA-18, and DNA-20). The recovery values were calculated by comparison of peak areas for samples obtained after extraction with final procedure, and standard solutions of the same concentration. The matrix effect was determined by following a typical procedure described in the literature [26], by comparison of the peak area for a standard sample with the peak area of the equivalent concentration of the analyte in a blank matrix sample spiked post-extraction. The matrix effect was included in the calculation of OGNs recoveries from spiked serum samples. Calibration curves were plotted based on standard solutions at seven concentrations (1.25, 2.5, 5.0, 7.5, 10.0, 15.0, and 20.0 \(\upmu\)M). The coefficient of determination (R\({}^{2}\)) showed the linearity for the calibration curve. A relative standard deviation of peak area for 10 injections in one day for concentration (for 1.25, 7.5, and 15 \(\upmu\)M) was calculated to the determination of intraday precision. A relative standard deviation of peak area for 5 injections at three different concentrations (for 1.25, 7.5, and 15 \(\upmu\)M) during the first, third, and seventh day of the experiment was calculated to the determination interday precision (repeatability). The limit of detection (LOD) and the limit of quantification (LOQ) were calculated based on Equations (2) and (3), respectively. In these equations, \(s\) is the standard deviation of the calibration curve intercept, and \(a\) is the slope of the calibration curve.  
\[LOD=\frac{3\times s}{a} \tag{2}\]  
\[LOQ=\frac{10\times s}{a} \tag{3}\]
## 4 Analysis and Evaluations
### Comparison between SDN and Legacy Network
#### 4.1.1 Bandwidth  
The results of bandwidth capacity in both the SDN and the legacy network were obtained by using _IPERF_ tool in Mininet. Here, we show the maximum bandwidth between hosts in neighbouring ASs and within the same AS for both the SDN and the legacy network. In addition to that, the maximum bandwidth when the hosts use AS65000 or SDN as a transit network to connect is also shown. As shown in Figure 9, the maximum bandwidth seems to be higher in the case of the SDN network, which can highlight the ability of SDN to transfer more data per second compared to their legacy counterparts.  
It can also be seen that the maximum bandwidth is higher for nodes within AS65000 or within the SDN compared to the neighbouring AS or when AS65000 and SDN are used as a transit network.
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 3 Resource Profiling and Performance Modeling
### Resource Profiling and Forecasting Process  
The overall resource profiling and forecasting procedure is illustrated in Figure 4. Our SCOUT system [10; 11] (denoted as \"Resource Monitor & Profiler\" in the Figure 4) periodically profiles and records information about each computing element (CE) of the VO including the number of CPU cores, queue waiting time, and average task response time. This information can be effectively utilized to forecast resource capability and performance such as CPU core availability and expected user response time.  
Our HTCaaS (High-Throughput Computing as a Service) [37; 38] system could take advantage of this forecasting data in order to efficiently submit a vast number of tasks by using all available computing resources in the Grid, Supercomputers, and Cloud. For example, the drug repositioning simulation needed about 80,000 docking tasks and we were able to successfully complete the experiment by leveraging 2300 CPU cores foretasted by our  
Figure 4: The overall process of identifying and forecasting resource capacity.  
models. Basically, HTCaaS aims to provide researchers with ease of exploring large-scale and complex HTC problems by leveraging Supercomputers, Grids, and Cloud by effectively hiding heterogeneity and complexity of harnessing different types of computing infrastructures from users. HTCaaS has been effectively integrated with national Supercomputers in Korea, international computational Grids, and Amazon EC2 resulting in combining a vast amount of computing resources to support most challenging scientific problems.
## 4 Design Analysis
### Finite Element Analysis
#### 4.7.4 Modal Analysis  
The analysis in this section is concerned with the dynamic behavior of the structure during launch. The first two natural frequencies were determined to ensure the first natural frequency was above 100 Hz, as is in the requirement of the launcher [3]. The same 3D model used in the quasi-static analysis was used for the modal analysis. According to the obtained results, the frequency of the first mode was 167.87 Hz, which deems the CubeSat structure robust since the frequency is greater than 100 Hz. The reaction wheels are the parts that are deformed during the first mode. Similarly, in the second mode, the reaction wheel is the most deformed part as shown in Figure 17.
# Effect of the Inter-Ring Delay Time on Rock Fragmentation: Field Tests at the Underground Mine
## 3 Theoretical Analysis of IRDT in Tunnel Excavation Blasting
### Collision of Fragment
If the delay time \(\Delta t_{3}\) is  
Figure 6: Tunnel excavation blasting model with different IRDTs: (**a**) tunnel excavation blasting model with IRDT of \(\Delta t_{1}\); (**b**) tunnel excavation blasting model with IRDT of \(\Delta t_{2}\); (**c**) tunnel excavation blasting model with IRDT of \(\Delta t_{3}\) (\(\Delta t_{3}\) is larger); where \(\Delta t_{3}>\Delta t_{2}>\Delta t_{1}\). large enough, the increase in the space S2 of collisions among the same-ring-blasted rocks will result in the reduced throwing energy, producing a much pile in the blasting cavity, reducing the collision space of the rock fragments generated after R3 initiation. As such, the explosion energy cannot be fully converted into rock fragmentation energy. In addition, according to the force of the collision process in Section 3.2, it can be seen that the blasting rock of the blast hole R2 collides with the blasting rock of the blast hole R1, which will increase the velocity of the rock mass in the S1 area perpendicular to the free surface and throw farther. Similarly, it can be seen that the rock burst collision between the same ring in the S2 region does not increase the velocity of the rock burst in R2 perpendicular to the free surface. It can be seen that the S1 region is more conducive to rock throwing. It can be seen that compared with the delay time, the S1 region gradually decreases and the S2 region gradually increases. Therefore, with the increase in the delay time between the rings, the rock-throwing distance will gradually decrease. At the same time, it should also be noted that when the delay between the rings is small enough, the throwing effect of the rock may be weakened. This is because R1 may not form a part of the free surface after detonation, and the explosive rock at R2 is made by the rock, which even leads to the failure of roadway excavation blasting. The above analysis shows that with an increase in the IRDT, the explosive energy is beneficial to rock fragmentation. However, when the delay time exceeds a certain value, the subsequent blasting rock throwing is weakened so that the formation of a muck pile in the blasting cavity reduced the following blasting rock forward collision space, reducing rock fragmentation. If the delay time is too short, the space for collisions among the same-ring-blasted rocks will be too small, which is unfavorable for rock fragmentation.
## 5 Results and Discussion
### Spatial Interaction Between HDT Behaviors and Clustered Freight Zones
#### 5.4.1 Tractor Truck (TT)  
Figure 9 shows a systematic analysis of the trip patterns of tractor trucks. As the primary vehicle type for trunk transportation and container logistics, tractor trucks demonstrate a pronounced spatial-temporal synergy with the functional positioning of freight areas. Sankey diagram analysis illustrates that Clusters 1 (72.28%) and 2 (20.49%) collectively account for 93% of tractor truck trips, with 59.92% of this volume comprising intra-Cluster 1 closed-loop movements, such as high-frequency round trips between hub pairs like Yangshan Port-Lingang Area and Pudong Aviation Freight Hub-Waigaoqiao Port. This reinforces the designation of these zones as international shipping hubs and core supply chain nodes. The highest \(HHI_{R}\) (0.71) underscores the structural concentration of transport corridors, a pattern deeply aligned with Cluster 1's specialization in bulk container shipping and other large-scale logistics operations that rely on tractor trucks'  
economies of scale. Port zone yard scheduling and cross-terminal transshipment further amplify tractor truck agglomeration in these high-intensity freight nodes.  
As transitional zones adjacent to core hubs, Cluster 2 comprises 20.49% of tractor truck trips, encompassing peripheral industrial parks, mixed-function nodes, and logistics support infrastructure. Tractor truck activities here exhibit two functions: (1) facilitating container barging between hubs and storage yards; and (2) providing raw material and finished product trunk transportation for manufacturing facilities. This dual role aligns with Cluster 2's positioning as a buffer zone in the freight network. The \(HHI_{E}\) of 0.57, the highest among all vehicle types, quantifies the intense spatial concentration of tractor truck operations. An ID entropy analysis further reveals organizational divergences: ports and logistics yards exhibit a higher ID entropy, reflecting multi-stakeholder, market-driven capacity scheduling and the public attributes of these sites, whereas the factory clusters show a lower ID entropy, indicative of closed-loop operations by corporate-owned fleets or long-term contracted carriers.
## 2 Materials and Methods
### Instance Segmentation  
The novel YOLOv8 model expands on the achievement of earlier YOLO iterations and incorporates additional capabilities and enhancements that significantly increase its performance and versatility [49]. The pre-trained weight model \"yolov8-seg.yaml\" from GitHub was used as the initial model. Rather than splitting up into two phases like Faster R-CNN, which first detects regions of interest before recognizing items in those areas, algorithms such as Single-Shot Detector (SSD) and You Only Look Once (YOLO) focus on locating every item in the shot in a single forward pass [49; 50; 51]. Faster R-CNN is a rather sluggish detector that fails in real-time tasks, while it has a slightly improved accuracy when real-time processing is not necessary [52; 53]. SSD is simpler compared to methods that require region proposals because it completely eliminates the proposal generation phase and the subsequent pixel or feature resampling phase, encapsulating all computations in a single network [50]. Therefore, YOLO is more suitable for our application to achieve real-time segmentation. To avoid the disturbance created by medical personnel or visitors entering the identification area, our customized YOLO model should have the ability to identify the head of a patient and heads of non-patient participants. Thus, instance segmentation is preferred over object detection as it takes more morphological information into consideration to identify similar objects. The training phase adopted python 3.10.1, torch 2.0.0, 100 epochs, batch size 16, a learning rate of 0.01, momentum of 0.937, and patience of 50. The computer (Centralfield Computer Ltd., Hong Kong, China) used for training with Windows 10 Education operating system (Microsoft Co., Redmond, WA, USA) 32 GB of RAM, a 2.1 GHz Intel(r) Core(tm) i7-12700 processor with 12 cores and 2 TB of solid state hard disk (SSD).
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 3 Results and Discussion
Compared to other antennas, the one that is being supplied in this research has great characteristics; it has a compact size, is wideband, and has a high gain, which allows it to cover the operational bands with great efficiency. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Size mm\({}^{2}\)** & **Substrate** & **Frequency Range (GHz)** & **Bandwidth (GHz)** & **Resonant Frequency (GHz)** & **Peak Gain (dB)** & **Methods** & **Applications** \\ \hline
[21] & 46.6 \(\times\) 53.4 & FR4 & 2.42–2.49 & 0.7 & 2.45 & 1.67–1.87 &
\begin{tabular}{c} Electromagnetic \\ bandgap structures \\ \end{tabular} & Wi-Fi \\ \hline
[22] & 50 \(\times\) 40 & FR4 & 2.24–2.79 & 0.55 & 2.45 & 3.75–10 &
\begin{tabular}{c} Reducing antenna \\ size and increasing \\ gain through \\ \end{tabular} & Wi-Fi \\ \hline
[23] & 60 \(\times\) 63 & FR4 & - & \begin{tabular}{c} 0.22 \\ 2.44 \\ 1.93 \\ \end{tabular} & \
\begin{tabular}{c} 1.62/2.45 \\ 4.37/5.56 \\ 6.0/8.81 \\ \end{tabular} & 2.18–5.79 &
\begin{tabular}{c} Using fractal \\ geometry to \\ achieve multiband \\ operation \\ \end{tabular} & Wi-Fi and \\ \hline
[24] & 40 \(\times\) 40 & FR4 & 2.88–3.92 & 1.04 & 3.38 & 0.8–6 & \
\begin{tabular}{c} Modifying the \\ antenna design to \\ achieve dual-band \\ \end{tabular} &
\begin{tabular}{c} WiMax and \\ WLAN \\ \end{tabular} \\ \hline
[25] & 57.2 \(\times\) 31.2 & FR4 & 2.35–2.55 & 0.2 & 2.45 & - & \
\begin{tabular}{c} Using an \\ asymmetric \\ \end{tabular} &
\begin{tabular}{c} GSM, \\ WLAN and \\ WLAN and \\ WMAX \\ \end{tabular} \\ \hline
[26] & 60 \(\times\) 60 & FR4 & 2.4–2.485 & 0.085 & 2.46 & - &
\begin{tabular}{c} Incorporating \\ double-sided \\ MIMO \\ \end{tabular} & WiMAX and \\ \hline
[27] & 48 \(\times\) 48 & FR4 & 1.69–1.94 & 0.25 & 1.775 & 0–7.5 & \
\begin{tabular}{c} Modifying the \\ antenna design to \\ achieve dual-band \\ \end{tabular} &
\begin{tabular}{c} GSM and \\ WiMAX \\ \end{tabular} \\ \hline
[25] & 40 \(\times\) 32 & FR4 & 2.34–2.52 & 0.18 & 3.1 & 1.6–3.8 & \
\begin{tabular}{c} Incorporating a \\ dumbbell-shaped \\ defected ground \\ \end{tabular} &
\begin{tabular}{c} GSM, WLAN, \\ and \\ WiMAX \\ \end{tabular} \\ \hline
[32] & 50 \(\times\) 50 & FR4 & 2.35–2.44 & 0.09 & 2.4 & - &
\begin{tabular}{c} Using printed \\ circuit board \\ technology \\ \end{tabular} & WiAN, and \\ \hline
[34] & 60 \(\times\) 45 & FR4 & 2.25–2.95 & 0.7 & 2.6, 3.5 & - & \
\begin{tabular}{c} Using a dual-layer \\ design with an \\ E-shaped and \\ U-slot patch \\ \end{tabular} & WiMAX \\ \hline Our Antenna & 40 \(\times\) 34 & FR4 & 2.26–4.10 & 1.84 & 3.51 & 2.1–9 &
\begin{tabular}{c} Using fractal \\ geometry, with an \\ \(\mathbb{L}\) shape added to \\ the partial \\ ground plane \\ \end{tabular} & WiMAX, C and \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the recommended antenna with other antennas described in the literature.
## 5 Further Discussion
### The Effect of the Damping Layer's Thickness (\(h_{d}\) Is Variable)  
In this subsection, we delve into the influence of the distribution of damping layer thickness on the system's overall damping by defining several functions that model variations in damping layer thickness under the conditions specified by Equations (24) and (25). The damping layer is situated within \([x_{d1},x_{d3}]=[0.16,0.28]\). Additionally, the impact of a damping layer with a uniform thickness of \(0.0002\,m\) serves as a benchmark for comparison. Following the findings from Section 3, which suggest that increased damping at the ABH tip yields better outcomes, we now contemplate the thickness \(h_{d}(x)\) that alters according to various specific mathematical expressions:  
\[\begin{split} h_{d10}(x)&=-\frac{1}{150}|x-0.22|+ \frac{1}{2500},\\ h_{d11}(x)&=-\frac{1}{12}(x-0.22)^{2}+\frac{3}{1000 0},\\ h_{d12}(x)&=\frac{1}{6}(|x-0.22|-0.06)^{2},\\ h_{d13}(x)&=-\frac{100}{81}|x-0.22|^{3}+\frac{1}{37 50},\quad\quad\quad\quad x\in[0.16,0.28]\end{split} \tag{34}\]  
Figure 16: The comparison of the effects of different damping layer positions on the system’s modal loss factor. The damping layer thickness consistently is set at \(h_{d}=0.0002\,m\).  
The graphical representations of the functions from Equation (34) closely resemble those shown in Figure 8; therefore, no further graphics are provided here. Figure 17 demonstrates the intensified ABH effect achieved by employing diverse thickness variation functions, as defined in Equation (34). The results obtained by customizing the damping layer's thickness with functions \(h_{d10}\) to \(h_{d13}\) are superior to those of a uniform thickness. Notably, Figure 17 reveals that the function \(h_{d12}\) yields the most significant ABH effect among the thickness variation functions. This indicates that focusing a higher concentration of damping material at the ABH tip and diminishing it as we move away from the tip significantly boosts the performance compared to an even distribution.