esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Germination and Plant Growth Test  
The fertilizers obtained were tested on baby lettuce _Lactuca sativa_ L. cultivar Chiara (ISI Sementi SpA, Fidenza, Italy), a vegetable specie of horticultural interest, chosen because of its short life cycle (3-4 weeks) and because its small size allows it to be grown both indoors and outdoors, in small pots or jars, vertical green walls, etc. The substrate used for the growth test was made by mixing agri-perlite Aggrillite 3 (Perlite Italian Srl, Italy) (particle size 2-5.6 mm, density 90 kg/m\({}^{3}\)) with a peat moss soil, Potgrod H (Klasmann, Germany), pH 5.5, salinity 3.5 dS/m, containing N (210 mg N/L), P (150 mg P\({}_{2}\)O\({}_{5}\)/L), K (270 mgK\({}_{2}\)O/L), Mg (100 mg Mg/L) plus micronutrients. Agri-perlite and peat moss soil were mixed at a rate of 3:1, respectively, with the aim of reducing the use of substrates of natural origin, decreasing the environmental impact linked to the exploitation of peat. Five seeds of lettuce per pot (with a capacity of 500 mL) were placed on the potting medium, and a quantity of water (50 mL/pot) was provided every 3 days. Six coated fertilizer granules were added to each pot, placing them in two layers in the center of the substrate. Two pots had APNUT_BSF and two pots had APV50_BSF. Another 4 pots without PIMs (controls) were prepared. The pots were placed in a growth room at 21 \(\pm\) 2 \({}^{\circ}\)C and relative humidity of 38 \(\pm\) 7% for 21 days under artificial light and long-day conditions (16 h light, 8 h dark).  
At the end of the 21 days, the following data were collected: average fresh weight/pot (g) of the plants, recorded as total fresh mass (TFM) and average dry weight/pot (g), recorded as total dry mass (TDM). The latter was obtained after drying the plants in a ventilated oven at 80 \({}^{\circ}\)C for 24 h. The average leaf area/pot (LA) was measured considering two plants for each pot and the three largest leaves of each plant. Prior to the measurement, the leaves were cut from the two plants and then scanned and analyzed using ImageJ software (version 1.52, NIH, Bethesda, MD, USA). The total area of the three leaves was considered for each plant, and the average value of the plant area for each pot was calculated.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Ground Segment Modeling
#### 3.2.2 Traceroute Classify
Atlanta in the United States leads with a 16.81% share of POP, followed by Los Angeles (12.79%) and Seattle (10.99%). Cities that contribute to the POP share include Madrid (8.18%) and São Paulo (5.86%). Additionally, an “other” category comprising 30 cities accounts for 12.71% of the POP share. discussions on Reddit by Starlink users, ICMP packets are routed through designated ground stations for processing. Our network measurements confirm that all Traceroute paths pass through nine specific ground stations, effectively covering most ground stations. However, South American ground stations were not covered due to the challenges in user location accuracy and limitations in the available dataset. (This limitation arises from the difficulty in pinpointing satellite network users and the constraints of the published Starlink dataset). All packets are initially transmitted to a designated ground station for processing, after which the ground station forwards them through the satellite network to the end-users. Utilizing empirical data, SatScope establishes efficient routing tasks between ground stations and terminal users.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 1 Introduction
The improved algorithm can de-interleave staggered signals and adapt to PRI jitter well, but it is still computationally inefficient. In addition to the typical de-interleaving algorithms described above, other types of signal de-interleaving algorithms have also been proposed, including signal de-interleaving algorithms based on square sine wave interpolation [21; 22], signal de-interleaving algorithms based on sequence correlation [23; 24; 25], and signal de-interleaving algorithms based on machine learning [26; 27; 28; 29]. In 2007, [PERSON] et al. proposed a square sine wave interpolation algorithm [21], estimating PRI with high accuracy, speed, and anti-pulse loss. However, it is sensitive to PRI jitter and unsuitable for de-interleaving staggered signals. In recent years, signal de-interleaving algorithms based on machine learning have drawn the attention of scholars. However, such algorithms are still far from application. The existing staggered PRI signal algorithms are computationally inefficient and cannot meet the real-time requirements of ESM systems. An improved algorithm has been proposed to solve this problem. First, an improved square sine wave interpolation algorithm has been proposed, improving computational efficiency while suppressing interference between staggered PRI signals' sub-PRIs and the frame period. A sequence retrieval algorithm incorporating matched filter theory is also proposed to improve the separation accuracy of radar pulse sequences. Finally, the staggered PRI signal is extracted, and the sub-PRIs are estimated. The simulation shows that the proposed algorithm is computationally efficient, reliable, and robust for de-interleaving staggered PRI signals from mixed signals. The contributions of this paper are summarized as follows. An efficient algorithm is proposed to de-interleave staggered PRI signals from the mixed signals. The algorithm can adjust to a broad PRI range and dense pulse signals with negligible computational costs. 2. An improved square sine wave interpolation (SSWI) function is constructed, making the algorithm adapt to staggered PRI signal and PRI jitter. 3. A threshold criterion is designed for the improved SSWI algorithm, which decreases the interference between the sub-PRIs and the frame period. 4. A sequence retrieval algorithm incorporating matched filter theory is proposed, enhancing the separation accuracy of radar pulse sequences. This paper is organized as follows. In Section 2, the TOA model of the mixed pulse sequences is constructed. Moreover, an efficient algorithm is proposed. In Section 3, the superiority and robustness of the proposed algorithm are verified by comparative and semi-physical simulation experiments. Moreover, the computational costs are analyzed. Finally, Section 4 concludes this paper.
###### Abstract  
Tunable mid-infrared lasers are widely used in laser spectroscopy, gas sensing and many other related areas. In order to solve heat dissipation problems and improve the environmental temperature adaptability of solid-state laser sources, a tunable all-fiber laser pumped optical parametric oscillator (OPO) was established, and a compact thermal control system based on thermoelectric coolers, an automatic temperature control circuit, cooling fins, fans and heat pipes was integrated and designed for the laser. This system is compact, light and air-cooling which satisfies the demand for miniaturization of lasers. A mathematical model and method was established to estimate the cooling capacity of this thermal control system under different ambient environments. A finite-element model was built and simulated to analyze the thermal transfer process. Experiments in room and high temperature environments were carried out and showed that the substrate temperature of a pump module could be maintained at a stable value with controlled precision to 0.2 degrees, while the output power stability of the laser was within \(\pm\)1%. The experimental results indicate that this compact air-cooling thermal control system could effectively solve the heat dissipation problem of mid-infrared solid-state lasers with a one hundred watts level pump module in room and high temperature environments.  
t  
tunable mid-infrared solid-state laser; thermal control; all-fiber laser; thermoelectric cooling; finite-element analysis; optical parametric oscillator
###### Abstract  
Tilt vibrations inevitably have negative effects on some precise engineering even after applying horizontal and vertical vibration isolations. It is difficult to adopt a traditional passive vibration isolation (PVI) scheme to realize tilt vibration isolation. In this paper, we present and develop a tilt active vibration isolation (AVI) device using a vertical pendulum (VP) tiltmeter and a piezoelectric transducer (PZT). The potential resolution of the VP is dependent on the mechanical thermal noise in the frequency bandwidth of about 0.0265 nrad, which need not be considered because it is far below the ground tilt of the laboratory. The tilt sensitivity of the device in an open-loop mode, investigated experimentally using a voltage controller, is found to be \((1.63\pm 0.11)\times 10^{5}\) V/rad. To compensate for the hysteresis nonlinearity of the PZT, we experimentally established the multi-loop mathematical model of hysteresis, and designed a parallel controller consisting of both a hysteresis inverse model predictor and a digital proportional-integral-differential (PID) adjuster. Finally, the response of the device working in close-loop mode to the tilt vibration was tested experimentally, and the tilt AVI device showed a good vibration isolation performance, which can remarkably reduce the tilt vibration, for example, from 6.0131 urad to below 0.0103 urad.  
title active vibration isolation; precise engineering; vertical pendulum; piezoelectric actuator; parallel controller
## 3 Results and Discussion
### Bubble Motion
#### 3.1.3 Bubble Motion near an Elastic Cylinder with Different Rod Sizes
At a relatively long dimensionless distance, it is easier to produce a mushroom-shaped shrinking bubble (\(t=2.15\) ms) during collapse. After the bubble is completely collapsed, it will cause the displacement of the local boundary. Figure 10b,c are the cases where the dimensionless distance is larger. For Figure 10b, it is worth noting that the area of contact between the bubble and the elastic boundary after expansion is smaller, and the deformation of the spherical bubble during expansion is smaller. On the contrary, in the collapse stage, as shown in \(t=2.01\) ms, the bubble is close to the elastic cylindrical boundary. Due to the interaction, the bubble is deformed and its shape is more like a bulb, or a mushroom with a longer stem. The deformation here is related to the dimensionless distance and the dimensionless radius of the elastic cylinder. In Figure 10c, the expansion and collapse of the bubble are still basically spherical, indicating that the influence of the dimensionless distance on the bubble deformation is greater. However, at \(t=1.90\) ms, it can be observed that the elastic cylinder still has a small displacement due to the collapse of the bubble. At this dimensionless distance, although it has basically no effect on the deformation of the bubble, it still affects the deformation of the elastic boundary. The size of the dimensionless elastic cylinder will affect the bubble's shape. The analysis here assumes that the dimensionless distance is close or slightly close because when it is large, the shape of the bubble is not affected by the dimensionless radius of the elastic cylinder. In other words, the dimensionless distance \(\gamma\) has a greater impact on the bubble shape. When \(\gamma_{rod}\) is small, the bubble basically maintains a spherical shape during expansion and collapse, even if it touches or wraps the elastic cylindrical boundary, it will not affect the bubble's spherical shape. When \(\gamma_{rod}\) is slightly larger, during the bubble expansion process, when there is no contact, the bubble still basically maintains a completely spherical shape due to the elasticity of the boundary. When it collapses, the side away from the elastic cylindrical boundary remains spherical, but the side close to the elastic boundary will be more drop-shaped. When the bubble contacts or even wraps around the elastic cylinder, a slight deformation will occur during the expansion process. Both parts are basically spherical, but the radius of the end passing through the elastic boundary is smaller than the end away from it. This state continues until the bubble expands to its maximum or even in the early stage of collapse. In the late stage of collapse, the interaction between the bubble and the elastic boundary causes the bubble to have similar adhesion on the side close to the elastic boundary, and the collapse speed is significantly slower than that on the side away from it. At this stage, the bubble will appear in the shape of a bulb or mushroom.
## 3 Methodology
### Proposed Approach  
We tested several combinations of techniques and analyzed the impact on the results. Different embedders were tested considering the context (or not), different clustering techniques (partitional and hierarchical-based), and dimension reduction techniques. Their impact on metrics was analyzed. Figure 1 illustrates the pipeline we developed to run these experiments and Table 1 presents a brief overview of the used combinations for the experiments.  
The same preprocessing steps were used in every approach testing. They consisted of applying the intra-phrase segmentation algorithm, followed by lowercasing, tokenization, lemmatization, removal of punctuation, and stop words. Whenever additional input data were sent to the clustering method, the min-max method was used to normalize the categorical classes.  
The models' outputs will not be changed in terms of dimension reduction. Using that as a baseline: from 200 to 100 dimensions in steps of 50, from 100 to 25 in steps of 25, from 25 to 5 in steps of 5, and from 5 to 1 in steps of 1. In terms of datasets, the baseball dataset, since it is more recent, will be used as the primary dataset to test approaches. In contrast, the Cars dataset will be used as a validation dataset.
# Distributed Uniform Streaming Framework:
## 6 Platform Transparency Using Dust: A Connected Driving Use Case
### Application Component Architecture for Hardware Transparency  
The OBU-software is based on the event-processing model (as described by [PERSON] et al. (2018)) and implemented using the DUST-Core. We have no DUST Coordinators in the OBU because we need a fixed execution location for every application component (although this could be a future research direction). As discussed in the previous subsection, the software architecture needs to be able to handle large hardware changes with a minimal change in software. To handle this change in hardware, we construct applications with an application graph of multiple application components that stream events to each other. This software architecture allows us to create a strong hardware flexibility without changing the entire application (the processing components are reusable). When we change the event producer of consumer (e.g., the sensor hardware, actuator hardware, or the communication hardware), we only need to change the event producer and/or event consumer that is relevant and reuse the processing components. Figure 7 shows that we can implement multiple producers to read the information from the sensor and publish it in the same data format. By doing so, the hardware change is transparent to the other application components. Additionally, to changing the hardware, we can also replay stored sensor data in the same data format as the original sensor. This allows developers to stub the original sensors without the need for the physical sensors. Figure 7 also shows that the receiving actuators can also be interchanged if we write a new consumer application component to control the actuator with the same data format.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Managed by** & **Filter1/2 Start** & **Filter1/2 End** & **CPU Usage of** & **Messages** & **Total Network** \\
**DUST** & **Device** & **Device** & **Sensor/Actuator (\%)** & **Received** & **Usage (MB)** \\ \hline ✗ & Actuator/Sensor & Actuator/Sensor & 63.4/64.3 & 133,270 & **38.40** \\ ✗ & Sensor/Actuator & Sensor/Actuator & 81.6/46.2 & 133,130 & **32.60** \\ \hline ✓ & Actuator/Sensor & Actuator/Sensor & 64.5/63.4 & 132,718 & **39.00** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The global optimisation results.  
When we change the execution location of a processing application component to a different processing unit, we only need to change the configuration of the application components. This change in configuration will change the flow of events to include the different processing unit in the application execution graph without any change in the application components. This software architecture allows hardware flexibility without the need to restructure the entire application.
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 1 Introduction
This study presents an SPI system with active illumination in the near-infrared (NIR) wavelength range of 850-1500 nm. The system utilizes single InGaAs photodetectors to capture NIR-SPI images, which are the foundation for generating 3D human meshes based on point clouds for depth perception. In addition, it incorporates a ViT-based classification model to determine the initial pose to implement an SMPL-X-based reconstruction framework. Our approach processes point clouds to generate high-fidelity 3D human meshes, using a self-supervised learning framework [27] to enhance accuracy. We introduce a novel 3D human pose reconstruction method that surpasses previous state-of-the-art techniques by integrating a probabilistic human model with depth information. This innovation achieves a new benchmark in accuracy, outperforming ViT-based models and setting a new standard for handling missing data and low-resolution scenarios. The final output is a detailed and precise 3D representation of the human pose in the SMPL-X model, providing an accurate reconstruction of human figures in three dimensions. Therefore, in this work, we propose the following:  
* Exploring the capability of Single-Pixel Imaging for generating 3D human pose point clouds from low-resolution 2D images. * Implementation of the ViT (Vision Transformer) model applied to SPI images to define a pre-trained SMPL-X human pose model. * Testing a self-supervised Deep Learning model for 3D reconstruction from the point cloud. * This work addresses the challenging task of predicting 3D hand poses from low-resolution 2D images. This can be applied to sensors used in rescue operations where lighting conditions are difficult.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### MCS Selection  
For each transmission attempt and selected RBs, the gNB shall determine a single MCS. In particular, for a transmission attempt \(t\), the gNB shall find the highest MCS \(MCS_{t}\) that provides a block error rate (BLER) for a multicast group below the target value \(p_{t}\). The MCS selection procedure consists of two steps. First, the gNB uses the error model to find the highest MCS \(MCS_{SINR}\) that provides BLER below \(p_{t}\) for a given set of worst SINRs (i.e., \(SINR_{j}\) in the allocated RBs). Since the wireless channel may significantly change with time, the precoder and SINR estimations quickly become outdated, and \(MCS_{SINR}\) may not provide the required reliability. To address this issue, at the second step, the gNB adjusts the MCS as discussed in detail below.
## 4 Results
### Experiment in Corridor
#### 4.1.2 Loop Closure Experiment  
We conducted a loop closure experiment in a travel distance of 100 m. Figure 13a,b clearly show the maps and the camera trajectory generated by the proposed method and ORB-SLAM2. It is observed that, in Figure 13a, the map has been wrongly divided into two. As seen from statistical results of matching error between the models generated by the two methods in loop closure experiment and the ground truth LiDAR data in Table 4, the mean error of our method is 0.1722 m, whereas that of ORB-SLAM2 is 0.7337 m.  
Figure 12: Comparison of models constructed by two methods, with LiDAR data rendered in red. (**a**) Model constructed by ORB-SLAM2 and (**b**) model constructed by the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Max (m)** & **Mean (m)** & **Std. Dev. (m)** \\ \hline ORB-SLAM2 & 4.5549 & 1.1882 & 0.9833 \\ Proposed Method & 2.0370 & 0.0834 & 0.0992 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results of matching error between sparse maps generated by the two methods and the ground truth LiDAR data.  
Figure 13: Camera trajectory of the two methods in loop closure experiment. (**a**) Camera trajectory of ORB-SLAM2 and (**b**) camera trajectory of the proposed method.  
Figure 14 illustrates a comparison of the camera trajectories produced by the two methods in the loop closure experiment; the trajectory in red was generated by the proposed method, and that in white was produced by ORB-SLAM2. When the map segment optimization approach was applied, the continuity of the trajectory was affected, as shown in Figure 13. Poor continuity occurs when the camera's cumulative error is corrected by the LiDAR data. The quantitative statistical results are provided in Table 5. It can be observed from Table 5 that the total lengths of the trajectories of the same data set produced by two different methods are different because of the difference in the cumulative error in the camera trajectory. It is evident from this result that the proposed method has a closure error of 0.0977 m and an error percentage of 0.09%, whereas ORB-SLAM2 has a much larger closure error of 3.4247 m and an error percentage of 3.46%.
## 3 Results
### Effect of Aluminum Sulfate on Performance of Machine-Made Sand Concrete
#### 3.2.1 Effect of Aluminum Sulfate Content on the Slump of Machine-Made Sand Concrete  
The effect of 0%, 0.5%, 1%, and 2% of aluminum sulfate by weight of the cementitious materials on the properties of the concrete was studied. Figure 5 shows that the slump of fresh concrete decreased when the amount of aluminum sulfate was increased. This is because, when the Ca(OH)\({}_{2}\) was formed through the hydration reaction of the main minerals C\({}_{3}\)S and C\({}_{2}\)S in the OPC as shown in Equation (3), the existence of aluminum sulfate contributed to the formation of ettringite through the reaction with Ca(OH)\({}_{2}\), as shown in Equation (4) [44]. The existence of more ettringite decreased the workability and slump of concrete.  
\[3\text{CaO}\text{-SiO}_{2}\text{+nH}_{2}\text{O}\ \rightarrow\text{xCaO}\text{-SiO}_{2} \text{-yH}_{2}\text{O}+(3-\text{x})\text{Ca(OH)}_{2} \tag{3}\]  
\[\text{Al}_{2}\text{(SO)}_{4}\text{-18H}_{2}\text{O}+6\text{Ca(OH)}_{2}\overset{ \text{H}_{2}\text{O}}{\rightarrow}3\text{CaO}\text{-Al}_{2}\text{O}_{3}\text {:3 CaSO}_{4}\text{:32H}_{2}\text{O} \tag{4}\]  
Figure 4: Effect of AMF content on the permeability of concrete.