esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Experiments
#### 2.5.2 Field Draught Force Test  
Figure 8a demonstrates the testing system used in the field draught force test, which consists of a tractor (YYX704, Jiangsu Yueda Intelligent Agricultural Equipment Co. Ltd., Yancheng, China, Figure 8a), a 4U-110 potato harvester and a field kinetic parameters telemetry system (Bona Science & Technology Co. Ltd., Harbin, China). The maximum range of the three force sensors used in the test is 30 kN. The values of the draught force under steady state are chosen as the experimental results, and each test is repeated three times. As shown in Figure 8b, the length of the ridges in the field draught force test is 150 m. Considering the instability of the tractor's speed during the beginning and ending phase, each ridge is equally divided into three parts, and every part consists of a 10-meter-long preparation area, S\({}_{1}\), a 30-meter-long sampling area, S\({}_{2}\), and a 10-meter-long finishing area, S\({}_{3}\).  
Figure 7: Soil cone index in field experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer (m)** & **0–0.10** & **0.10–0.20** & **0.20–0.30** \\ \hline VWC (\%) & 25.63 & 27.75 & 30.44 \\ Bulk density (kg/m\({}^{3}\)) & 1360 & 1350 & 1570 \\ \hline \hline \end{tabular} Note: The soil moisture content is measured by oven drying method and calculated as VWC (%) = (the wet weight of soil sample-the dry weight of soil sample)/(the volume of soil sample \(\times\) the density of water) \(\times\) 100%.  
\end{table}
Table 3: VWC and bulk density of different soil layers in field experiments.
## 3 Results  
### Summary of the Teachers' Testimony  
Each teacher filled a structured form exploring different perspectives of their experience. The remarkable diversity of opinions and perceptions among the teachers was notable. These differences likely arose from the varied backgrounds in their respective fields, the lesson content, the resources utilized, and the intrinsic dynamics of each discipline. Notwithstanding this heterogeneity, all four teachers concurred that implementing eye tracking to provide real-time gaze in educational interactions holds significant merit. Each asserted that eye tracking was undeniably beneficial and facilitated interaction with the students.  
A second point of consensus among the four teachers was that they, indeed, could identify the main targets of visual attention. They stated that this feature could be useful for identifying periods of students' difficulties, confusion, and uncertainties. Thus, they could immediately intervene and provide more guided, direct, and precise feedback to support the student. This may help to keep the student's attention and engagement. One teacher wrote: \"I could perceive when the students' gaze was deviating from where I would like it to be. So I could bring it back by saying some words or using the mouse pointer so they could better identify the important targets\".  
Figure 4: Biology eye-tracking activity. Screenshot from video showing student’s gaze path during one of the activities.  
In addition, there were differences in the figure analysis and reading patterns depending on the student's age. The teachers mentioned that they could observe the attention spent on each different visual information provided as well as the students' engagement. Moreover, the teachers commented that they could also infer distractions, curiosity, and confidence in the students' answers to inquiry. Quickly noticing the student's distraction and providing immediate intervention was also useful.  
It was reported that using one-to-one (teacher-student) real-time gaze to investigate educational resources that can be later used in the classroom (thus without eye tracking, in a one-teacher-to-many-student scenario) is also relevant. The teachers mentioned that this investigation could help them adjust the resource design and the difficulty level of the questions. The insights provided could support the design of clearer examples and offer more rich interactions.  
Furthermore, they mentioned that there are situations in which understanding students' main difficulties is very helpful in identifying how to use different strategies. Moreover, they also see great potential in inclusive education (emphasizing nonverbal students or cases of Autism Spectrum Disorder).  
Finally, all teachers highlighted individualized interaction as a characteristic of a good lesson with real-time gaze.
# Damage Imaging in Lamb Wave-Based Inspection of Adhesive Joints
## 3 Results and Discussion  
### Dispersion Curves  
Before analyzing the defects in the lap joints, the characteristics of Lamb waves were determined for two specimens: a single steel plate with a thickness of 3 mm and dimensions of 240 mm \(\times\) 300 mm, and two steel plates that were adhesively bonded with an adhesive layer with a thickness of 0.2 mm. In each plate, a five-cycle Hanning windowed sine function with center frequencies of 50 kHz, 100 kHz, and 200 kHz was used as an excitation signal. Out-of-plane vibration signals were measured in 101 points that were evenly distributed along a line with a length of 10 cm. The wavenumber-frequency relations (Figure 6) were obtained using 2D-FFT (cf. [23; 38]). In the experimental results, theoretical dispersion curves that were obtained using GUIGUW software [39] based on the semi-analytical finite-element formulation [40] were superimposed.  
In general, in the single plate, both S\({}_{0}\) and A\({}_{0}\) modes can propagate in the considered frequency range up to 200 kHz. However, the applied actuator utilizing the thickness-wise expansion effect enabled mainly exciting the fundamental A0 mode and the influence of the S0 mode was insignificant, as it can be seen in Figure 6a. The group velocities of A\({}_{0}\) mode were 2981.5 m/s, 2575.7 m/s, and 2062.7 m/s for the excitation frequencies of 200 kHz, 100 kHz, and 50 kHz, respectively, giving the wavelengths values of 14.9 mm, 25.7 mm, and 41.3 mm. Figure 6b shows the dispersion curves for two adhesively bonded plates. In this case, three modes could propagate in the frequency range up to 200 kHz (S\({}_{0}\), A\({}_{0}\) and A\({}_{1}\)), however, in the performed experiment, the A\({}_{0}\) mode was dominant. It was noted that the A\({}_{0}\) mode dispersion curves were similar for both plates, indicating that this mode will transmit across the waveguide transitions. The group velocities of the A\({}_{0}\) mode propagating in two adhesively bonded plates were 2865.6 m/s, 2448.5 m/s, and 2045.8 m/s for the excitation frequencies of 200 kHz, 100 kHz, and 50 kHz, respectively, giving the wavelengths values of 14.3 mm, 24.5 mm, and 40.9 mm.
# Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation
## 3 Proposed Model
### Complexity Analysis  
In this section, we analyze the theoretical complexity of GAFSRec. Since the time complexity of LightGCN-based convolutional graph encoding is \(O(L\times|\mathbf{R}|\times d)\), the total encoder complexity of this architecture is less than \(4\times O(L\times|\mathbf{R}|\times d)\) because the adjacency matrix of the auxiliary encoder is sparser than that of the user-item interaction graph. The time costs of the gate function and the aggregation layer are both \(O(m\times d^{2})\). This architecture adopts BPR loss; each batch contains B interactions, and the time cost is \(O(2\times B\times d)\). Since the contrasting between positive/negative samples in contrastive learning increases the time cost, cross-layer self-supervised contrastive learning contributes a time complexity of \(5\times O(B\times M\times d)\), where M represents the number of nodes in a batch. Since this model does not involve graph augmentation, the complexity of GAFSRec is much lower than that of graph-augmented social recommendation models. In our experiments, with the same embedding size and using the Douban-Book dataset, MHCN took 34 s per epoch and GAFSRec only took 11 s. Detailed information on the experiments can be found in Section 4.2.1.
## 1 Introduction
In this study we investigated, by means of Raman microspectroscopy technique and PCA, the biochemical effects on PBLM cells isolated from the blood obtained by a set of healthy human donors and exposed to 1.8 GHz EMFs for 5, 12 and 20 h. The main difference between the present investigation and that reported in [13] is the different number of healthy subjects from whom blood the PBLM cells were isolated. In particular, in this work, PBLM cells were obtained from the blood of many people, whereas in our previous work [13] each exposed and unexposed blood sample, for a given time, came from one single healthy subject. Biochemical modifications, consisting in the decrease of the DNA to protein intensity ratio in the exposed cells with respect to the unexposed ones, particularly after 20 h exposure time were found. These changes occurred with a SAR value of 0.21 W/Kg, which is lower than the limit value for public exposure established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) [19; 20]. Therefore, it is important a careful investigation about the currently existing safety standards occurs.
## 4 Simple Theoretical Model  
The equivalent throughput (\(R_{b,eq}\)) is the main metric for evaluating the performance of a data muling solution such as GROW. This is defined by Equation (1), which considers the transferred data (in bits) between the SU and the CSU over the time (in seconds) the data took to be transferred.  
\[R_{b,eq}=\frac{Datasize}{T_{DR}} \tag{1}\]  
In Equation (1), _Datasize_ is the number of bits transferred. \(T_{DR}\) is given by Equation (2) and depends on the following: (1) the undocking time \(T_{u}\), which represents the time for the DMU to move away from the CSU or the SU; (2) the travel time \(T_{t}\), which in turn depends on the distance between the SU and the CSU and the travel speed of the DMU; (3) the number of DMUs \(N\) available; (4) the docking time \(T_{d}\), which is the time that the high precision acoustic relative positioning and maneuvering system takes for approaching and accompanying the SU or the CSU; and (5) the transfer time (\(T_{SR}\)), which is the time required  
Figure 5: Block diagram of the iterative Extended Kalman Filter: prediction step and update step upon sensor measurement.  
for the file (or a chunk of the file) to be transferred over the short-range and high speed underwater link. In turn, the transfer time depends on the data size and the short-range link throughput. When compared with a typical communications system, the docking, undocking, and travel times can be seen as the propagation delay, while the short-range transfer to and from the DMU can be seen as the transmission delay.  
\[T_{DR}=T_{u}+T_{t}+N\times\left(T_{d}+\frac{T_{SR}}{N}+T_{u}\right)+T_{t}+T_{d} +\frac{T_{SR}}{N} \tag{2}\]  
Despite being a simple deterministic model, without localization errors or other external factors are considered, this simple model shows the theoretical limits of a data muling solution and establishes a baseline for performance comparison.
## 4 Results
### Scarp and Slope Deformation  
Figure 8 shows the 5 m- and 1 m-resolution clinometric maps of the vicinity of the north portal of Dongao Tunnel, along with the orthoimage that was produced by superimposition of an aerial image on a 1 m-resolution DEM. In this small area of 530 \(\times\) 500 m, the 5 m-resolution clinometric map was quite vague, and only the outline of the river and slope could be seen on it. Micro-landform features, such as scarps and erosion gullies, were difficult to identify, as was the forest road. In the 1 m-resolution clinometric map (Figure 8b), not only the geomorphic features of the river and terraces in the orthoimage (Figure 8c) but also the vegetation could be clearly observed, the latter as the shaded part of the forest road with a width of 2-4 m. Scattered scarps and gullies were also clearly seen. Figure 8d,e plots the topographical profiles, extracted from the 1 m-resolution DEM, along the south-bound and north-bound lanes of the Dongao Tunnel. Fine topographic features, such as the erosion gullies and scarps, were clearly depicted, favoring the interpretation of the boundary of a slope with deep-seated gravitational deformation.  
Figure 9 shows the slope with deep-seated gravitational deformation that is shown in Figure 8b, as it was found on site. The slate was twisted with open cleavage; collapsed debris had accumulated to form small lauses under scarps (Figure 9a). The cleavage of slate had a dip of about 30\({}^{\circ}\), which was significantly lower than the high angle of the cleavage that was measured for a nearby slate. However, the site was densely planted and identifying the scope of gravitational deformation was impossible.  
Figure 7: On-site investigation of the Houishan Fault. Images taken near the erosion gullies on the eastern coast (**a,b**) and the left bank slope of the Zuntoukeng River (**c,d**).  
Figure 8: Clinometric maps with 5 m- (**a**) and 1 m- (**b**) resolution, and an orthoimage (**c**) near north portal of the Dongao Tunnel. Topographic profiles along the north-bound (**d**) and south-bound (**e**) lanes of the tunnel, extracted from the 1 m-resolution DEM.  
Figure 9: Images taken near the boundary of a slope with a deep-seated gravitational deformation. (**a**) Debris piled-up to form a talus under a scarp. (**b**) A slate twisted with an open cleavage.
## 4 Specific Prenylquinones
### Vitamin K
#### 4.2.2 Menaquinone in Parasitic Protozoa  
In _P. falciparum_, MQ-4 biosynthesis has been characterized by [PERSON] et al. [60], but no putative enzymes have been identified for the biosynthesis of this compound. However, several MQs are also found in diatoms and red algae, the supposed endosymbiont apicomplexan parasitic ancestors so this could be the origin of MQ in _Plasmodium_[45; 51; 178].  
[PERSON] et al. [60] performed metabolic labeling experiments using radiolabeled GGPP, which did not elucidate whether MQ-4 is biosynthesized from chorismate or is just a product of isoprenic chain modification of host-incorporated vitamins [188]. Furthermore, the functions of MQ-4 in _Plasmodium_ appear to be complex and are poorly studied. NADH type II dehydrogenase that was chemically isolated in vitro from _P. falciparum_ extracts or expressed in bacteria was shown to be able to use MQ-4 and menadione (vitamin K aromatic head group), respectively [60; 61]. It was also observed that the UQ/MQ ratio was apparently inverted when the culture was maintained under anaerobic/microaerophilic conditions. RO 48-8071 (4-bromophenyl [2-fluoro-4-[[6-(methyl-2-propenylamino)hexyl]oxy]phenyl]-methananone), an inhibitor of MQ biosynthesis, apparently produced an increase in the UQ pool while decreasing MQ biosynthesis [60].  
In trypanosomatis, the biosynthesis of vitamin K has not been characterized. However, several _Trypanosoma_ and _Leishmania_ species have putative sequences for vitamin K epoxide reductase (VKOR) enzymes (_T. cruzi_, UniProt: Q4 DT49; _Leishmania (L.) donovani_, UniProt: E9 BJE1). Based on these findings, it would be interesting to explore vitamin K biosynthesis or the incorporation of vitamin K from external environments for trypanosomatid metabolic functions.
## 4 Experiments
### Comparison  
#### 4.6.1 Results of CrowdHuman  
The proposed detector is trained on the CrowdHuman training set, and then, compared with other state-of-the-art methods on the challenging crowded dataset. Table 4 shows the comparative results of the CrowdHuman verification set, where \(w\) (_w/o_) represents with (without) suppression factors, and the best results are shown in boldface. The results clearly show that our MA-NMS outperforms the other NMS methods [40, 48] in terms of the _Recall_ metric, even with only the application of strong and weak suppression. This is due to the fact that the adaptive application of strong and weak suppression allows for a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(N_{t}\) & **AP** & **Recall** & \(\text{MR}^{-2}\) & **FPS** \\ \hline Greedy NMS & 0.5 & 85.0 & 88.1 & 44.8 & **10.75** \\ Soft NMS [16] & 0.5 & 86.6 & 90.8 & 44.5 & 10.62 \\ Adaptive NMS [17] & 0.5 & 87.3 & 90.0 & 45.2 & 10.45 \\ MA-NMS (ours) & 0.5 & **90.2** & **94.6** & **42.0** & 10.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different NMS algorithms equipped on Faster R-CNN. MA-NMS—Multi-Attribute NMS.  
Figure 5: Results of MA-NMS with varying values of exponential constants. (**a**) Hyperparameter \(C_{1}\); (**b**) hyperparameter \(C_{2}\); (**c**) hyperparameter \(C_{3}\). The higher the \(AP\) and _Recall_, the lower the \(MR^{-2}\), and the better the result.  
divide-and-conquer strategy that effectively suppresses duplicate proposals while retaining those of occluded pedestrians, which is challenging to achieve using the single uniform suppression operation employed in other NMS methods [40,48]. Furthermore, with the application of suppression factors, MA-NMS outperforms all the competitors, especially considering its 3.3% improvement in \(MR^{-2}\) compared to IDADA [49]. Despite IDADA [49] utilizing data augmentation to obtain high-quality proposals, more duplicate proposals are incorrectly retained by its Greedy NMS, making IDADA [49] more sensitive to false positives than MA-NMS, and a similar dilemma is also faced by method the method used in [50,51]. Moreover, the authors of [39,52] trained their models to more accurately generate bounding boxes to assist Greedy NMS in identifying duplicate proposals; however, their assistance is limited in crowded scenes due to ubiquitous intra-class occlusion, and they lack the adaptive suppression capability of MA-NMS, ultimately resulting in 3.6% and 6.2% lag in _Recall_ compared to MA-NMS. Furthermore, MA-NMS gains 1.4% improvement in _AP_ compared to JointDet [18]. This can be attributed to MA-NMS effectively leveraging attribute information in pedestrian features for suppression, which is absent in JointDet [18], despite its attempts to extract more robust pedestrian features. These results validate the effectiveness of MA-NMS in improving the performance of generic pedestrian detectors in crowded scenes, making it a competitive choice over other advanced models [18,49,50,51] in crowded scenes.
# A Ku-Band Fully Differential Low-Power High-Input TdB Low-Noise Amplifier
## 2 Circuit Design
Figure 3 represents input power versus output power characteristics and amplitude-to-amplitude  
Figure 1: Schematic of the proposed low-noise amplifier. Figure 2: Simulated noise figure versus (**a**) transistor width and (**b**) number of transistors connected in parallel for same device size. (AM-AM) distortion levels with respect to bias variation at 14 GHz. Considering changes in gain according to class variation, as well as P1 dB and AM-AM distortion, a 0.23 V class AB level was chosen. To ensure stability while maintaining gain performance, neutralization capacitors (C2 and C4) were adopted [21; 22; 23]. Additionally, inter-stage gain was boosted by introducing L2 at the output of M1 and L3 at the input of M2 [7; 17]. To achieve input, inter-stage, and output matching, three transformers (TF1, TF2, and TF3) along with shunt capacitors (C1, C3, and C5) were utilized. A top metal layer with a thickness of 3.4 \(\upmu\)m was used to construct all transformers, which is the thickest metal layer with low resistance and small silicon substrate-related parasitics. The turns ratios were 1:1.5, 1:1, and 2:1. Their metal width was designed to be 3 \(\upmu\)m and the metal spacing was 2 \(\upmu\)m. Electromagnetic simulation was performed to check the coupling coefficient of the transformers. The average values of 0.674 for TF1, 0.686 for TF2, and 0.68 for TF3 were confirmed.
## 2 Experimental Section  
### Fringe-Resolved Autocorrelation  
Figure 1 shows a block diagram of the instrument used in this study. In measuring an AC trace, a fundamental beam of a Ti:sapphire laser (Elite, 800 nm, 35 fs, 1 kHz, 4 mJ, Coherent) was passed through a Mach-Zehnder interferometer, consisting of beam splitters made of BK7 plates with a thickness of 3 mm. Using a lens (BK7) with a focal length of 300 mm, the aligned beam was focused into argon gas contained in a cell (700 mm long, 10 mm i.d.) equipped with fused silica windows with a thickness of 0.5 mm. The third harmonic emission generated in the optical components such as the beam splitters was suppressed by passing the beam through a cover glass for microscopy and was confirmed to be negligible levels by evacuating the gas cell. The third harmonic emission generated in the argon gas was passed through an aqueous solution of NiSO\({}_{4}\) (500 g/L in water) to suppress the fundamental beam; a substrate of Si was used to remove the fundamental beam of a Ti:sapphire laser in the reported work [16]. The THG beam emitting at 267 nm was further isolated using a monochromator and was measured using a photomultiplier (R1332, no response at 800 nm, Hamamatsu Photonics) designed for measuring a large optical pulse. In the experiment, the signal intensity was measured at the level sufficiently lower than the level of signal saturation. The output signal was fed into a boxcar integrator, and the averaged signal was recorded using an oscilloscope. A function generator provided a signal for a piezoelectric transducer equipped with a translational stage with a retroreflector mounted on it. An autocorrelation trace was collected using the oscilloscope by recording the signal against the time delay between the pulses.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
###### Abstract  
In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.  
machine vision; laser spot; deep learning; convolutional neural network +
Footnote †: journal: Applied Sciences
# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 4 Algorithms
### A Genetic-Algorithm-Based Path Planning with VRAOA (GA w/VRAOA)  
A genetic algorithm (GA)-based [31; 32] path planning algorithm with the detour point selection algorithm VRAOA (GA w/VRAOA) is designed to solve the OCDM problem. In GA w/VRAOA, first, sensors are clustered using the K-means algorithm, where the number of clusters is the same as the number of UAVs; i.e., a UAV is assigned to a cluster of the sensor. Then, for each UAV, the near-optimal trajectory is obtained by applying a GA that uses VRAOA. GA is a meta-heuristic algorithm that consists of four main operations: population initialization, selection, crossover, and mutation.