esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Assessment of the Transformation from Industry 3.0 to 4.0 According to RAMI
#### 3.5.2 RAMI 4.0: Hierarchical Levels in Implementation
The methodology and integrated components are in line with what the referential model proposes for many of its key aspects, such as interoperability, flexibility and automation. The lack of full use of certain layers, such as product and service, does not invalidate the relationship with the referential model, as this framework is flexible and adaptable to different industrial scenarios. Therefore, it is correct to say that the work is based on the reference model architecture, using most of its layers and tiers to implement an effective solution aligned with Industry 4.0 principles. However, the Discussion section proposes certain criteria that could be incorporated into this study to cover the missing layers. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Business** & **Functional** & **Information** & **Communication** & **Integration** & **Asset** \\ \hline \multirow{8}{*}{**Product**} & \multirow{8}{*}{Limited use due to focus} & Process & Data & & & Physical \\  & & Orchestration: Task & Management: Critical & Communication Protocols: MQTT & product and its digital representation \\  & & & orchestration is ensured from & on orders & communication & applicable & through digital twins in cloud \\  & & & and products & & & in cloud \\  & & final process on & stored in & & & services infrastructure \\  & & & & & & \\ \hline \multirow{8}{*}{**Field**} & \multirow{8}{*}{Limited use due to production focus} & Interoperability: FC node and legacy systems & Sensor data on CPU load & Synchronization and on CPU load & Technology & Sensors and Integration: Field devices & Sensors and actuators that are part of the control \\  & & & legacy systems & and & AS-i and MQTT & integrate with the system, protocols & both legacy & system, including FCS nodes and PLCs \\ \cline{
## 6 Simulations and Testbed Experiments
### Simulation Results
#### 6.2.5 Simulation Results Summary  
In summary, the above simulation results show that the CDSAs we proposed can reduce energy significantly. The CDSAs can effectively reduce energy consumption in the Idle, Receive and Transmit  
Figure 13: Delay of the three schemes: (a) Average delay versus the nodes’ hop. (b) Average delay versus the number of descendants. (c) Average delay at each round.  
states through the increase of sleep time. In terms of buffer occupancy and delay, O-CDSA is sensitive to the traffic load, and T-CDSA is more robust to the traffic load. So, O-CDSA prefers a light workload application rather than a heavy workload. While T-CDSA can be widely used to many application. In comparison with O-CDSA, T-CDSA presents better network performances and is much more stable. The performance of O-CDSA is not as good as that of T-CDSA due to the absence of estimating Sleep state of the next-hop. However, the T-CDSA requires two updating processes at the SU stage, while O-CDSA requires only one updating process. This shows that O-CDSA is simpler than T-CDSA. Therefore, the SU process of O-CDSA can also be used in a light workload application because it has only one upstream update process. This is to say that the two CDSAs can apply to different applications based on the real application.
## 3 Results
### Cavity of Length \(L=\lambda/4\)
#### 3.1.2 Several Source Amplitudes  
To determinate which medium is more convenient to obtain more harmonic amplitudes for a given \(f\), we change both \(N_{g}\) and \(v_{0g}\) (thus also \(R_{0g}\) and \(f_{0}\)) by keeping \(v_{v}\) constant at \(v_{v}=3.2725\times 10^{-5}\). Figure 2 shows the maximum amplitude of the 2 nd, 3 rd, and 4 th harmonics vs. ratio \(f_{0}/f\) for \(p_{s}=20\) kPa, \(p_{s}=22.5\) kPa, and \(p_{s}=25\) kPa. The higher is ratio \(f_{0}/f\) (medium with more smaller bubbles), the higher are the harmonic amplitudes. When \(f_{0}/f\) increases from 3 to 20 the harmonic amplitudes grow quickly, whereas for values of \(f_{0}/f\) from 20 to 65 their growth is slower and ends up to be constant. This behavior is similar for the three \(p_{s}\) studied here, although, as expected, the harmonic generation is more intense for larger \(p_{s}\).  
Figure 1: Amplitude of \(f\) (black line), \(2f\) (blue line), \(3f\) (green line), and \(4f\) (red line) in the cavity of length \(L=\lambda/4\) vs. for \(p_{s}=20\) kPa.  
Figure 2: Maximum amplitude of \(2f\) (**a**), \(3f\) (**b**), \(4f\) (**c**) in the cavity of length \(L=\lambda/4\) vs. \(f_{0}/f\), for three different source amplitudes \(p_{s}=20\) kPa (blue line), \(p_{s}=22.5\) kPa (green line), and \(p_{s}=25\) kPa (red line).
## 3 Results and Discussion
### Injection Rate Characteristics
The higher viscosity of biofuels reduces the harmful effects of cavitation inside the injectors, allowing them to avoid significant damage and malfunctions. Consequently, the negative effect of cavitation on the flow within the injector, spray formation, and atomization of vegetable oil might be less critical, even for a cylindrical nozzle's hole. Although the density of rapeseed oil is higher than that of diesel fuel (Table 2), the latter exhibits a higher mass injection rate than RO at lower injection pressures of 24.0 and 40.0 MPa, at the very least. The maximum mass flow rate of RO remains still 13.9% and 12.8% below that of diesel fuel at injection pressures of 24.0 and 40.0 MPa, respectively. The difference is clearly visible at the maximum flow rates when the needle is fully open, and the injection process is stable. This phenomenon makes a difference associated with the results obtained by [PERSON] et al. [26] where the maximum mass flow rate of diesel fuel was approximately 6-7% higher than gasoline, which is the same value as the squire root of the densities. It should be noted that in that study the maximum injection pressures were 1200 and 1800 bar and, therefore, the effect of density, which was 13.6% higher than that of gasoline, on the mass flow rate could be relatively greater. As could be expected (Figure 3b), the differences in maximum mass flow rates between diesel fuel, rapeseed oil and fuel blends become negligible at the higher injection pressures of 60.0 and 80.0 MPa due to the roughly 10% higher density of RO, which compensates for the lower volumetric flow rate. In fact, the higher the injection pressure, the greater the average values of volumetric and mass flow rates, as the needle-valve returns to the seat more quickly and the time available to inject the same fuel quantity becomes extremely limited (Figure 3a,b).
## 5 Discussion  
Inter-satellite links are characterized by low signal-to-noise ratio (SNR), complex electromagnetic interference, and the time slot of each satellite being only 1.5 s. These all factors bring great difficulties to long code acquisition in inter-satellite links.  
In addition, with the rapid development of inter-satellite links, the number of satellites is on the increase and as a result, the electromagnetic environment is becoming more and more complex [31]. It is to be expected that the period PN-code would increase in the face of increasingly complex  
Figure 8: Theoretical results for mean acquisition time of Zero-padding (“ZP”), DC-XFAST (“DX”), DF-XFAST (“DF”) and XFAST (“XF”) with N equals to 4096, F equals to 5 and \(N_{th}\) equals to 1(**a**), 2(**b**), 3(**c**), 4(**d**).  
electromagnetic environment. Thus, studying long PN-code acquisition in inter-satellite links has a significant meaning to the theory and practices of inter-satellite link development.  
The proposed method uses multi-peak verification to guarantee lower false alarm probability, and thereby, to some extent, avoid SNR deterioration result from folding and provide a more stable detection performance than folding methods like XFAST and DF-XFAST.
## 3 Simulation
### The Influence of Step Size Analysis  
In this simulation, the primary noise was the narrowband white noise with a frequency range between 320 Hz and 350 Hz. The influence of step size \(\mu\) was analyzed. It mainly affected the convergence rate and the steady state error of the SFDFBANC algorithm. The two different step sizes \(\mu\) were chosen to realize the proposed SFDFBANC algorithm. One step size \(\mu\) was 0.04 and the other was 0.05. Figure 17 shows the time domain error signal curves with different step sizes. The convergence rate responded to a step size of 0.05 faster than it did to a step size of 0.04, but the steady state error was larger for a step size of 0.05 than it was for a step size of 0.04. In Figure 17, when the step size \(\mu\) was 0.05, the proposed SFDFBANC algorithm was unstable in nearly \(7.5\times 10^{5}\) samples. However, when the step size \(\mu\) was 0.04, the SFDFBANC algorithm had better steady-state characteristics than it did when the step size was 0.05.  
In this simulation, the time domain error signal curves between step sizes 0.04 and 0.03 were compared. The time domain error signal curves are shown in Figure 18. The convergence rate of step size 0.04 is faster than that of step size 0.03.  
Figure 16: MSE of the two algorithms.  
Figure 17: Time-domain error signal curves with large step size.  
Due to the set of simulations, if the larger step size \(\mu\) is chosen, the convergence rate will be accelerated; however, the steady state error will be increased. If the step size \(\mu\) was set to be too large, the system may be unstable. If the smaller step size \(\mu\) is chosen, the steady state error will be reduced, and the convergence rate will be slowed down. The optimal step size was the maximum step size without divergence of the SFDFBANC algorithm by trial and error.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 4 Results
### Wind Dynamics  
Figure 8a shows the onset of the up-valley wind in the valley centre, which occurs around 10:00 a.m. UTC and coincides with the vertical range increase. The mean up-valley wind intensity and depth tend to decrease during the stagnation stage. The layer develops up to 500 m AVL on 9 and 10 February, then decreases to a depth of around 200-300 m AVL on 11 and 12 February.  
The down-valley wind starts at 5:00 p.m. UTC and is characterised by a jet-like structure that varies in thickness, elevation, and intensity over the period:  
* The signature of the jet is pronounced during the night of 9 to 10 February with wind oscillations. The first hundred metres above the ground are the most affected by oscillations, with hourly wind reversal (more details in Section 4.2.2).
* The down-valley wind intensity is very weak on the night of 10 to 11 February This induces poor ventilation in the Passy basin boundary-layer, which is mainly affected by wind oscillations and waves.
* During the following night from 11 to 12 February, the jet forms at around 200 m AVL and then descends, reaching 120 m AVL in the early morning. Its upper structure is out of reach, probably because of signal extinction.
* For the last night of the episode, from 12 to 13 February, the jet forms at lower altitude (40 m AVL) with a base slightly disconnected from the ground, resulting in an almost motionless surface layer.  
The recurrence of the alternation between up-valley winds during the day and down-valley winds at night throughout the episode shows that the circulation is thermally driven, as expected under winter conditions [10]. The morning transition (from down- to up-valley wind) occurs 1 h 40 min after the local sunrise, which is at around 8:20 a.m. UTC according to the Site 1 observations. The evening transition is close to the local sunset, which occurs at 3:50 p.m. UTC. Those delays are consistent with those observed in the Cadarache valley by [PERSON] et al. [27]. It is interesting to note that the daytime and nighttime maximum wind intensities are similar. In comparison, up-valley winds three times stronger than down-valley winds have been reported by [PERSON] et al. [22], who focused on late spring and summer periods in the Adige Italian valley. However, it is important to bear in mind that, during the wintertime period considered in the present study, the energy available from solar insolation to support the up-valley wind is significantly lower.  
In the eastern part of the basin, the morning and evening transitions occur at the same times, but Figure 8b shows a more strongly layered structure. The down-valley wind layer remains at constant elevation (130 m AVL) and has a mean thickness of 80 m. Above this layer, an up-valley wind is observed intermittently during the night. Differences between the two parts of the basin will be discussed in greater details in Section 5.1.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Attention-Based Approaches
#### 3.5.3 Semantic Attention Models  
Semantic attention models aim to integrate high-level semantic information into the captioning process, thereby generating more coherent and meaningful captions by understanding the video's content. [PERSON] et al. [36] proposed a hierarchical network for video captioning that uses three levels to link video representations with linguistic semantics, extracting semantic features at different levels to guide caption generation. Similarly, [PERSON] et al. [44] introduced a unified framework (Figure 14, named aLSTMs, an attention-based Long Short-Term Memory model with semantic consistency. This framework takes the dynamic weighted sum of local spatio-temporal 2D CNN feature vectors and 3D CNN feature vectors as input for the LSTM decoder, integrating multi-word embedding and cross-view methodology to project generated words and visual features into a common space, bridging the semantic gap between videos and corresponding sentences.  
[PERSON] et al. [45] presented a Semantic-Based Temporal Attention Network (STAN) for Arabic video captioning, utilizing a Semantic Representation Network (SRN) to extract semantic features and employing temporal attention to align keyframes with relevant semantic tags. [PERSON] et al. [46] introduced an attention-based LSTM method focused on semantic consistency, combining attention with LSTM to capture significant video structures and explore multimodal representation correlations. Despite these advancements in  
Figure 13: The STAT video caption framework (redrawn by the authors, adapted from [16]) utilizes a spatial-temporal attention mechanism and comprises three main modules: (I) Feature extraction, (2) LSTM with spatial-temporal attention, and (3) caption generation.  
improving semantic richness in captions, semantic attention models face challenges related to the availability and quality of semantic features and require sophisticated architectures and training strategies, increasing computational complexity and training time.
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 4 Conclusions  
In the process of vehicle detection, due to the complex background, illumination changes, and occlusion, the accuracy of vehicle target segmentation from the background is poor, and the recognition accuracy is poor. This paper proposes a method of occluded  
Figure 9: Visual comparison results. (**a**) Vehicle image 1, (**b**) Cascade R-CNN segmentation result based on (**a**), (**c**) QuerlyInst segmentation result based on (**a**), (**d**) segmentation result of the method proposed in this paper based on (**a**).  
vehicle target detection based on vehicle component features, uses the U-net module, and introduces the residual U-shaped block so that the module can find the vehicle target area from the complex image. It introduces a vehicle component feature extraction module based on the Swin Transformer to effectively solve the problem of misdetection and missed detection of occluded vehicle targets caused by insufficient feature extraction. It combines the Swin Transformer module, receptive field expansion module, and efficient attention module and efficiently obtains the vehicle target component features and realizes the recognition of vehicle targets after the extracted feature labels and images are sent to the classifier trained by CMR. The experimental results show that the proposed method can effectively solve the problem of misdetection and missed detection of occluded vehicle targets due to insufficient feature extraction, and the recognition effect of occluded vehicles is better than other advanced methods.  
In practice, vehicle data are easily subject to different interference, so more features need to be considered to enhance model recognition, such as color features. In the next step, the color feature recognition part will be added on the premise of ensuring the speed of the model detection so as to improve the detection accuracy of the model and make it more suitable for practical application.  
Conceptualization and methodology, software and validation, writing--original draft, H.H.; conceptualization and methodology, writing--review and editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Science and Technology Program of Shaanxi Province, grant number 2023-YBGY-342, and the National Natural Science Foundation of China, grant number 62073256.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Isoprene Emissions, Oxidation Chemistry and Environmental Impacts
## 3 Initial Isoprene Oxidation Chemistry
The observations of elevated OH concentrations in tropical forested regions (e.g., Amazon, Borneo) throughout the 2000s [59; 60] contradicted these findings, and a large model-measurement of OH breakdown began to become apparent. Unexpectedly high OH concentrations, up to 10 times more than predicted by models at the time, were measured [61]. These differences were most noticeable in areas where high isoprene concentrations (>1 ppbv) corresponded with low NO\({}_{\mathrm{x}}\) concentrations (<1 ppbv). [PERSON] et al. [62] calculated the turnover rates of OH and HO\({}_{2}\) during a measurement campaign in the Pearl River Delta, China. The calculated OH concentration was found to be 3- to 5-fold higher than expected, and they proposed the additional OH was being generated by a pathway independent of NO and thus not producing additional ozone. These findings suggest that there is an unknown recycling mechanism of OH at low NO\({}_{\mathrm{x}}\) which is not included in conventional isoprene degradation mechanisms. [PERSON] et al. [59] suggested that an OH recycling efficiency in isoprene oxidation of 40-80% was needed to explain the unexpectedly high OH concentrations over the Amazon rainforest.
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
## 2 Simulation
### Equivalent Model  
In order to further investigate the advantages of triangular devices, we compared the trends of \(V_{\text{th}}\) and \(I_{\text{on}}\) with a cross-sectional area of different GAA SNWTs. From Figure 4, we can see that \(V_{\text{th}}\) and \(I_{\text{on}}\) have a linear correlation with the cross-sectional area, and are almost independent of cross-sectional shape, so the cross-sectional area is the root factor affecting the short channel effect of GAA SNWTs.  
\(S_{\text{square}}\)\(=D^{2}\), \(S_{\text{circle}}=\frac{\pi}{4}D^{2}\), \(S_{\text{triangle}}=\frac{\sqrt{3}}{4}D^{2}\)  
For quadratic, circular, and triangular nanowire devices, the relationship between line width and cross-sectional area is shown in Equation (1). The triangular devices have the smallest cross-sectional area under the same line width condition, and thus have the strongest electrostatic control capability and can obtain the best device performance.
# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 3 Experimental Results and Analysis
### Robustness Evaluation
This improved algorithm employs nonlinear diffusion filters to accentuate feature edges, effectively highlighting and preserving valuable image details across various scales while minimizing information loss through maximum downsampling. The result is efficient feature matching in scenes with rich textures, such as tree bark in the Bark dataset, as well as in scenarios with rotational changes, such as the Boat dataset. Moreover, it provides robust performance even with significant lighting variations, as seen in the Bikes and Leuven datasets, and substantial viewpoint differences, as in the Graf dataset. This advancement showcases the applicability and efficiency of the fast image-matching algorithm based on nonlinear filtering across a diverse range of real-world application settings.
## 2 Materials and Methods
### Experimental Protocol  
All participants underwent functional evaluation via skin marker-based kinematic analysis using an 8-camera motion analysis system at 100 Hz (Vicon 612, Vicon Motion Capture, Oxford, UK) (Figure 1).  
Two force plates embedded in the floor (Kistler, Switzerland) recorded the ground reaction forces at 2000 Hz. A single experienced examiner fitted each participant with 18 reflective markers on anatomical landmarks on the shank, calcaneus, midfoot, metatarasus and proximal phalanx of the hallux, according to the Rizzoli Foot Model ([PERSON], 1993; [PERSON], 1993). Local anatomical reference frames were established on each segment according to the position of relevant markers. The relative orientation between segments was determined using the Joint Coordinate System (Kistler, Switzerland). Gait analysis was performed during barefoot walking at comfortable self-selected speed. For each participant, relative motion with respect to the three axes of the joint coordinate system (dorsi/plantar flexion, inversion/eversion and adduction/abduction) was measured between the following pairs of segments: calcaneus and shank (ShCa; ankle joint complex); midfoot and calcaneus (CaMi; midtarsal joint); metarasus and midfoot (MiMe; tarsometatarsal joint); metatarasus and calcaneus (CaMe). The overall shank-to-foot motion (ShFo), assumed to be one single rigid segment, and the sagittal plane motion of the first metatarasophilangeal joint (MTP) were also measured. Time histories of joint kinematics were normalized over gait cycle duration. Timing of gait events and spatio-temporal parameters were determined by the analysis of the markers' trajectories and of the ground reaction forces. Range of motion (ROM) of all joints with respect to the three axes of rotations was computed as the absolute difference between maximum and minimum value over stance phase duration. All gait data were averaged across five walking trials for each subject and used as samples for the statistical analysis. Right limb data only were used in the present analysis.