esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 1 Introduction
Due to the significant reduction in the field of view required for single imaging, it is easier to meet the constraints of camera volume and weight. The swing scanning achieves large area coverage by swinging the optical axis of the high-resolution camera along the wingspan direction. During the scanning process, the camera performs multiple exposure actions to obtain high-resolution images of different orientations in the wingspan direction. At the same time, the camera system follows the platform forward to achieve coverage in the flight direction, and then forms a large range of high-resolution images through stitching methods. According to different image registration methods, image stitching is generally divided into three types: grayscale-based stitching methods, transform domain-based stitching methods, and feature-based stitching methods. The grayscale-based registration method is used to select a template in a fixed window from the reference image, search from the image to be registered, and use some evaluation method as the registration measurement to find the optimal registration position. The main stitching algorithms include the cross-correlation method, mutual information method, block matching method, ratio matching method, and grid matching method [18; 19; 20; 21]. The mosaic method based on the transform domain is used to carry out some transformation on the image. The parameters of this mathematical transformation are separated by the method of parameter separation, and then the invariant is constructed. After matching the transformation model, the subsequent mosaic process is carried out. The main algorithms include wavelet transform, phase correlation, trace transform, and polar coordinate transform [22; 23; 24; 25]. The feature-based stitching method is used to extract certain invariant features, such as interest points, lines, textures, etc., then match and calculate the transformation parameters between images, and finally complete the stitching. Feature-based stitching is currently the most widely used method, including Moravec, Harris, SIFT, SURF, ASIFT, GLOH, PCASIFT, and other algorithms [26; 27; 28; 29; 30]. The second part of this article mainly introduces the design of the wide-area and high-resolution imaging system. Firstly, the optical design of a high-resolution camera is introduced, mainly including the optical design, imaging quality analysis, thermal analysis, and tolerance analysis--the mechanical and electronic design of the camera are also introduced. Secondly, this paper introduces the design of the stable platform and the design of the swing scanning method. The third part of this article mainly introduces the flight verification results, including the image stitching results and high-resolution results.
# Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry
## Materials and Methods  
As shown in Figure 1, the galvanic frame is formed by 4 tubular beams welded to compose a rectangle. In the general configuration, on the shorter sides, several hooks are joined. The workers use inert metal wires to knot together a variable number of items. Successively, each wire is linked to a couple of corresponding hooks (i.e., the nth on the upper side with the nth on the lower) so that the wire results in an arrangement on the frame along an approximately vertical direction. Once all the couples of hooks are filled, the galvanic frame is sent to the electroplating bath. Considering that items can be very small in size (down to 10 mm on the shorter side), the variability in length (thus in mass) of the wire itself precludes the adoption of any weight-based approach for the counting system. Moreover, two consecutive items can be attached at a relative distance down to 20 mm.  
Consequently, attention has been focused on computer vision-based approaches. The main idea is to properly acquire a 2D digital image of the frame, on which to detect each item by means of computer vision (CV) tools [7].
# Situationally Sensitive Path Planning
## 4 Methodology
### Global Environment Layer
#### 4.1.2 Global Environment Graph Definition  
To extend Dijkstra to situational preference-based path planning, let us define the weighted graph with respect to situation domain values. Consider the designed virtual environment as a weighted graph with environmental waypoints taking the place of the graph's nodes. Let us define each waypoint pair connected by an edge as a region segment \(s\). Each segment \(s\) must terminate with a waypoint at each end and must connect to at least one other segment. Model pedestrians (supplied by the local model) will traverse between waypoints in the environment along these segments and will adjust their path determination at each waypoint. Conceptually, this translates to habitual motion along the path, with a context switch to situational factors at the waypoint.  
```
procedureDijkstraGraph \(G\), node source, node, target \(dist\leftarrow[]\), \(prev\leftarrow[]\), \(U\leftarrow[]\) fornode\(n\in G.Nodes\)do \(dist[n]\leftarrow\infty\) \(prev[n]\leftarrow\) undefined \(U.push(n)\) \(dist[source]\gets 0\) whileU is not empty do \(c\leftarrow\underset{u\in U}{min}\;dist[u]\)  remove \(c\) from \(U\) for\(\{(c,u)\mid u\in U,(c,u)\in G.Edges\}\)do \(d\gets dist[c]+G.Edges[(c,u)]\) if\(d<dist[u]\)do \(dist[u]\gets d\) \(prev[u]\gets c\) endif endfor if\(c==target\)breakable endwhile \(S\leftarrow[]\) \(n\leftarrow\)target if\(prev[n]\) is defined do while\(n\) is defined do \(S.push(n)\) \(n\leftarrow\)\(prev[n]\) endwhile endif return\(S\) endprocedure
```  
**Algorithm 1** [PERSON]'s Algorithm for Shortest Path
# Zirconium-Containing Metal-Organic Frameworks (MOFs) as Catalysts for Biomass Conversion
## 4 Conversion of Biomass into Chemicals over UiO-66 and MOF-808  
Scheme 3 summarizes some of the main existing routes for converting biomass into chemicals using MOFs as heterogeneous catalysts. Following this very simple (and obviously incomplete) scheme, and to illustrate the great potential of archetypal UiO-66 and MOF-808, in this review we have selected some representative examples from the literature, which have been organized into three blocks, depending on the biomass source, as follows:  
- Conversion of carbohydrates;  
- Conversion of lipids (fatty acids and glycerol);  
- Conversion of other biomass-derived compounds.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Lewis Acidity** & **Bronsted-Induced Acidity** \\  & **(Dehydrated Material)** & **(Hydrated Material)** \\ \hline \multirow{3}{*}{UIO-66} & \(\star\) Few acid sites (only at defect sites) \\  & \(\star\) Restricted pore space & Strongly polarized H\({}_{2}\)O molecules \\  & (hindered Lewis sites) & (strong Bronsted sites) \\ \hline \multirow{3}{*}{MOF-808} & \(\checkmark\) All Zr\({}^{4+}\) sites available \\  & \(\checkmark\) Wide pores & \(\star\) Weakly polarized H\({}_{2}\)O molecules due to \\  & (efficient Lewis sites) & geminal \(\checkmark\)OH groups \\ \cline{1-1}  & (weak Bronsted sites) \\ \hline \end{tabular}
\end{table}
Table 1: Acid properties of hydrated and dehydrated UiO-66 and MOF-808.
## 5 Experiments
### Baseline Results  
Tables 7 and 8 summarize the performance of all baselines on the three datasets. By looking at these two tables, we observe:  
* Our customized IR system outperforms the off-the-shelf version and the improvement is larger on the English questions.
* For the MCMLE dataset, pretrained models significantly outperform non-pretrained models and all neural models outperform the non-neural baselines. However, for the USMLE and TWMLE datasets, the non-pretrained neural models (Max-out) cannot even surpass the IR baseline and, most surprisingly, most of the pretrained models for the USMLE dataset cannot even beat the IR baseline.  
Overall, even the strongest pretrained model (BioBERT-Large, RoBERTa-Large) cannot harvest good scores on any of the three datasets, validating the great challenge of our proposed data. Notably, we did not include human performance for comparison due to the high variance of scores for human examinees (best medical students can earn almost full marks while worse ones cannot pass the exams).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Dev** & **Test** \\ \hline Chance & 25.0 & 25.0 \\ \hline PMI & 36.6 & 36.9 \\ IR-ES & 38.3 & 37.2 \\ IR-Custom & 39.1 & 37.8 \\ \hline Max-out & 51.8 & 50.9 \\ \hline BERT-Base-Zh & 66.5 & 65.8 \\ BERT-Base-wwm-ext & 64.4 & 64.0 \\ MBERT-Base & 62.1 & 62.3 \\ \hline RoBERTa-Large-wwm-ext & **69.3** & **70.1** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of baselines in accuracy (%) on the MCMLE dataset.
## 3 Results and Discussion
### Wettability Between B-Containing PR Solution and Carbon Fibers  
Although increasing temperature could decrease the surface tension of the B-containing PR solution, absolute ethanol is extremely volatile to increase in viscosity of the solution, which is unfavorable for impregnation. Thus, B-containing PR solution with 0.8 g/mL at 35 \({}^{\circ}\)C could be selected to impregnate the carbon fiber braid from the points of char yield and impregnation effect. To make preparations for future impregnation of a B-containing PR solution, the wettability process with B-containing PR solution at a concentration of 0.8 g/mL and carbon fibers treated at 500 \({}^{\circ}\)C for 1 h to remove the polymer surface  
Figure 4: FTIR spectra of common PR and B-containing PR.  
sizing agent was tracked by a surface tension tester at 35 \({}^{\circ}\)C. Figure 5 shows the whole wetting process at different times; it can be clearly seen that B-containing PR solutions can spread out quickly on the surface of carbon fibers, taking only 7.5 s to reach an equilibrium state, along with the contact angle reaching 0\({}^{\circ}\). The above results indicate good wettability between B-containing PR solution and carbon fibers, laying a solid foundation for subsequent successful impregnation of B-containing PR into carbon fiber braids.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Temperature (\({}^{\circ}\)C)** & **Concentration (g/ML)** & **Surface Tension (mN/m)** \\ \hline \multirow{6}{*}{Common PR} & 25 & 0.40 & 32.10 \\  & 25 & 0.80 & 29.45 \\  & 35 & 0.40 & 29.23 \\  & 35 & 0.80 & 26.23 \\  & 45 & 0.40 & 25.06 \\  & 45 & 0.80 & 23.12 \\ \hline \multirow{6}{*}{B-containing PR} & 25 & 0.40 & 28.45 \\  & 25 & 0.80 & 25.62 \\  & 35 & 0.40 & 26.02 \\ \cline{1-1}  & 35 & 0.80 & 23.17 \\ \cline{1-1}  & 45 & 0.40 & 22.71 \\ \cline{1-1}  & 45 & 0.80 & 20.01 \\ \hline \end{tabular}
\end{table}
Table 1: The surface tensions of common PR and B-containing PR solutions at different concentrations and temperatures.  
Figure 5: Shape of B-containing PR droplet on carbon fibers at different times. (**a**) 0 s, (**b**) 0.15 s, (**c**) 0.3 s, (**d**) 0.6 s, (**e**) 1.2 s, (**f**) 1.95 s, (**g**) 2.4 s and (**h**) 7.5 s, respectively.
## 5 Results
### Case B1: Self-Discharge Test  
The results achieved during the self-discharge test are shown in Figure 12 and resumed in Table 5. In particular, the proposed FESS is preliminary fully charged at its maximum speed, then the self-discharge test is started by disconnecting the IRLA-SPM from its DC/AC converter. As a result, the speed slowly decreases due to all the frictional torques acting on the rotor shaft. Both Figure 12 and Table 5 reveal that \(\omega_{m}\) drops at its rated/minimum value after approximately 47 h, meaning that the FESS self-discharge daily rate is approximately 0.51 pu (=24 h/47 h). However, since FESS is able to provide energy even below the rated speed (but not at rated power), FESS self-discharge rate could be also estimated as 0.30 pu (=24 h/81 h). In any case, such high self-discharge rates suggest that the proposed FESS should not be used for storing and then delivering energy over time horizons longer than few hours unless strictly needed. Otherwise, most of the stored energy would be wasted due to frictional losses.  
Figure 10: FESS torque (\(T_{e}\)), speed (\(\omega_{m}\)), power (\(P_{m}\)), DC-link voltage (\(V_{DC}\)) and currents (\(i_{g}\) and \(i_{m}\)) achieved during round-trip tests (cases A1 and A2, quantities normalized to their corresponding rated values).
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 5 Simulation Results and Discussion  
To evaluate the performance of the proposed PAM-8 VLC system, a co-simulation platform of MATLAB and Cadence was adopted. This platform was based on the VLC system structure presented in Figure 10, and it combined the digital baseband in MATLAB with the AFE in Cadence to incorporate the advantages of these two software design tools. The assessment of the proposed VLC system consisted of a transceiver system performance evaluation, a data transmission evaluation, and a training performance evaluation. In addition, to prove the superiority of the RBF-NN, a comparison with a DNN as a post-equalization method was also performed.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 1 Introduction
### Analysis of Components and Positioning System in a UUV System  
The control architecture of an Unmanned Underwater Vehicle (UUV) is illustrated in Figure 1, specifically focusing on a Remotely Operated Vehicle (ROV).  
At the core of this system is the ROV Central Control Unit, which serves as the primary processing hub. This unit is responsible for receiving commands from the Surface Control Module--the interface through which the operator interacts with the vehicle. The Surface Control Module connects to a computer and controller, allowing the operator to send commands and receive real-time feedback about the ROV's status and environmental conditions.  
Importantly, the power supply powers both the CCU and the Electronic Speed Controllers (ESCs), ensuring that all systems receive the necessary energy to operate effectively.  
Key to the operation of the ROV is (in this case) the Inertial Measurement Unit (IMU), which provides critical information regarding the vehicle's orientation and motion. These data feed into the Inertial Navigation System [15], which utilizes the IMU's information, along with inputs from other sensors, to accurately determine the vehicle's position and trajectory in the underwater environment [44; 45; 46]. The ROV Central Control Unit processes this information and integrates it with operator commands to ensure the vehicle moves as intended.  
The Electronic Speed Controllers (ESCs) play a vital role in regulating the power supplied to the thrusters and the propulsion mechanisms of the ROV. By adjusting the speed and direction of these thrusters based on commands from the control unit, the ESCs enable precise maneuvering of the vehicle [94]. Additionally, various sensors collect data on environmental factors such as depth, temperature, and pressure, which are essential for safe navigation. These sensor data are continuously fed back to the ROV Central Control Unit, allowing for real-time adjustments to the vehicle's operation.  
For visual monitoring and navigation, the ROV is equipped with a camera managed by a Camera Controller Board. The camera provides essential visual feedback to the operator, especially in dark or murky underwater environments, where the accompanying lights enhance visibility. The control unit can adjust the camera's settings and manage its operation according to mission requirements.  
Finally, an error correction module is integrated into the system to ensure reliable performance. This component works by correcting any discrepancies or errors detected in the data or control commands, thereby enhancing the accuracy of the ROV's navigation and control. Overall, this block diagram represents a sophisticated control system that facilitates the effective operation of an ROV, allowing it to respond dynamically to both operator inputs and the challenges of underwater environments.  
Figure 1: Block diagram of an Unmanned Underwater Vehicle (UUV) control system.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## Appendix C  
This appendix contains the well-known four-point-mesh interpolation method, which was applied in this paper to determine the shape, \(a\), and rate, \(b\), parameters from matrices \(\mathbf{A_{h}}\) and \(\mathbf{B_{h}}\) for a given set of coordinates \((i,\phi)\). More interpolation methods are widely available in the literature.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 1 Introduction
### Recent Advancements and Challenges in Deep Learning  
Recent technological advancements have driven the evolution of image analysis, particularly with the introduction of deep learning algorithms [19]. Deep learning offers the advantage of automatic feature extraction, exemplified by the TASSEL framework, which combines CNN-based feature extraction with OBIA classification [20]. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), hold significant potential for land cover classification and object detection using high-resolution imagery [21]. However, challenges remain in enhancing the generalization and robustness of these models, necessitating the use of diverse training datasets [22]. Additionally, studies have presented techniques using pre-trained neural networks for land cover mapping on Landsat 5/7 images, potentially accelerating map production but limiting generalizability [23].  
In land cover mapping with deep learning, semantic segmentation excels by dividing images into meaningful segments, offering superior accuracy, especially in vegetation mapping [24; 25; 26; 27]. To address the limitations of single-scale convolution kernels, advancements like multi-scale fully convolutional networks (MSFCNs) have been introduced [28]. Similarly, the U-Net temporal attention encoder (U-TAE) and the remote sensing segmentation transformer (RSSFormer) approaches enhance semantic segmentation with temporal attention or are specifically tailored for high-resolution remote sensing applications, respectively [29; 30]. While CNNs are explored for per-pixel classification in high-resolution remote sensing, leveraging detailed data for precise object classification [31], challenges persist in acquiring specialized datasets due to high costs associated with data collection, demanding significant time, labor, and financial resources for activities such as field surveys and manual annotation of vegetation classes [29; 30; 31].
## 4 Case Studies
### Port of Milford Haven  
The Port of Milford Haven (www.mhpa.co.uk, accessed on 25 November 2023) is the busiest port in Wales, handling over 38.90 million tonnes of cargo in 2022. It is the largest energy port in the UK (handling 30% of total UK gas demand) and its South Hook LNG terminal is the largest LNG terminal in the continent Europe. In 2020, 85% of gas consumption in Wales came through the Milford Haven port.  
In 2021, the Welsh Government approved a net zero target for 2050 with interim targets of 63% reduction by 2030 and 89% reduction by 2040 from 990 level. The Port of Milford Haven is the first port facility in the UK to pledge to the green port work standards. Decarbonisation measures at the Port of Milford Haven include the following:  
* **Electric vehicle**: In 2021, seven new electric commercial vehicles were in service at the port.
* **Renewable energy supply**: In 2019, the port switched its energy to a renewable energy tariff, indicating that all the electricity usage comes from renewable sources.
* **Renewable energy production**: In 2021, RWE launched the Pembroke Net Zero Centre to maximise the potential of hydrogen, floating offshore wind, and carbon capture to help decarbonise industries in Wales. In 2023, a major hydrogen fuel generation scheme, which could make up to five tonnes of the gas a day at the former Puma Energy site in Milford Haven, was submitted to county planners.
* **Infrastructure**: By 2019, the port had invested in solar panels, LED lighting, heat pumps, insulation, and smart energy innovations to minimise carbon emissions. In 2022, the Port of Milford Haven started the construction of a new supersize slipway and new workboat pontoons at Pembroke Port, as part of the GBP 60 million Pembroke Dock Marine project. The infrastructure will enhance the Port's operations and maintenance capabilities for the floating offshore wind industry.
* **Welsh and UK Governments support**: Expand South Wales' grid capacity by 10 GW by 2030; implement a fast-tracked consenting regime; incentivise the production and use of low-carbon fuels; back the Skills Accelerator and Supply Chain Accelerator programmes.
###### Abstract  
This study aimed to assess the clinical value of three-dimensional printed personalised vascular models (3 DPPVMs) in assisting with the pre-operative planning and simulation of endovascular interventions. CT angiographic images of four cases, namely, abdominal aorta aneurysm (AAA), carotid artery stenosis, coronary artery stenosis, and renal artery stenosis, were selected, and 3 DPPVMs were obtained. A total of 21 clinicians specialising in interventional radiology and vascular surgery were invited to participate in the study, comprising 6 radiologists and 15 vascular surgeons. Of these, 66.7% had not used a 3 DPPVM prior to their participation. Considering all areas of experience and all four models, it was observed that 75% of the participants gave a ranking of 7 or above out of 10 with regard to the recommendation of the use of the 3 DPPVMs. The mean scores of the participants' ranking of the models ranged from 3.2 to 4.3 out of 5. The AAA model was ranked the highest for realism (4.10 \(\pm\) 0.89, \(p\) = 0.002), the planning of interventions and simulations (3.90 \(\pm\) 1.12 and 4.05 \(\pm\) 0.95), the development of haptic skills (3.56 \(\pm\) 0.98), reducing the procedure time (3.47 \(\pm\) 1.12), and clarifying the pathology to patients (4.33 \(\pm\) 0.69, \(p\) all >0.05), indicating consistency amongst the participants. The carotid artery model was ranked the highest for accurately displaying anatomical structures (4.3 \(\pm\) 0.73). All the 3 DPPVMs enhanced the understanding of the disease demonstrated, with rankings between 3.8 and 3.95. All the models aided in elucidating the intervention procedure required and in the planning of vascular interventions, with rankings of 3.5 and 3.9. The highest rankings were given by qualified clinicians with 8 or more years of experience. This study shows the potential value of using 3D-printed vascular models in education for clinicians and patients, as well as for clinical training and the pre-surgical simulation of endovascular stent-grafting procedures.  
3D printing; education; training; vascular intervention; communication; surgery 2025