esa-sceva/satcom-chunk-collection huggingface dataset:
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 3 Our Proposed Approach  
In this work, we propose an elegant method using map-matching technique to obtain high lane-level localization accuracy. The framework of the proposed method is presented in Figure 2. Our method takes three inputs: Camera, GPS, and a Reference Map.  
Figure 2: The pipeline of our proposed system.  
The GPS point constituting the trajectory is given as follows:  
\[\mathbf{p}_{w}=(p_{x_{w}},p_{y_{w}}) \tag{1}\]  
where \(p_{x}\) and \(p_{y}\) represent the longitude and latitude coordinates, respectively, and \(w\) is the current window timestamp.  
The reference map positions \(\mathbf{r}\) can be defined as follows:  
\[\mathbf{r}_{j}=(r_{x_{j}},r_{y_{j}}) \tag{2}\]  
where \(r_{x}\) and \(r_{y}\) represent the longitude and latitude coordinates, respectively, and \(j\) is the location number of the reference point.  
The GPS provides the local positions and creates a local search map for an area of 25 \(\times\) 25 m. The reference points of local map are selected from the reference map whose points fall inside the local area where the current GPS position is currently located. Therefore, the local reference of the window timestamp \(\mathbf{r}^{w}\) can be defined as follows:  
\[\mathbf{r}_{k}^{w}=(r_{x_{k}},r_{y_{k}}) \tag{3}\]  
where \(k\) is the location number of the reference point in the local map area.  
The distance between these points and the current local GPS position is calculated and compared using the simple closest point algorithm.  
Using sliding window technique, as shown in Figure 3, we search for the closest point (**CP**) between the position acquired from the current GPS and the positions in the reference map where the vehicle passes through it. The distances are calculated using Euclidean distance. The minimum distance is selected as the most appropriate position that is close to the vehicle. The closest point for a corresponding window is determined by the following relation:  
\[\mathbf{CP}_{\mathbf{p}_{w}}^{w}=\mathbf{r}_{j}^{w},\quad\underline{where} \quad\operatorname*{argmin}_{j}\left\{\sqrt{\sum_{t=1}^{2}(p_{wt}-r_{jt})^{2 }}\right\} \tag{4}\]  
Simultaneously, the camera supplies the sequence of images to be processed by lane segmentation algorithm. Using lane segmentation, we have vehicle's position relative to median lane.
# Underwater Sea Cucumber Identification Based on Improved YOLOv5
## 1 Introduction
proposed an underwater target recognition and tracking method based on YOLOv3 algorithm [7], which realized the identification, positioning, and tracking of underwater targets, with an average precision of 75.1% and a detection speed of 15 frames per second. [PERSON] proposed a sea cucumber target recognition algorithm based on tiny-YOLOv3 network [8], which modified and optimized the original YOLO; the detection precision reached more than 90%, and it could still reach 47 frames per second on the low-performance computing platform. [PERSON] et al. proposed underwater object detection based on class-weighted YOLO network [9], with an average precision of 78.3% and a processing speed of 12 frames per second, which greatly improved the multi-target recognition effect. Therefore, the YOLO algorithm takes into account the characteristics of high precision and small memory occupation. Aiming at the problems of low degree of differentiation of sea participation environment in natural environment and imprecise identification of small targets, an underwater sea cucumber recognition algorithm based on improved YOLOv5 is proposed. 1. Improve the structure of the YOLOv5s model, increase the number of upsampled in the Neck network, and increase the Detect layer in the Head network, so that small sea cucumber targets can be detected;
2. Add the CBAM module, which can save parameters and computing power, and ensure that it can be integrated into the existing network architecture as a plug-and-play module;
3. The image was pre-processed by MSRCR algorithm, which enhanced the differentiation of the sea participation environment and provided help for precise and rapid identification of sea cucumbers in the natural environment. 4. After modifying the YOLOv5s model, ablation study was conducted, and the feasibility of improvement was proved. Compared with YOLOv4 and Faster-RCNN, the experimental results showed that the improved YOLOv5s had a higher precision and recall rate. In Section 2, the images are processed using MSRCR and the dataset is augmented with OpenCV. In Section 3, an introduction to the YOLOv5s network structure is introduced, the YOLOv5s network structure is improved, the CBAM and the Detect layer of the Head network are added, and the Ablation Study is conducted. in Section 4, the dataset using the improved YOLOv5s model is trained and the training results are analyzed. In Section 5, YOLOv5s, YOLOv4, and Faster-RCNN are compared using an improved YOLOv5s model and the experimental results are analyzed. Finally, conclusions and outlooks are provided in Section 6.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 3 Analysis of the Current Situation and Limitations of Top-Down Monitoring Methods
### GEOS-Chem Model Inversion
The objective of the top-down method is to reduce the discrepancy between the simulated and observed CO\({}_{2}\) concentrations with respect to spatial and temporal scales. However, the accuracy of the a priori emission inventory and the complexity of the model parameterization scheme have a significant impact on the accuracy of the simulation results. Despite the potential for real-time detection across a vast geographic area, satellite remote sensing is constrained by the influence of complex meteorological conditions, including cloud cover, precipitation, and aerosol pollution. The assimilation and inversion of global carbon emissions from biomass burning is hindered by several issues, including low data utilization, an uneven spatial and temporal distribution of data, and insufficient inversion accuracy. These challenges contribute to an increased level of uncertainty in the process.
# Consumer Acceptance of Cultured Meat: An Updated Review (2018-2020)
## 6 Barriers to Acceptance
### Unnaturalness  
The perceived unnaturalness was one of the key themes in our previous review [12; 57; 58], and was often at the root of disgust and health and safety concerns. Unnaturalness continues to play a central role in consumers' reasons to avoid cultured meat. Indeed, this was a key theme identified in many qualitative studies [22; 35; 36] and appears to be a more basic objection than other barriers to acceptance, such as perceived safety or ethical problems. For example, [PERSON] et al. [13] found that messages targeting the perceived unnaturalness failed to persuade consumers that cultured meat was, in fact, natural, or that naturalness should not matter. [PERSON] et al. [31] provide some quantitative validation of the importance of naturalness, noting that this was a major component of \"emotional objections\" which were a major driver of cultured meat rejection.  
However, the perception of unnaturalness does not necessarily lead to rejection: [PERSON] and [PERSON] [23] observed that their young German sample found cultured meat to be less natural than insects, but preferred it nonetheless. Indeed, [PERSON] and [PERSON] [29] demonstrate that the subjective importance of naturalness predicted a lower perceived naturalness of cultured meat, which in turn predicted a lower consumption intention. These findings appear to suggest that those who are especially concerned about naturalness per se are likely to reject cultured meat. We note that the subjective importance of naturalness is different from the perceived unnaturalness; for example, one might agree that cultured meat is unnatural, but not consider this fact important.  
There is one notable exception to this trend: [PERSON] et al. [32] found that, whilst several psychographic factors such as food hygiene sensitivity, food neophobia and political conservatism did predict cultured meat rejection, a naturalness bias did not. They also showed no correlation between a naturalness bias and measures of cultured meat acceptance, indicating that this was not an issue of overfitting. It is notable that the strongest quantitative studies demonstrating that the subjective importance of naturalness is a factor affecting cultured meat acceptance used European samples [29; 31], whereas [PERSON] et al. [32] used an American sample. Given the higher rates of cultured meat acceptance observed in the US compared to Europe [59], it is plausible that naturalness is more of an important issue to Europeans than to Americans.  
[PERSON] and [PERSON] [34] found that, whilst focus group participants greeted both insects and cultured meat with some amount of disgust, insects were considered more natural than cultured meat.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 6 Conclusions  
In this work, an extended review on retrospective registration used for OCT retinal motion correction is provided. We show how the increase in imaging acquisition rate, from the first TD-OCT systems at a few A-scans/second, to the current SS-OCT devices at a few millions A-scan/second, has changed the main motivation behind the retrospective motion correction over the past few years. The nature of the motion artefacts is discussed as well as the registration techniques. The pre-processing and the current validation methodologies are also detailed. Despite the simplicity of the retrospective approaches compared to hardware solutions, their validation remains a major concern and limitation, compromising the reliability of the registered data, specifically on pathological images. The development of faster acquisition devices will certainly contribute to reduce the retinal motion artefacts in SS-OCT imaging. However, the integration of technologies such as AO-OCT and OCT-A sets even higher demands on image acquisition speeds. It has already been shown that registration algorithms will be fundamental to ensure a high in vivo retinal imaging quality with a cellular structure resolution. Besides drifts and microsaccades, retinal tremors will be a new variable to consider in the retinal motion correction models. Hence, new precise registration procedures incorporating different OCT modalities (OCT, AO-OCT, OCT-A) together with robust validation techniques should be tackled by the OCT scientific community over the upcoming years.  
Conceptualized and designed the review: L.S.B., D.A.D.J., T.v.W., and S.K.; Drafted the manuscript: L.S.B., and D.A.D.J.; Revised the manuscript for intellectual content: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
This work was supported by the Horizon 2020 research and innovation programme (Grant No. 780989: Multi-modal, multi-scale retinal imaging project).  
The authors declare that there are no conflicts of interest related to this article.
## 2 Results and Discussion  
### Fertilisation Experiment  
During the growing season, four organic foliar fertiliser mixtures, based principally on marine algae, amino acids and microelements (Treatment 1, Treatment 2, Treatment 3 and Treatment 4, namely T1, T2, T3 and T4) (treatment detail in Table S1), were tested on the same cultivar (Cascade) compared to a control (C) with no foliar fertilisation. The nutrient mixture was applied as foliar treatment. The hop yard has a loamy clay soil, with a pH of 7.33, Electric Conductivity (EC) of 264 \(\upmu\)S/cm and Cationic Exchange Capacity (CEC) of 30.86 meq/100 g (other physical and chemical properties of the soil are reported in the Material and Methods section, Table 6). The experimental design and the first results of the experiment regarding physiological measurements have already been published [22]. The amounts of macro- and microelements applied to the hop plants are indicated in Table 1 and the fertiliser mixtures are indicated in Table S1. Non-treated plants were also considered, representing the control. For each treatment, three independent replicates were used, with each replicate consisting of 150 plants. Each treatment was separated from the next by five rows of untreated plants.
## 3 Results and Discussion  
### Variability Evaluation of NDT  
Non-destructive testing techniques are known as methods for evaluating properties and soundness without destroying the target structure. However, NDT may cause variability in measurement results depending on the inherent error of the equipment, the proficiency of the tester, and the environment with the concrete members. Therefore, research through NDT must evaluate the variability in the equipment used in advance. The NDT device used in this study was equipment that measures the ultrasonic velocity of the core specimens with the direct method. The variability that can occur during this process is largely due to equipment errors and the tester's proficiency, and the variability in the surface and thickness of the core specimens is assessed.  
The evaluation results are shown in Figure 5, and the average variability on the left side represents the average coefficient of the inherent error range of the equipment. The relative difference on the right is compared with the ultrasonic velocity of lower core specimens cut in half to show the relative difference in the thickness of the core specimens. If the result was positive (+), the measured results of the cut core specimens were significant, and if it was negative (\(-\)), it means that the measurement result of the entire thickness core specimens was large.  
As a result of the analysis, the inherent variability of the ultrasonic velocity measurement method was 0.36%. According to a study by [PERSON] (2017), NDT variability was approximately 10% for non-destructive test equipment [16]. Comparing the results with the variability of the ultrasonic velocity measurement method in this study, it can be seen that the inherent variability of the ultrasonic velocity investigation technique is much lower than that of the striking NDT equipment. The relative difference between the results of ultrasonic velocity measurement of the before-cutting and the half-cutting core specimens was 2.04% to evaluate the effect of the thickness. This result is predicted to be due to the micro structural difference between the surface conditions that came into contact with the probe before cutting and the surface conditions after cutting, as the half-cutting core specimens went through the surface cutting process.  
Figure 5: Variability results of ultrasonic velocity investigation methods.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 2 Materials and Methods
### EEG Dataset  
We used EEG recordings of 109 subjects performing four motor/imagery tasks using a 64-channel BCI2000 system to construct a general-purpose BCI system available in PhysioNet database [23]. Each subject performed 14 experimental runs containing two baseline runs and three two-minute runs of real/imaginary motor tasks. We used only the data corresponding to the MI tasks, as the BCI requires only imaginary movements for classification. The timing of the experiment is displayed in Figure 3. The experimental setup was as follows. The first two runs were the baseline runs, one with eyes open and the other closed. Next, the subject imagined opening/closing the left/right fist according to the displayed cue on the left/right side of the screen. If the visual cue appeared on the top of the screen, the subject was to imagine closing and opening both fits. If the visual cue was on the bottom of the screen, the subject was to imagine opening and closing both feet. The subject then relaxed after each cue.
# Sign Language Interpreting System Using Recursive Neural Networks
## 3 The Proposed System  
The main idea is to utilize spatial coordinates of the body parts involved in MSL, specifically the hands, torso, and face because gestures play an important role in MSL.  
Then, use these coordinates as patterns for training an RNN to predict words of the MSL in an online scenario. We planned the proposed system to receive live video from a camera; and then, compute the necessary data from it; next, feed this data into the RNN and make predictions, and finally, loop again in a continuous manner. We conceive this process as illustrated in Figure 1.  
We believe this distinguishes our work from many deep learning-based approaches to video interpretation since these use frame changes to identify content; unlike most of them, we tailored a system based on the spatial coordinates as we explained above. Also, we considered the position relative to the torso to provide further information about spatial position. We utilized the mediapipe model to compute the spatial coordinates of the body parts we are interested in (Figure 2).  
In the remainder of this section, we explain the procedure to construct the dataset, and then, we discuss the proposed model of the RNN.
## 4 HP-SFC Performance Analysis
### Results and Evaluation
In HP-SFC, the whole segment was viewed as failed for either SFF-SFF link failure or SF-SFF link failure, and this enabled it to detour traffic with few additional links and provide a more consistent protection performance, as shown in Figure 7a,b. On the contrary, the composition of the enterprise topology was much different than the data center topology, where a few densely connected switches provided multiple routes, however, there were only a few end-to-end shortest paths. This topology composition caused the results of local recovery and global recovery to vary dramatically for the SFF-SFF link failure and SF-SFF link failure cases in Figure 8a,b, whereas HP-SFC again showed more consistent results and had the lowest RTT increment in the case of SF-SFF failure in Figure 8b. Based on the results in Figures 7 and 8, it can be concluded that HP-SFC might not always provide the lowest RTT increment, but its performance was more consistent and reliable in comparison to local and global recoveries. Figure 5: Network traffic recovery delay for single SF chain incurred by HP-SFC in the data center and enterprise topologies. (**a**) Throughput at the primary SF (\(f_{1}\)) and backup SF (\(f_{1}^{\prime}\)) in the data center topology. (**b**) Throughput at the primary SF (\(f_{1}\)) and backup SF (\(f_{1}^{\prime}\)) in the enterprise topology. Figure 6: Flow table resource utilization comparison of local recovery, global recovery, SSP, and HP-SFC in the data center and enterprise topologies. (**a**) Flow table resources utilized in the data center topology by backup paths. (**b**) Flow table resources utilized in the enterprise topology by backup paths. Figure 7: Data center topology, average RTT increment of SFCs 1, 2, and 9 for local recovery, global recovery, and HP-SFC protection mechanisms. (**a**) RTT increment for SFF-SFF link failure. (**b**) RTT increment for SF-SFF link failure.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
###### Abstract  
The ease of deployment and the auto-configuration capabilities of Wireless Sensor Networks (WSNs) make them very attractive in different domains like environmental, home automation or health care applications. The use of multichannel communications in WSNs helps to improve the overall performance of the network. However, in heavy traffic scenarios, routing protocols should be adapted to allow load balancing and to avoid losing data packets due to congestion and queue overflow. In this paper, we present an Acknowledgement-Based Opportunistic Routing (ABORt) protocol designed for high data rate multichannel WSNs. It is a low overhead protocol that does not rely on synchronization for control traffic exchange during the operational phase of the network. ABORt is an opportunistic protocol that relies on link layer acknowledgements to disseminate routing metrics, which helps to reduce overhead. The performance of ABORt is evaluated using the Cooja simulator and the obtained results show that ABORt has a high packet delivery ratio with reduced packet end-to-end delay compared to two single channel routing protocols and two multichannel routing protocols that use number of hops and expected transmission count as routing metrics.  
Keywords:wireless sensor networks; multichannel routing; queueing delay; acknowledgement; synchronization +
Footnote †: journal: Sensor and Actuator Networks
## 4 Implementation of RUL Prediction Based on Proposed Method
### Experimental Verification and Analysis
#### 4.4.3 Comparison of Different Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Evaluating Indicator** & **B1-3** & **B2-1** & **B2-6** & **Average** \\ \hline GRU & MAE & 0.0750 & 0.1094 & 0.1495 & 0.1113 \\ RMSE & 0.0970 & 0.1321 & 0.1940 & 0.1410 \\ MAE & 0.0878 & 0.1195 & 0.1483 & 0.1185 \\ TCN & RMSE & 0.1369 & 0.1549 & 0.1919 & 0.1612 \\ MAE & 0.0646 & 0.0947 & 0.1391 & 0.0995 \\ RMSE & 0.0924 & 0.1279 & 0.1831 & 0.1344 \\ MAE & 0.0670 & 0.1950 & 0.0375 & 0.0998 \\ RMSE & 0.0830 & 0.2610 & 0.0507 & 0.1316 \\ MAE & 0.0560 & 0.1570 & 0.0720 & 0.0950 \\ RMSE & 0.0700 & 0.1900 & 0.0940 & 0.1180 \\ Proposed & MAE & 0.0453 & 0.0885 & 0.1172 & 0.0836 \\ Method & RMSE & 0.0632 & 0.1209 & 0.1570 & 0.1137 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation indexes of different models. Figure 18: Comparison of different model prediction results.
## 1 Introduction
LNOI has been confirmed to be a promising candidate for nonlinear optics applications due to its appealing properties, such as strong \(\chi^{(2)}\) (\(r_{33}\) = 3 \(\times\) 10\({}^{-11}\) m/V) and \(\chi^{(3)}\) (\(n_{2}\) = 1.8 \(\times\) 10\({}^{-19}\) m\({}^{2}\)/W) nonlinearities [17], high refractive index (\(n\)-2.21 @ 1550 nm) [18], ultralow loss (record @ 0.2 dB/m) [19], and wide transparency window (0.35-5 \(\upmu\)m) [18]. In addition to nonlinear effects, LNOI is also outstanding in the multi-physics field coupling and can realize a wide variety of piezo-optomechanical and thermo- and acoustic-optic devices [20]. The refractive index of LNOI is about 0.21 higher than that of Si\({}_{3}\)N\({}_{4}\), which yields a stronger contribution of waveguide dispersion and better dispersion tailoring ability. Lithographic control of the waveguide geometry can precisely tailor the dispersion. Furthermore, compared with the Si\({}_{3}\)N\({}_{4}\) platform, thick-film LNOI ranging from 600 to 900 nm is commercially available. In view of the aforementioned advantages of LNOI, tremendous advances have been demonstrated in this area in recent years, such as coherent and octave-spanning SCG [21; 22], Kerr frequency comb generation [17; 23; 24], and an electro-optic micro-ring-based dual-comb interferometer [25]. An octave-spanning SCG on an LNOI waveguide was demonstrated by [PERSON] et al. [21] and [PERSON] et al. [22], which focused on the single-core waveguide and tailored the two-ZDW dispersion profiles for high performance SCG. In this work, we leverage the four-ZDW dispersion profile produced by the anti-symmetric mode in the LNOI dual-coupled waveguide to obtain highly efficient and flat MIR DW-enhanced SCG. We designed the geometrical parameters of the dual-coupled waveguide to have a relatively flattened four-ZDW dispersion profile. We used the fourth-order Runge-Kutta method to solve the generalized nonlinear Schrodinger equation (GNLSE) to obtain the evolution in the frequency and time domain. The performances of the bandwidth, flatness, and conversion efficiency of the MIR-DW were numerically studied and are discussed. We also studied the influence on the MIR DW-enhanced SCG when the pumping conditions are changed.