esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Study on the Waterstop of the Rodin Jet Pile Method in a Water-Rich Sandy Gravel Stratum
## 2 RJP Construction Method
### Process Characteristics  
1. Large pile depth and large pile diameter  
The RJP method can achieve deep foundation improvement, and the maximum depth can reach 60 m. Through the secondary cutting technology, a solid structure with a diameter of 2.0 to 5.0 m can be realized, while the strength can reach 1.0 MPa or higher, and the permeability coefficient can reach \(10^{-7}\) to \(10^{-8}\) cm/s.  
2. Low mud discharge and low displacement  
Due to efficiency improvement of the rotary jet, the amount of grouting material is reduced by approximately 20 to 25% compared with that generated by conventional rotary jet grouting [7], thereby reducing the amount of mud, reducing the environmental impact and reducing the formation deformation by reducing the amount of injection.  
3. Shortening the construction period  
The RJP method reduces the spray loss rate by using a nozzle with an extremely low frictional resistance and improves the ability to cut into the soil by 10%. The combination of the upper and lower nozzles ensures the diameter of the pile, avoids double spraying, and greatly shortens the construction period.  
4. Flexible equipment  
The RJP method has the characteristics of miniaturization and a small-diameter drill pipe and can be constructed in a small space or close to a building.
# High-Power Broadband Frequency Chirped Intensity-Modulated Single-Frequency 1064-nm Laser
## 1 Introduction  
Optical carried microwave radar (OCMR) uses an intensity-modulated laser beam as the detection medium. The phase of the modulated returned signal is used to resolve the distance of the object to the transmitter and velocity of the detected objects. OCMR has high spatial resolution as lidar; at the same time, the modulated beam has stronger resistance to atmospheric turbulence and scattering than conventional lidar [1; 2; 3; 4]. However, when OCMR is applied for ranging, there is a contradiction between the high ranging resolution, which requires a high modulation frequency, and the long unambiguous distance, which requires a low modulation frequency. The frequency domain reflectometry (FDR) technique can decouple the dependence of the range resolution and unambiguous range of hybrid lidar-radar systems and has since been used in a wide variety of applications where a large unambiguous range and high precision are desired [5; 6].  
Broadband frequency chirping intensity modulation of the laser beam was obtained with Mach-Zehnder electro-optic modulator(EOM) [7; 8] and applied in an amplitude modulation (AM) lidar system by [PERSON] et al. The chirped frequency was 80-280 MHz, the scanning period was 200 us, and the range accuracy reached 0.8 m [9]. However, in the existing AM radar system, the output power of the transmitter was generally lower than 100 mW, even though a highly sensitive photon counting detector was used, and the ranging distance was limited by the low power of the lidar transmitter [10; 11; 12]. In order to apply FDR technology in long-distance ranging and detection, an intensity-modulated light source with high-power broad-band frequency chirping is desired.  
In this paper, we present a new approach of obtaining high-power broad-band intensity-modulated light source based on modulation and amplification. A low-power light source with a narrow linewidth was modulated via a Mach Zehnder EOM. The output from the EOM was amplified via a two-stage ytterbium-doped fiber amplifier (YDFA). The modulation depth and frequency chirping linearity before and after amplification are discussed.
## 5 Conclusions  
This is the first study to investigate the ability of four different accelerometry derived metrics and a triple GNSS to predict known distance. Linear regression analysis revealed that GNSSD, IL, MAG, SUM, and PL could indicate total distance completed while walking. The findings will be of interest to researchers and sports scientists to investigate whether GNSS and accelerometry are equally capable of quantifying training loads associated with sport-related training and competition. More research using controlled trials are needed to compare these technologies to detect sports events (e.g., contact, jumps, sprinting) and quantify training loads associated with acceleration, deceleration, and directional change, which are considered crucial characteristics of match play in some sports. Another possible area of future research would be to investigate whether training-load quantification is enhanced using a combination of GNSS and accelerometry, or whether a single sensor, GNSS, or accelerometer is adequate to quantify training loads in sports, considering that not all teams can afford the high-cost of both technologies.
## 1 Introduction
### Literature Review
#### 1.2.3 Combined Surface Roughness and Porous Structure in Additive Manufacturing
The best-performing structure achieved a 2.96 times higher HTC at 1.567 W/cm\({}^{2}\).K and a 3.20 times higher CHF, exceeding 74.29 W/cm\({}^{2}\), compared to plain surfaces. The enhancements were attributed to the increased surface area, higher nucleation site density, and capillary-assisted suction. [PERSON] et al. [35] explored porous Ti-6 Al-4V coatings produced by cold-spray additive manufacturing. The resulting honeycomb-structured porous coatings increased the HTCand CHF due to enhanced surface area and nucleation site density. [PERSON] and [PERSON] [36] investigated the pool boiling of nitrogen on 3D-printed porous copper heaters versus smooth ones. Porous coatings significantly enhanced the HTC up to six times at low heat fluxes. While the smooth heater exhibited a clear boiling transition and a CHF of 16.9 W/cm\({}^{2}\), the porous samples showed boiling at lower heat fluxes without distinct transitions. Critically, one porous sample (No. 1) achieved a nearly 30% higher CHF, while another (No. 2) matched the smooth one due to localized film boiling. [PERSON] et al. [37] investigated hierarchical sintered porous structures combining copper mesh and porous columns. These structures exhibited enhanced capillary effects, increasing CHF to 2422.9 kW/m\({}^{2}\), outperforming traditional sintered mesh surfaces by 43.7%. The addition of micro-/nano-coatings further increased the nucleation sites, improving HTC, especially at lower heat fluxes. These studies highlight the synergistic benefits of combining porosity with microstructures for enhanced boiling performance.
## 2 System Model
### LDPC Coding and Decoding Algorithms
#### Decoding  
In the decoding process, the sum-product algorithm (SPA) is being taken into consideration which is a soft decision algorithm that accepts as input the probability of each bit that has been received. This algorithm is based on the factor graph representation of the sparse parity-check matrix, updating iteratively the soft data between check nodes \(m\) and variable nodes \(n\)[49].  
The iterative process for SPA, used for LDPC codes, is summarized as follows [50]:  
1. Initialization: variable nodes are initialized;
2. Update check nodes: a check node m gathers all incoming messages and evaluates the message sent to the variable node \(n\);  
**Step 3.**: Update variable nodes: a variable node passes the message to all the check nodes in connection with it;
**Step 4.**: Decision: all messages of a variable node \(n\) that indicates the probability to decode the variable node \(n\) to 1 or 0 can be obtained by adding all incoming messages to the variable node \(n\). After each iteration, a soft decision on variable \(n\) is taken--decoded as zero or decoded as one. If a valid codeword can be formed, the algorithm stops and the decoding result is out. If not, the algorithm returns to **Step 2**.  
The SPA needs high computational complexity because of the numerous multiplication and division operations made to complete check node updates. Therefore, a simplified version of SPA is being proposed that replaces those operations with additions and subtractions.  
In this paper, we take into consideration the simplified SPA algorithm to decode the received data.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 4 Network Planning and Optimisation
### Methodology  
A network planning algorithm is implemented to study the proposed architecture. We investigate the statistical stability of the simulations by running a series of simulations until specific simulation metrics (Section 4.5) complete a deviation of \(+-0.5\%\) of the average value of the metric over 100 simulations. As a result, we found stability for most metrics with 25 simulations. This leads to running 25 independent simulations for each configuration to obtain an accurate value of the investigated metric.  
As defined previously, for UC1 and UC2, we investigate the impact of environmental factors such as rain and vegetation and the location of ENs. For these use cases, we compare no rain (NR), light rain (LR) and heavy rain (HR) and no vegetation (NV), winter, with vegetation (VE), summer. For the location of ENs, we compare the facade (FA), lamp post (LP) and both facade and lamp post (FL) techniques. This adds up to 18 simulation configurations for each use case. For the UC3, only the location of ENs is investigated, assuming the best-case scenario for the urban case where neither rain nor vegetation is accounted for. Finally, the multi-bouncing ray tracing technique for a line-of-sight (LoS) and non-line-of-sight (NLoS), up to two reflecting orders, is investigated.
# Study on Stiffness Parameters of the Hardening Soil Model in Sandy Gravel Stratum
## 3 Numerical Simulation Verification
### Model Parameters  
The project's construction land covers 8913 m\({}^{2}\), with a length of about 120 m and a width of about 60 m. It has four floors underground, and the buried depth of the foundation is about 17.70 m. This paper's constitutive models of the soil layers are all HS models. According to the survey report and the actual excavation on site, the soil is divided into eight layers from top to bottom. The specific parameters of each layer are shown in Table 3. Among them, the sandy gravel parameters of the fifth and seventh layers are calculated according to \(E_{\text{oed}}\):\(E_{\text{50}}\):\(E_{\text{ur}}\) = 1:1:4.3.  
The boundary of the model should be 3-5 times the depth of the foundation pit, so the length \(\times\) width \(\times\) depth is 400 m \(\times\) 200 m \(\times\) 80 m, respectively. Based on the conditions, a three-dimensional stratum structure model is established. The whole model has 350,693 nodes and 268,479 units. The structural models of the foundation pit, stratum, and surface buildings are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Soil Layer** & **Thickness** & **Gravity** & **Cohesion** & **Internal Friction** & **Poisson’s** & **\(E_{\text{50}}\)** & **\(E_{\text{ood}}\)** & **\(E_{\text{ur}}\)** \\  & **(m)** & **(m)** & **(kPa)** & **Angle (Rad)** & **Ratio** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline Miscellaneous fill & 4 & 16.5 & 5 & 10 & 0.35 & 13 & 13 & 52 \\ Sandy silt & 3 & 21 & 0 & 15 & 0.29 & 10 & 10 & 40 \\ Clay & 2 & 18.8 & 21 & 12 & 0.31 & 6 & 6 & 30 \\ Fine sand & 5 & 19.8 & 0 & 28 & 0.27 & 20 & 20 & 60 \\ Sandy gravel & 7 & 21.2 & 0 & 35 & 0.23 & 30 & 30 & 129 \\ Silt & 2 & 14 & 15 & 25 & 0.37 & 14 & 14 & 56 \\ Sandy gravel & 8 & 21.5 & 0 & 40 & 0.21 & 40 & 40 & 172 \\ Silty clay & 3 & 19.2 & 27.2 & 13.9 & 0.357 & 12 & 12 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical and mechanical parameters of different soil layers.  
Figure 9: The finite element model of the foundation pit.  
In this part, the boundary around the model restricts the normal displacement, and the vertical displacement is free. The bottom is a fixed boundary condition, and the top soil layer is set as a free boundary. At the same time, in order to ensure that the model is closer to the actual site construction conditions, the precipitation step is added to the model. An initial pressure head is applied to the surface around the soil at \(-19\) m of the stratum; a pressure head of 0 is applied at \(-30\) m of the excavated soil of the foundation pit as the dewatering head. Existing buildings around the pit are transformed into an equivalent load. The structural load of the two-story is 1687.5 kN, and the structural load of the five-story is 4218.75 kN. The buildings west of the pit are simplified as a uniformly distributed load, which is 843.75 kN.
## 2 Related Work
In this work, we focus on the robustness and distinctiveness of RSS fingerprint data with the support of pre-processing to make pre-existing indoor localization frameworks more efficient and accurate. Previously reported frameworks mainly focused on enhancement of RSS fingerprint data by suppressing noise through both pre-processing and post-processing, whereas our proposed approach transforms the raw fingerprint data into a more meaningful shape that is robust and leads to highly accurate localization. BoF and Bag-of-Words (BoW) [37] exist for image and document classification, respectively, whereas here it is implemented and incorporated for RSS-based indoor positioning for the first time.
# Magnus-Forces Analysis of Pitched-Baseball Trajectories Using YOLOv3-Tiny Deep Learning Algorithm
## 2 Materials and Methods
### Recognition of a Pitched-Baseball Trajectory by Deep Learning Algorithm  
To analyze the results of the ball speed and spin rate of the baseball through aerodynamic theory, this study used the YOLOv3 deep learning algorithm to recognize the baseball and its flight trajectory. YOLOv3, the third generation of YOLO, has the advantages of the previous generations' algorithms and has an updated multi-scale detection function and a more powerful feature extraction network. Therefore, it can detect objects of different scales simultaneously, thereby improving the accuracy and performance. The YOLOv3 algorithm is divided into the following two major parts [17]: (1) feature extraction and (2) multi-scale detection. The architecture of YOLOv3 is illustrated in Figure 5. The feature extractor of YOLOv3 is Darknet-53, which was modified from Darknet-19 used by YOLOv2 [30]. Using the concepts of residual network, ResNet, and residual learning, it avoids the gradient diminishing problem by increasing the number of network layers for more complex feature extraction.  
In the multi-scale detection portion, YOLOv3 uses feature pyramid networks (FPNs) upgraded from the original single-layer 13 \(\times\) 13 feature prediction network to a three-layer 13 \(\times\) 13, 26 \(\times\) 26, and 52 \(\times\) 52 feature prediction network. The single layer of five bounding boxes is changed to three layers of three bounding boxes each, which is equivalent to a total of nine bounding boxes. The FPN architecture merges the better target at the lower level with the better semantic features at the higher level and makes independent predictions at different feature levels, showing an obvious improvement in small object detection. YOLOv4 [24; 25; 26] utilizes a spatial pyramid pooling and path aggregation network to improve the performance of YOLOv3.  
However, because the baseball thrown by the pitcher is fast and live-action TV videos like sports and concerts are typically at least 24 fps or more [27], this study used YOLOv3-tiny algorithm to speed up the calculation [18]. To speed up the calculation, this study further used YOLOv3-tiny to recognize the baseball and its flight trajectory. YOLOv3-tiny is a simplified version of YOLOv3, as shown in Figure 6. The backbone network of Darknet-53 is simplified to a network similar to that of Darknet-19, and the 52 \(\times\) 52 small object prediction branch and its feature layer have been omitted, leaving only two independent prediction branches. Although the accuracy is not as high as that of YOLOv3 and YOLOv4, the calculation becomes extremely fast [19; 23; 26]. The efficiency and accuracy of the calculation were sufficient for the identification and tracking of the baseballs used in this study. In the next section, this study has compared the performance of models trained by YOLOv3 and YOLOv4 with that of a model trained by YOLOv3-tiny algorithm.  
Figure 5: Architecture of YOLOv3.
## 4 Results
### Cost Analysis
#### 4.2.1 Scenario 1  
The S1 or ONP-RND scenario included the on-site concrete printing of the round design. In S1, we used a robotic arm and the dry mix shipped to the construction site to build the walls of the house (the base floor slab and the roof were not part of our modeling and calculations). There were four main cost components in S1: robot, material, labor, and transportation costs. For this research, where the scope of the construction project was 100 identical houses, we assumed the use of five robotic arms to reduce the overall project completion time by executing parallel production. In S1, due to the round shape of the design, the print job could be performed with no movement of the robotic arm during the construction of each house, due to the sufficient reach of the robotic arm. In S1, like the other scenarios, we used a two-axle truck for the transportation of the materials and the robotic arm, and a forklift to move the robotic arm from one completed house to the next.  
Our calculations illustrated that 55.6% of the wall construction costs related to labor costs, while 30.4% of the costs related to the materials used for the printing, reinforcement, and waste. Transportation and robot costs were responsible for 11.1% and 2.9% of the total wall construction costs in this project, respectively. This illustrated that, although the process took advantage of robotic automation, the monitoring and management of the robot still entailed costs for a significant amount of manual and skilled work. For more detail refer to Table 2.
# Ultrafast Mid-IR Laser Pulses Generation via Chirp Manipulated Optical Parametric Amplification
###### Abstract  
Over the past decades, optical parametric amplification (OPA) has become one of the most promising sources of ultrafast Mid-IR laser, owing to its outstanding properties including ultrabroad bandwidth, superior tunability, good beam quality, and scalable energy. In this paper, we review the recent progress in ultrashort laser pulse generation via chirp manipulated OPA, which improves the energy scalability and gain bandwidth by strategically chirping both pump and seed pulses. The gain mechanism is theoretically analyzed and the OPA processes are numerically simulated. In addition, the concept is verified experimentally. Femtosecond pulses with hundreds of mJ are generated in a high energy dual-chirped-OPA (DC-OPA), and ultrabroadband \(\upmu\)-level spectra supporting sub-2-cycle pulse durations are achieved in BBP-OPA. Furthermore, the obtained pulses show excellent tunability through the NIR to Mid-IR regions, which makes them a suitable seeding source for further amplification as well as powerful tools in various applications such as strong field physics, attosecond science, and ultrafast spectroscopy.  
ultrafast laser; strong field physics; high-order harmonic generation; optical parametric amplifier; mid-infrared source +
Footnote †: journal: Applied Physics Letters
# Truck Platoon Control Considering Heterogeneous Vehicles
## 3 Multi-Vehicle Control  
This paper considers the scenario where the leader truck is driven by a trained truck driver. The proposed approach is to make all trucks follow the leader's trajectory.  
An autonomous truck keeps tracking the leader by following the waypoints of the leader. Whenever a following truck's distance from a waypoint \(W_{m-1}\) is less than \(Th\), the truck heads towards the next waypoint \(W_{m}\).  
To enable this, each truck localizes itself in the global coordinate system using various sensors, such as Lidar, Distance Measurement Instruments (DMI), Global Positioning Systems (GPS) or, Inertial Measurement Units (IMU). (Localization based on multiple sensors is not within the scope of this paper and is presented in various papers, such as [34; 35; 36]. In the case where there are sensing or communication infrastructure along a road, we can use the infrastructure to localize a truck. In order to localize a truck, many estimation methods were utilized, such as time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48].)  
Using V2V communication [49], the leader's information (\(W_{m}\)) is transmitted to every following truck. At each time step \(k\), the leader's information (\(W_{m}\)) is transmitted to every other truck in real time.
## 7 Passive Optical Network
### TDM-PON Application  
There are different variants of TDM-PON, such as Asynchronous Transfer Mode (ATM) PON (APON), Broadband PON (BPON), GPON, and Ethernet PON (EPON). In the current networks, GPON and EPON are the most extensively implemented TDM-PON schemes [33; 143]. Therefore, in the parts that follow, we pay more attention to both solutions.
## 2 Topologies in Smart Inverters
### Topologies under Wired Communication  
The wired communication is vulnerable to electromagnetic interference and offers lesser operational dependency with batteries. The cost of implementation is high, and the signal quality declines over **PowerLine Communication (PLC)**. The main advantages include easy implementation for long distances and wide coverage. PLC can be classified as (a) narrow band (NB) PLC's and broadband (BB) frequencies. NB and BB PLCs operate at 500 kHz and 30 MHz, respectively. NB PLCs provide low-data rates but offer higher range. BB PLCs provide high-data rate but for a shorter duration with lower reliability. Hence, NB PLCs are preferred for smart grids or smart inverters and domestic applications. Comparative analyses between different wired technologies are shown in Table 1.
# The Pre-Heating Effect for Porosity Control during the Laser Welding of GAlvanized Steel Sheets
###### Abstract  
Recently, for the high performance of automobiles, the application ratio of zinc-coated steel sheets with excellent corrosion resistance has been increasing. However, it is difficult to achieve sound welds, as porosities form from zinc during welding. In this study, a laser welding process, with the addition of a pretreatment step to conventional laser welding, was devised to improve weld quality by eliminating pores caused by zinc in the welds. Laser welding was performed on the overlapping joint of hot-tip galvanized steel sheets (SGARC 340, Hyundai Steel., Suncheon-Si, Joellaman-do, Republic of Korea), with a thickness of 1.2 mm in the upper sheet and 0.9 mm in the lower sheet, and the welding characteristics were investigated by varying the laser power and focal position. Compared to conventional laser welding, the three-step laser stitch welding process significantly reduced the degree of spatter generation and welding defects. Additionally, it increased the tensile-shear load by approximately 37%. Moreover, the reduction in the zinc component of the three-step laser stitch welds was confirmed through SEM-EDS analysis. These findings contribute valuable information for securing high-quality welding joints and effectively addressing zinc-coated steel sheet welding quality issues in the automotive and industrial sectors.  
hot-tip galvanized steel; three-step laser stitch welding; zinc removal effect; porosity control