esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
### Limitations  
Although UMFNet demonstrates excellent performance in multiple benchmarks, there are still some limitations. Firstly, as shown in Figures A2-A8 of Appendix B, under extreme low-light conditions (below 0.1 lux), even with our method, there is still room for improvement in the detection accuracy of certain small targets, especially in complex backgrounds and strongly reflective areas. Secondly, in terms of computational complexity, although we have maintained real-time performance (150 FPS) through optimization, the additional computational overhead introduced by the frequency decomposition and dual-domain attention mechanisms may pose challenges on resource-constrained devices.
## 2 Materials and Methods
### Peeling Test  
Table 5 summarizes the peel strength testing results under a peeling force at different clamping distances. The peak elongation of the 50 mm clamping distance increased by 1.92%, and the overall elongation of the 50 mm clamping distance decreased by 2.45%, compared with the 25 mm clamping distance. Compared with the 50 mm clamping distance, the peak and overall elongation of the 100 mm clamping distance increased by 8.20% and 12.57%. As can be seen from the comparison, the overall elongation of the test samples fluctuates in a small range without evident change, and the peak elongation increases slightly with the change in clamping distance. The average peeling strengths of the 50 mm and 100 mm clamping distances were consistent, and the peeling strength of 25 mm is 3.15% lower. Therefore, the results of the junction peel strength test stay essentially with the increase in the distance between clamps.  
Figure 13 exhibits the plots of the peel stress-elongation of the junctions under different clamping distances. All of the curves followed the same pattern. The peeling strength in the linear area pre-peak developed slowly and required more deformation, whereas the strength in the area post-peak rapidly diminished. Under the action of a peeling force, Figure 14 shows the failure process of samples with varying clamping distances. It can be seen that all the test samples failed in the same way, with the failures occurring at the connection between the junction and the strip, indicating that direct failure of the junction under the action of a peeling force is implausible. The welded junction is prone to material deterioration during the welding process, and stress concentration occurs at the perforated area, resulting in this failure mechanism.  
Furthermore, some test results with considerable deviations were found during the experiment. The peel strength of these samples had decreased by 20% to 30%. They showed different fracture modes, and the welded-together strips were entirely separated at the junction. Figure 15 shows that the sample's welded junction had entirely separated due to the peeling force. Because this failure mode is uncommon, we consider it to be caused by the unqualified welding of the welded junctions during the production process.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Clamping Distance** & **Peeling Strength** & **Peak Elongation** & **Overall Elongation** \\
**(mm)** & **(N-cm\({}^{-1}\))** & **(mm)** & **(mm)** \\ \hline
25 & 106.27 & 14.592 & 27.33 \\
50 & 109.62 & 14.872 & 26.66 \\
100 & 109.62 & 16.092 & 30.012 \\ \hline \end{tabular}
\end{table}
Table 5: Peeling test results of the junctions.  
Figure 14: Failure modes of the junction subjected to a peeling force under various clamping distances: (**a**) 25 mm, (**b**) 50 mm, (**c**) 100 mm.  
Figure 13: Peel stress–elongation relationship of the junction subjected to a peeling force under various clamping distances: (**a**) 25 mm, (**b**) 50 mm, (**c**) 100 mm.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 2 Method
### Construction of Object Semantic Enhancement Model
#### 2.1.2 Object Semantic Model Under Multiple Cross-Attention  
This paper proposes an enhanced appearance semantic model of multi-layer feature fusion. By utilizing the multi-level feature representations of Layer2 and Layer3 of ResNet50 and adopting cross-attention learning and feature fusion strategies, the model achieves deep understanding and the accurate tracking of objects. The model can effectively extract and emphasize the most relevant features to an object, which helps the model accurately recognize objects among many similar objects and improve feature differentiation ability. Figure 3 shows the internal components of the multi-layer feature fusion network.
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 3 Front End (FE)  
The front end consists of a Kerr-lens mode-locked Ti:Sa oscillator, a negative stretcher, and a ten-pass preamplifier (Figure 1). The broadband pulses supporting a transform-limited duration of 7 fs are generated by a Kerr-lens mode-locked (KLM) Ti:Sa oscillator at 75 MHz repetition rate (Venteon Pulse One oscillator by Venteon Laser Technologies GmbH). The beam, actively stabilized for beam pointing (Figure 2), is steered into a transmission grating version of a single-grating folded stretcher [16]. The throughput of the stretcher is about 33%. The negatively stretched, \(\sim\)40 ps pulses are sent to the multipass Ti:Sapphire amplifier pumped by \(\sim\)25 mJ, 140 ns, frequency-doubled pulses from a Nd:YLF diode-pumped laser (DM-527-32 by Photonics Industries, Ronkonkoma, NY, USA).  
There are two further tasks that the FE needs to fulfil. First, it must pick the pulses from the 75 MHz pulse train at 100 Hz. Second, it must ensure proper dispersion control of the pulses prior to compression. The standard method for the latter is the use of an AOPDF (Dazzler by FASTLITE). In a usual architecture, the AOPDF is positioned before the (first) amplifier stage. However, the throughput of an AOPDF is in the range of a few ten percent. Hence, the energy of the pulses entering the first pass of the amplifier would be in the range of hundreds of pJ, resulting in a high amplified spontaneous emission (ASE) content of amplification. In many cases, this requires an additional pre-amplifier with a few tens of gain.  
In our design, we overcome this situation using a confocal (f = 500 mm), ring-type multipass amplifier with two layers (Figure 2). In the five passes of the upper layer (red line), the full train of pulses is amplified to about 1.1 mJ. Then, the pulses are sent through the AOPDF and a telescope to re-adjust the beam mode to the amplifier. The shaped pulses are amplified to 2.7 mJ in the next five passes of the bottom layer (violet line), resulting in a blue-shifted spectrum with a bandwidth over 50 nm and an energy stability better than 0.45% (RMS).  
The additional Pockels cell (Lasermetrics 5046 SC by FastPulseTechnology, Saddle Brook, NJ, USA), inserted just before the AOPDF, has an \"open\" time window of 10 ns. This selects the pulses at 100 Hz repetition rate from the 75 MHz pulse train with high extinction ratio (2000:1) pulses. Moreover, compared to the 140 ns long pump pulses, it  
Figure 2: The optical components of the front end, including the beam stabilization unit (BSM1, BSM2, BSD1, BSD2), the transmission grating (TG) stretcher, and the multipass confocal amplifier with concave mirrors (CV1, CV2).  
also prevents the evolution of parasitic laser modes in the confocal multipass amplifier and further enhances the (ASE) temporal contrast.
## 3 Results
### Fitting Residual  
The fitting residuals are calculated based on the clock offset time series. Taking satellites C35, C36 and C37 as example, the fitting residuals using \"one-hop\" reduction method and the whole-network adjustment method are shown in Figure 6.  
As shown in Figure 6, the fitting residuals of \"one-hop\" reduction method are fluctuating between \(\pm\)3.5 ns, and the RMSs of fitting residuals of satellites C35, C36 and C37 are 0.25 ns, 0.37 ns, and 0.38 ns, respectively. The fitting residuals of the whole-network adjustment method are fluctuating between \(\pm\)1 ns, and the RMSs of fitting residuals of satellites C35, C36 and C37 are 0.10 ns, 0.14 ns, and 0.13 ns, respectively.  
The RMSs of fitting residuals of all satellites are further calculated, which reflect the internal coincidence accuracy of fitting results. The RMS statistical results of clock offset fitting residuals of 27 satellites are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **SGL+ISL Method** & **WNA Method** & **Improvement Rate** \\ \hline C38 & 0.21 & 0.13 & 19.05\% \\ C39 & 0.20 & 0.12 & 19.05\% \\ C40 & 0.19 & 0.12 & 16.67\% \\ C25 & 0.34 & 0.13 & 50.00\% \\ C26 & 0.26 & 0.13 & 30.95\% \\ C27 & 0.34 & 0.13 & 50.00\% \\ C28 & 0.34 & 0.12 & 52.38\% \\ C29 & 0.35 & 0.13 & 52.38\% \\ C30 & 0.38 & 0.15 & 54.76\% \\ C19 & 0.32 & 0.12 & 47.62\% \\ C20 & 0.41 & 0.11 & 71.43\% \\ C21 & 0.28 & 0.10 & 42.86\% \\ C22 & 0.31 & 0.14 & 40.48\% \\ C23 & 0.39 & 0.15 & 57.14\% \\ C24 & 0.28 & 0.12 & 38.10\% \\ C32 & 0.36 & 0.13 & 54.76\% \\ C33 & 0.42 & 0.19 & 54.76\% \\ C34 & 0.31 & 0.12 & 45.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS statistical results of fitting residuals (unit: ns).  
Figure 6: Clock residuals.  
As shown in Table 1, the RMSs of clock offset fitting residuals of all satellites subject to \"one-hop\" reduction method between 0.19 ns and 0.42 ns, with a mean of 0.32 ns. The RMSs of clock offset fitting residuals of all satellites subject to whole-network adjustment range between 0.10 ns and 0.19 ns, with a mean of 0.13 ns. Compared with the \"one-hop\" reduction, whole-network adjustment improves the clock offset fitting residual accuracy by nearly 45.06%.  
It is noted that the improvement rate differs widely, the reason is that the value of all satellites subject to \"one-hop\" reduction method differs widely, and the value subject to the whole-network adjustment method is relatively small and close. This result further proves that the whole network adjustment method eliminates the equipment errors of different satellites.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 3 Acoustic Methods for Locating Buried Pipelines
### Seismic Wave Methods  
Seismic wave methods are well-developed detection techniques that have been widely used in reservoir (oil and gas) exploration [91]. In this case, hydrocarbon deposits usually are at a depth of hundreds of meters or even kilometres below the surface. The common depth point (CPD) stacking technique [92] is usually employed to image the location of reservoirs. However, these techniques are not suitable for detection of shallow-buried objects due to the shorter separation of travel time [93],  
Figure 10: Detection results of point vibration measurements: (**a**) resonance frequencies along measurements lines; (**b**) accelerate magnitude at the resonance along measurement lines [90].  
Figure 9: Point vibration measurements: (**a**) pipe rig prior to burial; (**b**) acoustic source (shaker); (**c**) measurement lines (the cross are for illustrative purposes only) [90].  
requirements of higher frequencies [94], and the presence of wave speed variation near surface [95]. Therefore, studies have been conducted in order to modify the seismic wave method for shallow depth detection [96; 97; 98]. More recently, research has been performed to assess the applicability of seismic reflection in buried pipe detection [99; 100]. Consequently, a compressional wave method and a shear wave method applied for locating buried pipes have been elaborated in [101] respectively. The detection results have been shown to be encouraging, suggesting the efficacy of using seismic wave methods in locating underground utilities.  
The basic principle behind seismic wave methods is briefly outlined in Figure 11. When the ground is mechanically excited, acoustic waves are generated and propagate away from the source point into the soil, after which they are scattered by buried objects with mechanical properties different from the surrounding soil. The scattered waves carrying the information of object location can be captured by an array of geophones at the ground surface. Accordingly, detection and analysis of these scattered waves allow the buried objects to be located [102].  
In principle, three main types of seismic waves coexist in the systems of buried objects, including compressional wave, shear wave and Rayleigh wave. This leads to three seismic wave methods including a compressional wave method, a shear wave method and a surface wave method, each of which has one predominant wave excited by selecting the specific forms of excitation.
## 3 Results and Discussion  
### Structural Characterization  
#### 3.1.1 Ssm  
Figure 2 presents the SEM image that demonstrates the morphology of the untreated and treated layered silicates, where the layered structure between them can be compared. For MMT and REC, their platelet edges were generally curled, and the layered structures were unclear in Figure 2a,b, chiefly because of the irregular edges. After organic modification, layered structures were clearly observed; meanwhile, platelet edges were found to be regular without any remarkable curls. This demonstrates that the organic reagent embellishes irregular edges. More precisely, organic reagent molecules are able to form tight ion pairs with the layered silicates by adsorbing negative charges that exist on the surface, as well as to enter interlayers of the layered silicates through the reaction with exchangeable cations [17; 21]. By comparing Figure 2c,d, it can be obtained that OREC had a larger-sized platelet than OMMT. Furthermore, both OMMT and OREC exhibited an intercalated structure without an exfoliated platelet, which can be further validated by the XRD test.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 9 Preliminary Analysis C: Evaluating the Hotspot Scenario
### Evaluation Scenario  
The evaluation scenarios are similar to those in Sections 7.1 and 8.3, since there are three eNBs equally distanced by 1000 meters. In this analysis, scenarios 1 and 2 have uniformly distributed users, while scenario 3 also has 5 hotspots with 15 users each. Figure 15 illustrates the approximate position of each hotspot, which are not random. The differences between these scenarios are summarized in Table 7.  
There are two scenarios without hotspots, but with different amounts of users. As a result, it is possible to evaluate the impact of hotspots that increase (or not) the total amount of users in the system. Other simulation parameters are presented in Table 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scenario 1** & \\ \hline
60 uniformly distributed users and no hotspots & \\ \hline
**Scenario 2** & \\ \hline
135 uniformly distributed users and no hotspots & \\ \hline
**Scenario 3** & \\ \hline
135 users: 60 uniformly distributed users and & \\
5 hotspots with 15 users each & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Difference between hotspot scenarios.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Bandwidth (RBs) & 100 \\ \hline UE distribution & Uniform / Hotspots \\ \hline Number of UEs & 60 / 135 \\ \hline Distance between eNBs (m) & 1000 \\ \hline Scheduling algorithm & _Proportional Fair_ \\ \hline Simulation duration & 6000 subframes \\ \hline Channel model & Friis Model \\ \hline Error model & MIESM \\ \hline UE mobility & No mobility \\ \hline Traffic model & Non-GBR TCP-based \\ Video (Buffered Stream) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Simulation parameters: hotspot scenarios.  
Figure 15: Scenario for preliminary analysis C with the approximate position for each hotspot, which are not randomly located.
# Pain Perception during Orthodontic Treatment with Fixed Appliances
###### Abstract  
The present study aimed to determine the intensity of Pain perception in patients undergoing fixed orthodontic treatment. We analyzed the severity of pain concerning four routine procedures: the placement of separating elastics, ring cementations, arch activations, and elastic tractions. Our study consisted of a sample of 100 patients between 12 and 35 years old during the initial months of orthodontic treatment with fixed appliances. The patients completed a questionnaire meant to assess their pain sensation perception. The study sample was divided according to age and sex. By determining the intensity of pain felt during the four orthodontic procedures, we found that the most painful one was the ring cementation in all four age groups. The therapeutic-arch-activation procedure ranked second, with a higher mean value (2.66) in the 18-24 age group; the least painful was considered the elastic traction procedure, with a higher value (1.33) in the group over 30 years old. The most painful period was during the first 3-4 days after procedures. Most patients showed moderate pain after following the studied orthodontic interventions and required analgesic medication, the most frequently used being Nurofen, ketonal or paracetamol. The level of pain felt was significantly higher in men than in women. Patients suffer differently from the intensity of perceived pain as they grow older.  
pain perception; fixed appliances; orthodontic treatment 2022
# Longitudinal Seismic Response of Continuously Welded Track on Railway Arch Bridges
## 3 Influence of Track Constraints on Arch Bridge Seismic Response
### Comparison of Pier Top Displacement  
Pier top displacement was used to assess the effect of seismic activity. Given the huge amount of data generated from the models, only the highest pier column, P7, is used as an example. The pier top longitudinal displacement over time is shown in Figure 7a, and the maximum displacement of each pier top is shown in Figure 7b below.  
As shown in Figure 7, when the track constraints were considered in the calculation for a deck arch bridge, the pier top longitudinal displacement under seismic action was greatly reduced. In Figure 7b, the maximum pier top longitudinal displacements of piers P6-P13 were 14.6, 9.6, 8.1, 15.0, 22.8, 22.8, 23.7, and 24.2%, respectively--obtained when no track constraints were considered. In the analysis of simply-supported beam bridges, the pier top displacements were about 19-62% when no track  
Figure 5: Longitudinal rail force envelope. (**a**) Tensile force; (**b**) compressive force.  
Figure 6: Rail expansion force.  
constraints were considered [17]. This is chiefly because the arch bridge pier column stiffness is generally less than the average pier stiffness; while the constraint of the track longitudinal resistance on beams in an the arch bridge is greater than that of supporting beam bridges, resulting in significant reduction in pier top displacement. Since the column pier deformation directly affects the structural forces (such as pier shear and bending moment, rib axial force and bending moment, etc.), it is proposed that track constraints should be taken into consideration in railway arch bridge seismic design.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 3 Proposed Method
### DIFSCL Method
#### 3.2.1 Crop Data Augmentation  
In DIFSCL, we employ a data augmentation technique that involves random cropping and scaling, applied to a \(9\times 9\) input patch. We preserve the initial proportions of the patch while randomly cropping a portion of the image and then scaling it back to the original \(9\times 9\) size. Subsequently, the image is either horizontally or vertically flipped with a certain probability. Specific parameters include: the cropped area, determined by the parameter scale = (0.6, 1.0), and the aspect ratio of the crop, determined by aspect ratio = (6.0/9.0, 9.0/6.0). This indicates that the cropped area ranges from 60% to 100% of the original \(9\times 9\) patch area, and the resulting aspect ratio of the cropped area ranges from 6/9 to 9/6. If the cropped length or width exceeds the original dimensions, the cropping is adjusted to the original length or width. The values specifying the cropped area and aspect ratio are  
Figure 1: Framework of the proposed method.  
drawn from a uniform distribution. The cropped patch is then resized back to a \(9\times 9\) spatial dimension using cubic spline interpolation. After each cropping, there is a 50% chance that the patch will be flipped either horizontally or vertically, with the direction of the flip randomly chosen. The diversified views provided by crop are more in line with the goal of contrastive learning, helping the model better distinguish between positive and negative samples.
# A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing
## 5 Discussion on SI, PCT, and the User's Interactions  
SI has been used as a valuable KPI for QoE estimation [11]. In Reference [26], it is established that the web users keep interacting with a web page beyond the first page-load which influences the QoE of interactive web applications. However, SI estimates the speed of the first page load [9]. Consequently, if the video of web browsing extends beyond initial ATF to include further user interactions, the estimated speed may not be an accurate representation of the loading speed. This is illustrated by the extended above-the-curve area (gray area after the sky blue line) in Figure 12.  
A possible solution to this is splitting the video of web browsing into multiple parts based on the user's interactions. Then, compute the SI for each interaction. This forms an array of SI estimates where each element represents the speed of loading associated with the user's interaction. However, similar to the first page load, the existence of animation in the ATF area may negatively impact the speed estimation of SI. In Figure 13, Cumulative SI (CU SI) is computed to illustrate how the speed estimation of SI is artificially inflating and increasing when a user keep interacting with a single page AJAX application.  
The previous section (Section 4) established that using PCT as an ATF measure in SI solves the inaccurate measurement of SI caused by animated content. Thus, SI upper bounded to PCT can be used to measure the speed of interactions without being concerned about the content type.  
Motivated by the concept of iLT that we proposed in Reference [26], the term interactive SpeedIndex (iSI) can be introduced. As shown in Figure 13, iSI represents computation  
Figure 12: VC progress calculation for an entire web mapping session. The blue line shows the visual progress over time. While the sky blue vertical line is the time that objective ATF occurred, the purple vertical lines show the user’s subsequent interactions.  
of SIs associated with a user's interactions for the entire web browsing session. It can be seen that iSI has a potential to demonstrate an accurate distribution of speed of the user's interactions in compare to SI computed for the entire session.