esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### Pre-Training
#### 5.1.3 Self-Supervised Pre-Training  
A powerful approach that has emerged is that of pre-training models on vast amounts of image-text pairs collected from the internet. This is motivated by the fact that there are vast quantities of unstructured natural language data on the internet. This approach enables models to learn about images directly from raw text. The datasets used in these approaches are also interesting in that they do not need to be heavily curated and annotated using expensive and expert human agents. All this makes training at an unprecedented scale possible. After pre-training, natural language is used to refer to learned concepts from the visual domain are to describe new concepts not previously seen. Among other things, this facilitates zero-shot or few-shot transfer of the model to other downstream tasks. Representative models that employ this approach are CLIP [53] and ALIGN [54].  
**CLIP** refers to Contrastive Language-Image Pre-training ([PERSON] et al. [53]). CLIP learns perception from the supervision based on natural language paired with images. They developed a dataset (WeblmageText) consisting of 400 million image-text pairs taken from the internet. They experiment with two image encoders: one based on ResNet50 [12] and another based on the Vision Transformer (ViT) [99]. The text encoder is based on a transformer. They use a contrastive objective based on the cosine similarity function to learn a multimodal embedding space and to predict correct and incorrect text-image pairings. The optimization is via a cross entropy loss. The pre-trained model can then be transferred to various down-stream vision tasks, including in zero-shot settings, where it matches the performance of strong, fully supervised baseline models. For instance, in image classification on ImageNet, the CLIP model matches the original ResNet50 without using any of the crowd-labeled training examples that ResNet50 used.  
**ALIGN** refers to A Large-scale ImaGe and Noisy-text embedding [54]. It is conceptually similar to CLIP but differs in that for pre-training, it uses noisy data taken from a large number of image alt-text pairs using an approach similar to that of Conceptual Captions [59], but without the expensive filtering or post-processing steps. The resulting corpus consists of 1.8 billion image-text pairs. The authors show that the scale of their resulting dataset makes up for the inherent noise and leads to good performance. During pre-training, they use a dual-encoder architecture to align the visual and language representations in a joint embedding space. The dual-encoder consists of an EfficientNet-based [100] image encoder and a BERT-based text encoder coupled with a cosine-similarity function. For their objective, they use a contrastive loss, similar to CLIP. The aligned vision-language representations enable zero-shot transfer learning and cross-modal retrieval (image-to-text and text-to-image).
## 2 Modern Physical Layer
### Underwater Acoustic Channel Characterization
#### 2.1.3 Doppler Effect  
The relative motion of the transmitter and the receiver produces a Doppler effect, which causes an additional signal distortion. The Doppler effect is significantly important in networks, including mobile elements, such as autonomous underwater vehicles (AUV).  
As described in [13], Doppler-shift can be seen as either a frequency shift or a time scaling (expansion/compression) of the signal waveform. These effects are shown in Equation (5), where s(t) and r(t) are the source and the received signal, respectively. These effects are also reflected in a frequency shift of the received signal following Equation (6). The distortion factor, \(\Delta\), is proportional to the modem's relative speed, as shown in Equation (7)  
\[r(t)=s((1+\Delta)t) \tag{5}\]  
\[f_{r}=(1+\Delta)f_{s} \tag{6}\]  
\[\Delta=\frac{v_{s,r}}{c} \tag{7}\]
## 4 Algorithmic Approaches
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Network Name**} & \multicolumn{6}{c}{\(M_{1}\) **with 20\% Controllless Density**} & \multicolumn{6}{c}{\(M_{2}\) **with 20\% Controllless Density**} \\ \cline{2-13}  & **Solution** & **Best Bound** & **MipGap** & **CPU** & **NC** & \(B\&B\) **Nodes** & **Solution** & **Best Bound** & **MipGap** & **CPU** & **NC** & \(B\&B\) **Nodes** \\ \hline Arpanet196912 & 1 & 1 & 0 & 0.03 & 1 & 1 & 1 & 1 & 0 & 0.01 & 1 & 1 \\ Abline & 0.4 & 0.4 & 0 & 0.33 & 3 & 1 & 0.4 & 0.4 & 0 & 0.16 & 3 & 1 \\ Aamet & 0.48 & 0.48 & 0 & 0.72 & 3 & 304 & 0.48 & 0.48 & 0 & 0.29 & 3 & 1 \\ Ans & 0.25 & 0.25 & 0 & 1.16 & 4 & 1 & 0.25 & 0.25 & 0 & 0.56 & 4 & 1 \\ HurricaneElectric & 0.18 & 0.18 & 0 & 1 & 4 & 1 & 0.18 & 0.18 & 0 & 0.21 & 4 & 1 \\ Atmnet & 0.25 & 0.25 & 0 & 3.93 & 5 & 544 & 0.25 & 0.25 & 0 & 1.68 & 5 & 131 \\ Benplanet & 0.16 & 0.16 & 0 & 5.24 & 6 & 61 & 0.16 & 0.16 & 0 & 8.88 & 6 & 1385 \\ Bios & 0.19 & 0.19 & 0 & 58.55 & 7 & 1904 & 0.19 & 0.19 & 0.19 & 0 & 16.36 & 7 & 1645 \\ CrlNetworkServices & 0.11 & 0.11 & 0 & 12.99 & 7 & 1 & 0.11 & 0.11 & 0 & 13.98 & 7 & 1339 \\ Internet2 OS3E & 0.17 & 0.17 & 0 & 28.66 & 7 & 683 & 0.17 & 0.17 & 0 & 26.95 & 7 & 1025 \\ Gent2009 & 0.21 & 0.21 & 0 & 25.13 & 7 & 585 & 0.21 & 0.21 & 0 & 14.52 & 7 & 1618 \\ NetworkUsa & 0.18 & 0.18 & 0 & 41.19 & 7 & 1075 & 0.18 & 0.18 & 0 & 56.45 & 7 & 1033 \\ VtlWavenet2008 & 0.02 & 0 & 0.93 & 3600.08 & 18 & 1148 & 0.02 & 0 & 0.91 & 3600.01 & 18 & 2202 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical results for the real benchmark networks obtained with models \(M_{1}\) and \(M_{2}\) across the three density scenarios. Bbnplanet, CrlNetworkServices, and NetworkUsa networks in terms of CPU time for the 20% controller density scenario. Additionally, it can be observed that Model \(M_{2}\) is more efficient for all studied networks in scenarios with 20% and 30% controller densities. Finally, regarding the time used and changes in MipGap across all networks, we can conclude that the scenario reporting the highest complexity in its resolution is the 20% controller density scenario, followed by the 30% scenario, and finally, the 40% scenario. Similarly, from the review of models \(M_{1}\) and \(M_{2}\) on randomly generated networks, we can derive from the information shown in Table 3.

For the scenario with a 20% controller density, it can be observed that for all networks, Model \(M_{2}\) is more efficient in terms of CPU processing time, except for the Randomic networks with 80 nodes and 100 nodes, where an optimal solution cannot be obtained, and the best near-optimal solution is found after 1 h of searching. In the case of the 80-node Randomic network, the MipGap obtained in \(M_{1}\) is better than in \(M_{2}\), while in the 100-node Randomic network, the MipGap is approximately the same. It is noteworthy that for \(M_{1}\) in this scenario, optimal solutions are achieved from the 20-node network up to the 60-node network; this result is worse than that obtained using \(M_{2}\), which also achieves optimal solutions for networks with 65 and 70 nodes.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception of Terrestrial and LEO Satellite Antenna System
#### 6.2.3 LoRa CubeSat with Crossed Yagi-Uda UHF  
The ground control satellite system has tested signal reception from the LoRa CubeSat within the TinyGS network [31], which is a LoRa communication on the UHF frequency band. The test was conducted with the Sapling2 satellite [32], a 1U CubeSat weighing approximately 1 kg, on the frequency of 437.4 MHz, with communication parameters of SF7 and a bandwidth of 125 kHz. Our ground station used a UHF Yagi-Uda antenna for reception. The test results showed successful reception of text data, reporting the CubeSat's status every 1 min, with a signal-to-noise ratio (SNR) ranging from \(-6\) to \(+4\) dB throughout the satellite's pass. Additionally, the system was capable of receiving signals from other satellites within the TinyGS network that had low transmission power.
## 2 Characteristics, Principles, and a Case Study of SPH
### Principles of SPH  
The theoretical basis of smoothed particle hydrodynamics (SPH) stems from the particle method, which employs a collection of discrete particles to represent continuous physical quantities. This framework allows for numerical analysis through particle-based interpolation. In the SPH approach, the essential equations of fluid mechanics are converted into computational expressions via kernel function evaluation, wherein these particles act as mechanical quantities. As a result, integration is accomplished using a discrete set of points, effectively simplifying the resolution of ordinary differential equations (ODEs) that involve only time derivatives while eliminating the complexities associated with partial differential equations (PDEs) that include both time and spatial derivatives.  
Central to SPH is the kernel function, \(W\), which acts as a regulatory mechanism, determining the influence of neighboring particles within a specified smoothing length, \(H\), on the particles of interest. Any macroscopic variable, such as velocity or pressure, and its gradient in SPH, can be expressed through a set of kernel interpolations of disordered particles. The fundamental equations of fluid mechanics can then be re-expressed in SPH form. The time integration of the resulting ODEs is typically performed using explicit methods such as the Predictor-Corrector method, the fourth-order Runge-Kutta method, or the standard Leapfrog algorithm. Additionally, methods like the Central Difference and Lax-Wendroff difference schemes can also be employed.
## 5 Path Following Control Design
### 3 DOFALOS Guidance Law
#### 5.1.1 3 DOF Adaptive Look-Ahead Distance
The main function of the look-ahead is adaptive to the change of curvature. At different speeds, the trend of the look-ahead with the cross-track error and the curvature is similar, but the pace of change varies.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 3 Model Solving  
The synchronous optimization model proposed in this paper assumes that passengers choose the shortest path when calculating the in-vehicle travel time cost in the objective function. However, there may be multiple shortest paths between the same origin-destination (OD) stations. In such cases, it is essential to consider passengers' choices among different paths. Therefore, it is necessary to statistically analyze the number of shortest paths between each OD pair. The Yen algorithm can analyze reachable paths between the same OD pair, count these paths based on their weights, and provide a sorted ranking of weights. In this paper, the Yen algorithm is utilized to statistically analyze the shortest paths between different OD pairs. During application, a flexible approach is taken to set the K value (number of shortest paths to consider) by comparing the weights of adjacent shortest paths. This allows for adaptable selection of the K value for different OD stations.  
This paper proposes a synchronous optimization model that deals with a combinatorial optimization problem. Such problems have a finite number of combination possibilities, and the optimal solution can be found using an enumeration method. However, when there are many decision variables, the computational complexity of the problem can become extremely large, often exhibiting exponential growth. For example, when considering an optimized bus network with N network sites, n dimensions, r routes, and a headway range of R, the enumeration would require N\({}^{n}\)\({}^{*}\)R\({}^{r}\) iterations, resulting in a massive computational load. This falls into the category of typical NP-hard problems. Due to the high computational difficulty and complexity of such problems, intelligent optimization algorithms are commonly used for solving them. Considering the characteristics of large-scale bus network construction in this research, which involves a high number of site dimensions and computational complexity, genetic algorithms are chosen as they offer high operational efficiency and are widely applicable for solving large-scale combinatorial optimization and non-linear optimization problems. Therefore, this paper employs a genetic algorithm to solve the synchronous optimization model.  
For the synchronous optimization problem of the bus network and scheduling discussed in this paper, relevant algorithm designs have been made for the synchronous optimization model, which are specifically described as follows:
# A Thermopile-Based Colorimetric Temperature Measurement Method for Arbitrary Bandwidth
## 5 Conclusions  
This study has demonstrated that when the wavelength ranges of two arbitrarily wide infrared bands do not overlap, there exists a monotonic relationship between the ratio of radiative energies within these bands and the temperature of the object being measured. This relationship allows for the determination of the object's temperature based on the energy ratio. Building upon this finding, the study proposes a thermopile-based arbitrary bandwidth colorimetric thermometry method for the first time. Simulation experiments are conducted to validate this method. The simulation results indicate that when the wavelength ranges of the two bands do not overlap, there is a monotonic relationship between the electric signal ratio and the measured temperature, making this method suitable for temperature measurement. However, when the wavelength ranges overlap, the electric signal ratio may exhibit non-monotonic behavior with respect to temperature, making it impossible to determine the temperature of the object. The thermopile-based arbitrary bandwidth colorimetric thermometry method enables temperature measurement even when the emissivity of the measured object is unknown. Additionally, it offers  
Figure 4: Relationship curves. (**a**) \(Q\)-\(T\) relationship curve using thermopile 1 and thermopile 2; (**b**) \(Q\)-\(T\) relationship curve using thermopile 3 and thermopile 4.  
Figure 3: Spectral transmittances and responsivities of thermopiles. (**a**) Spectral transmittance of thermopile 1; (**b**) responsivity of thermopile 1; (**c**) spectral transmittance of thermopile 2; (**d**) responsivity of thermopile 2; (**e**) spectral transmittance of thermopile 3; (**f**) responsivity of thermopile 3; (**g**) spectral transmittance of thermopile 4; (**h**) responsivity of thermopile 4.  
advantages such as wide absorption band coverage and low measurement costs, presenting promising applications in various fields.  
Conceptualization, Q.J. and G.D.; methodology, Q.J.; software, Q.J.; validation, Q.J., Y.M. and G.D.; formal analysis, Q.J.; investigation, Y.M.; resources, G.D.; data curation, K.W.; writing--original draft preparation, Q.J.; writing--review and editing, G.D. and X.C.; visualization, Q.J.; supervision, X.C.; project administration, G.D. and X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Key Research and Development Plan (Grant No. 2021 YFB3203104).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
Evapotranspiration (ET) plays a crucial role in water balance within the global hydrological cycle. Timely assessment of ET products can provide the scientific basis for quantitative analysis of hydrological cycle processes and water resources assessment. In this paper, four high spatial resolution remote sensing ET products--the Moderate-resolution Imaging Spectroradiometer global terrestrial evapotranspiration product (MOD16), the ET product based on Penman-Monteith-Leuning equation version 2 (PML-V2), the ET product based on the Breathing Earth System Simulator (BESS) and the ET product of the Global LAnd Surface Satellite (GLASS)--were firstly assessed using the eddy covariance (EC) of different vegetation types in the Lancang-Mekong River Basin (LMRB). To fully assess the performances of these four products, spatiotemporal inter-comparisons and literature comparisons were also conducted across different climatic zones. The results are summarized as follows: (1) MOD16 does not perform well as compared to the other three products, with its Root Mean Square Error (RMSE) being higher than GLASS, PML-V2 and BESS, which are approximately 0.47 mm/8-day, 0.66 mm/8-day, and 0.90 mm/8-day, respectively; (2) the performance of each product varies across different vegetation types, and even within the same climate zone. PML-V2 performs best in evergreen broadleaf forests, BESS performs best in deciduous broadleaf forests and croplands, and GLASS performs best in shrubs, grasslands and mixed vegetation; (3) each product can well reflect the spatial difference brought by topography, climate and vegetation over the entire basin but all four ET products do not show either a consistent temporal trend or a uniform spatial distribution; (4) ET ranges of these four products over LMRB are consistent with previous literature in evergreen broadleaf forests, deciduous broadleaf forests, needleleaf forests and mixed forests in other regions with the same climate zones, but they show great differences in croplands, grasslands and shrubs. This study will contribute to improving our understanding of these four ET products in the different climatic zones and vegetation types over LMRB.  
Keywords:evapotranspiration; accuracy assessment; Lancang-Mekong; river basin; eddy covariance; literature comparison +
Footnote †: journal: Remote Sens._**2022**, _14_, 479. [[https://doi.org/10.3390/rs14030479](https://doi.org/10.3390/rs14030479)]([https://doi.org/10.3390/rs14030479](https://doi.org/10.3390/rs14030479))
# Low-Carbon Certification Systems in Agriculture: A Review
## 3 Existing Low-Carbon Certification Systems
### EU Countries
#### 3.1.2 Low-Carbon Label (France)
The method takes into account factors such as crop type, livestock species, and specific farming practices to tailor emission calculations to individual farms. The certification process typically involves an audit or assessment conducted by qualified professionals who evaluate the farm's practices and the carbon reduction efforts taken. The assessment may include data collection, farm visits, and review of documentation to ensure compliance with the certification standards. To calculate the carbon footprint using the Carbon Agri method, farmers need to collect relevant data for their agricultural practices, such as crop areas, livestock numbers,  
Figure 4: The Carbon Agri methodological framework. feed consumption, and energy use. This information is then used in combination with the calculation factors provided by the Carbon Agri method to estimate GHG emissions. The Carbon Agri method uses either the CAP'2 ER(r) tool [52] at level 2 (mixed farming and livestock farming) or any other tool that has been acknowledged as methodologically equivalent and certified by an independent third-party organization. The application of the Carbon Agri method allows farmers to assess their emissions, identify emission hotspots, and develop targeted strategies to reduce their carbon footprint. Farmers aiming to obtain the \"Label Bas Carbone\" certification must demonstrate their commitment to reducing carbon emissions through the implementation of sustainable practices. Relevant activities that may be implemented include precision agriculture techniques to optimize fertilizer and water usage, conservation tillage or cover cropping to improve soil health and sequester carbon, and exploring the use of renewable energy for on-farm operations.
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 5 Conclusions  
Coherent noise removal is very important for seismic data processing. The traditional coherent noise removal methods often cause some damage to the effective signal while suppressing coherent noise or cannot suppress the interference wave effectively at all. In this removal, based on the multi-scale and multi-direction properties of the NSST and its simple mathematical structure, a coherent noise removal method in NSST domain is proposed which can effectively remove the coherent noise while ensuring the effective signal is not damaged. The effectiveness and practicability of the proposed method on coherent noise attenuation of the source array seismic data are fully proven using synthetic examples and filed data examples. According to the processing results of the synthetic seismic data and the filed seismic data, the denoising method proposed in this article can effectively protect the effective signal, enhance the continuity of in-phase axis, and effectively improve the signal-to-noise ratio and resolution of seismic data while removing the coherent noise, which is of great significance to accurately identify the geological information contained in seismic data.  
Conceptualization, M.Y.; methodology, M.Y., X.G. and X.W.; software, M.Y.; validation, M.Y., X.G. and X.W.; formal analysis, M.Y.; investigation, M.Y.; resources, M.Y.; data curation, M.Y.; writing--original draft preparation, M.Y.; writing--review and editing, M.Y.; visualization, M.Y.; supervision, X.G.; project administration, X.G.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (42074151).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction  
The safety and efficiency of an aircraft heavily depend on the state of its engine [1]. A decline in engine performance or a defect can significantly reduce the performance of the aircraft, and in severe cases can lead to safety accidents. Therefore, accurately monitoring the state of an aircraft engine is crucial. However, accurately recognizing and monitoring an aircraft engine is not easy, as such engines have complex structures and operate under various environmental conditions [2].  
To solve this problem, 3D LiDAR (light detection and ranging) sensors have recently been receiving attention. Such 3D LiDAR sensors measure the distance to an object using the speed of light and provide accurate 3D information in real time. Because of these advantages, 3D LiDAR sensors are widely used in various applications such as autonomous vehicles, robotics, and geography. In particular, 3D LiDAR sensors can be very useful tools for recognizing aircraft engines [3]. However, 3D LiDAR sensors generate a 3D point cloud composed of many points. Processing this 3D point cloud is very difficult [4].  
To solve this, a deep learning model called PointNet was proposed. PointNet extracts features by directly utilizing the information from each point to process the 3D point cloud [5]. The extracted features are processed through 3D convolution, and finally 3D object detection is performed.  
In this paper, we propose a method to recognize aircraft engines in real time from 3D LiDAR data using PointNet. Through this, we expect to be able to monitor the status of an aircraft engine in real time and improve the safety and efficiency of the aircraft based on this [6].  
This research will make an important contribution to improving the safety and efficiency of aircraft. In particular, the results of this paper will provide an important application area for jetbridge autonomous driving technology. The jetbridge is a moving passage that allows passengers to board or disembark from an aircraft. The jetbridge's autonomous driving technology needs to accurately recognize the location of the aircraft engine. For this, the method of recognizing aircraft engines using PointNet proposed in this paper will be very useful. Through this, we expect to improve the safety and efficiency of jetbridge autonomous driving technology.