esa-sceva/satcom-chunk-collection huggingface dataset:
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 4 IPN HandS Dataset
### Annotation Process and Temporal Refinement  
Skeleton annotations were created using the annotation tool described in Section 3. The process began by initializing all frames in a video sequence with landmarks estimated by the MediaPipe Holistic model [13]. Each annotated hand was represented by 21 landmarks per frame, enabling precise modeling of finger articulation and hand posture. While this provided a solid baseline, the automatic predictions often exhibited frame-to-frame inconsistencies or errors, especially in motion-blurred frames or complex poses, as further analyzed in Section 4.3. These estimates were then reviewed and refined by a single expert annotator using our interactive editing tools, which allowed for adjustments to individual landmarks, fingers, or entire hands as needed. The use of a single experienced annotator ensured consistency across all videos and eliminated inter-annotator variability. Moreover, the high efficiency of our tool made it feasible to inspect and revise annotations at frame-level granularity, supporting both spatial and temporal refinement at scale.  
Beyond skeleton annotation, we restructured the gesture taxonomy to improve class separability, similarly to our previous work [31]. In the original dataset, some dynamic gestures like throwing-up, throwing-down, and related movements included an initial grabbing motion that was visually and semantically distinct. To capture this nuance, we introduced a new gesture class, _grab_, which isolates this initial component from the subsequent throwing gestures. Figure 4 illustrates this refinement, where a single sequence previously labeled as \"throwing-up\" is now separated into \"grab\" followed by \"throwing-up\". This change enhances classification accuracy by better aligning gesture labels with observable motion patterns.  
We also performed a systematic revision of the temporal boundaries for all gesture instances. In many cases, the annotated start and end points failed to capture the full motion or included irrelevant transitions. Thus, each gesture sequence was manually inspected and adjusted to ensure its temporal limits precisely reflected the actual onset and offset of the gesture. As shown in Figure 5, these corrections eliminate ambiguity and  
Figure 4: Gesture taxonomy refinement. The initial grabbing motion, previously embedded within the throwing gestures, is now separated as a distinct “grab” class, enabling more precise gesture definitions and improving classification accuracy.  
reduce noise in the training data, especially for gestures with subtle or short transitions such as zoom-in and zoom-out.  
In total, annotating the full IPN HandS dataset (comprising 200 videos) required 5160 min (approximately 86 h) using our annotation tool, with an average of 25.8 min per video. The ability to annotate such a large-scale dataset with high precision and manageable effort underscores the practical value of our tool and supports the public release of the annotations for future research in skeleton-based gesture recognition.
## 7 Experimental Results and Discussion
### Complexity Considerations  
The complexity reduction that MSA-DPD brings with gaining similar performance as compared to CPWL method is significant contribution. The complexity of the DPD model construction of the models is shown in Table 9, which is mainly measured by the required number of real multipliers, as the multipliers take up most of the hardware resources. Table 9 signifies that MSA DPD (220 multiplications) has much lesser complexity as compared to CPWL (880 multiplications). The advanced variations in Volterra series can be obtained by changing memory depth and nonlinearity order to higher numbers. However, the computational complexity has to be considered as shown in Equation (11). This means that while selecting the DPD model and its complexity, a smart trade-off between complexity and performance can be made accordingly. For a comparative evaluation of NN and GMP methods in terms of complexity and performance, we evaluate expressions for each method in term of its complexity. The number of coefficients and multiplications in CPWL architecture are higher as compared to GMP, however, the complexity in CPWL results in better performance as compared to GMP (as seen in results section in Figures 11 and 12). The reduction in complexity is brought into the play as proposed in MSA method. The performance of MSA achieves similar performance as compared to CPWL, however, the complexity in terms of coefficients in MSA is reduced from 520 to 260 and multiplications are reduced from 880 to 220. Similarly, looking at NN architecture, the performance (see Figures 11 and 12) is comparable to MSA which is in similar proportions to NN, however, the number of coefficients rise to 8526 and number of multiplications reached to 8224. NN complexity is a challenging issue, and this can be reduced as shown in this work that a very limited number of hidden layers and neuron per layer are employed. The multiplications and complexity of the NN increases exponentially with the increase in number of hidden layers and neurons per layer, depending on the application, MSA with much less complexity and similar based DPD can be employed as they achieve a similar performance as compared to NN.
## 2 Materials and Methods
### Biological Activation of Electrodes  
Subsequently, the electrodes previously electrodeposited with GO were biologically activated to check their biological viability. The 3 PLA electrodes selected (0.2, 0.6 and 1.4) were placed in the same chamber to ensure that the conditions were the same for all of them. For this purpose, an inoculation was performed with a 50:30:20 ratio of river sediments, a bioanode output previously grubbed with mature biofilm and a synthetic medium, respectively. The sediments were obtained from a nearby river (Torio river, Leon, Spain), and the synthetic medium was composed of 2.14 g K\({}_{2}\)HPO\({}_{4}\), 1.06 g KH\({}_{2}\)PO\({}_{4}\), 0.02 g CaCl\({}_{2}\), 0.015 g NaHCO\({}_{3}\), 0.5 g NaCl, 10 mL mineral solution and 1 mL vitamin solution per liter as described in other experiments of our group [27]. The cell was fed with a total of 0.9 g of sodium acetate per cycle in 3 takes when the current was falling. These experiments were performed in 500 mL glass bottles (SIMAX, Czech Republic) with a platinum wire as a counter electrode and Ag/AgCl as a reference as explained above in Section 2.2. The working potential was set at 0.1 V vs. Ag/AgCl. After inoculation, the cells were flushed with nitrogen for 30 min to remove oxygen.  
After 3 batch inoculation cycles (21 days), the current stabilized at around 7 mA and the inoculation phase was concluded. Next, the operation process was started in which 7-day batch cycles were carried out, at which time the complete medium was changed, and in which the bioanode was fed with sodium acetate every 3-4 days. Thirteen cycles (91 days) were performed. During the medium change, parameters such as dissolved oxygen, conductivity (Hach, HQ40d--two-channel digit multimeter, Hach Lange Spain, Barcelona, Spain) and pH (pH-Meter BASIC 20 +, Crison, Hach Lange Spain, Barcelona, Spain) were measured to study the evolution of the cell. Samples were also taken at the end of each cycle to measure the total organic carbon (TOC), inorganic carbon (IC) and total nitrogen (TN) using an analyzer (multi N/C 3100, Analytik, Jena, Germany). Finally,  
Figure 1: Electrode design and dimensions.  
thermogravinetry was performed to check whether electrode degradation had occurred using a thermogravimetric analyzer (SDT650, TA instruments, New Castle, DE, USA). The thermogravimetry was carried out using a temperature ramp of 10 \({}^{\circ}\)C min\({}^{-1}\), with a starting temperature of 25 \({}^{\circ}\)C and an end temperature of 900 \({}^{\circ}\)C in a nitrogen atmosphere. In order to be able to compare the sediment in the cells, thermogravimetry was also carried out for the GO and the PLA/carbon black filament separately. The used cell samples were collected at the end of cycle 9, and after centrifugation and drying were analyzed.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 3 Results
### GPU Exhaustive vs. GPU with Honeybee Search Algorithm  
This experiment also uses all the 314 videos of the ALOV dataset and all the tests use NCC and the GPU. The tests labeled BEE use Parallel HSA configured to run for two generations, and the ones labeled NO BEE use exhaustive search. Keep in mind that the full capacity of the GPU is not used in BEE because of the synchronization needs.  
Notice in Figure 12 that the average difference of F-score between BEE and NO BEE is relatively small. This difference is 1.54% of the overall standard deviation, an irrelevant difference. This validates that BEE performs as good as the exhaustive NCC tracker.  
The difference of average time per frame, displayed in Figure 13a, is of 42.22% of the overall standard deviation. However, there is a remarkable difference in the standard deviation of both groups. In other words, BEE provides a lower and more stable time per frame (about 7.58 times steadier). This result validates that using Parallel HSA provides an improvement in time per frame against a plain GPU-accelerated NCC. Figure 13b shows how using the GPU accelerates the implementation that uses HSA and how it compares against exhaustive search. This outcome shows how the polynomial time complexity of GPU BEE is lower than GPU NO BEE, which is lower than CPU NO BEE, supporting the theoretical analysis \(O(mnwh)>O(\frac{mmwh}{c})>O(\frac{mm}{q})\).  
Finally, Figure 14 shows that using parallel HSA (GPU BEE) provides a greater Score-Time Efficiency meaning it is the best alternative. This simple observation is an intuitive condensation of the previous analysis of both the F-score and average time per frame observations.  
Figure 12: (**a**) Comparison of F-score between using parallel HSA (GPU BEE) or parallel exhaustive search (GPU NO BEE). \({}^{}\)Both’ shows the average and standard deviation of GPU NO BEE and GPU BEE combined. \({}^{}\)(**b**) Comparison of F-score survival curves between using parallel HSA (GPU BEE), parallel exhaustive search (NO BEE) or sequential HSA (CPU BEE).  
Figure 13: (**a**) Comparison of average time per frame between using parallel HSA (GPU BEE) or parallel exhaustive search (GPU NO BEE). \({}^{}\)Both’ shows the average and standard deviation of GPU NO BEE and GPU BEE combined. (**b**) Comparison of average seconds per frame survival curves between HSA (BEE) and exhaustive search (NO BEE). There are four survival curves, these are also marked as sequential (CPU) or parallel (GPU).
# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 3 System Realization
### Task Model Decomposition  
Each product's manufacturing process includes multiple manufacturing resources' action coordination. To extract and apply the manufacturing process experience, task and action models need to be decomposed, hierarchically divided, and described by a basic combination of action primitives. Additionally, the manufacturing process provides a clear hierarchical structure for subsequent knowledge reuse. This work embodies the hierarchical structure and logical relationship between the actions by ontologically modeling the manufacturing process's actions.  
Specifically, as shown in Figure 4, the execution actions' levels can be divided into the following: task-based actions (_ActionsOnTask_), station-based actions (_ActionsOnStation_), and the bottommost action primitives (_PrimitiveActions_). Task-based actions state the manufactured product's purpose or a function. Station-based actions are derived from the higher-level actions according to the processing procedure and are combined with the manufacturing process's characteristics. The actions are defined for each manufacturing station, and the action primitives are used for processing. The process is refined and decomposed, and actions are decomposed and combined from the manufacturing resources' level. Reusable action control information is obtained by decomposing different manufacturing processes to the primitive level and then reorganizing the action primitives to construct the new task.  
Figure 4: Action model hierarchy.  
Figure 3: Task-based ontology model.  
As an example, Figure 5 outlines a simplified engraving processing task. The task's execution needs to be based on the material information, the processing object's model, the processing equipment model, and the manufacturing process information. From the user's order, through a series of processes such as order arrival, loading, cutting, product packaging, and unloading, a product that meet the user's needs is obtained. These processes are engraving process's sub-tasks from the hierarchical structure perspective. Different processing procedures consist of various multi-action manufacturing resources' execution processes (e.g., the cutting procedures' subdivision). From the material's arrival to the end of the processing and returning to the pallet, it is necessary to move the manipulator and clamp the material successively. The material is obtained, placed, processed using the manufacturing tools, taken out, and finally put back into the pallet, forming a series of non-decomposable, primitive actions in the processing procedure. Thus, the complex manufacturing process involving multiple resources is decomposed into tasks and becomes a combination of action primitives, each corresponding to a single action of an individual device. Each new processing task is decomposed and refined, and the original action primitive combination is used to reuse the original manufacturing resource knowledge.
## 3 Results and Discussion
### Characterization of nZVISLP
#### 3.1.2 SEM and EDX  
SEM analysis revealed the morphological characteristics and surface features of nZVISLP. As shown in Figure 3a,b, before adsorption, the surface of the material possessed a rough structure and exhibited a different micromorphology. It was also observed that nZVISLP exhibited different sizes in the range of 86-113 nm, and the particles were clumped in aggregates. EDX analysis (Figure 3c and Table 1) indicated the presence of Fe, C, O\({}_{2}\), and Ca in nZVISLP. C and O could have been due to the carbonaceous and oxygenated phytochemicals of sweet lime pulp extract. The small O\({}_{2}\) peak observed in Figure 3c may also signify the occurrence of oxidized Fe particles [30].
## 2 Study Area and Data  
To test the performance invariant polygon and adjacent bands, we evaluated Landsat\(-\)8 OLI as the reference image and the Sentinel\(-\)2A as a distortion image. The study area is located at Baiquan, in Heilongjiang Province, China. Images were from September 2019 (Figure 1). In this area, crops, including rice, corn, and soybean, are close to harvest, resulting in mixed pixels with soil properties, contributing to the generalizability of our results. Field surveys identified five types of ground material: water, grass, trees, pure soil, and artificial. A regression relationship of minimum and maximum reflectance [20] proved to be the most accurate to predict land cover. Through field experiments, we established the relationship between two images based on the reflectance of different land cover types, including minimum through maximum reflectance values.  
To focus on radiometric normalization, we geometrically registered two images covering the same spatial range. To match tiles, we upscaled Sentinel\(-\)2A images using the nearest neighbor method to obtain the same spatial resolution as the matching Landsat\(-\)8 OLI image. Data were downloaded from the European Space Agency ([[https://sentinels.copernicus.eu/](https://sentinels.copernicus.eu/)]([https://sentinels.copernicus.eu/](https://sentinels.copernicus.eu/)), accessed on 26 September 2019) and the United States Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 22 September 2019), both with absolute radiometric calibration. Here, we regard the Landsat\(-\)8 OLI image as a reference and Sentinel\(-\)2A as the subject. Six data bands were chosen for analysis (Table 1). Due to higher spatial resolution, the 0.785-0.900\(\upmu\)m band was selected from Sentinel\(-\)2A imagery.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 2 Uplink and Downlink Channels in Satellite-Ground FSO Communications
### Atmospheric Turbulence
Optical signal attenuation caused by the atmosphere is significantly greater on ground\(-\)satellite uplink than on satellite\(-\)ground downlink. For uplink and downlink FSO channels, the wavelength and refractive index structural parameter should be taken into consideration in order to assess the strength of intensity fluctuations and to calculate the optimal value of Rytov variance and the scintillation index.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Resins**} & \multirow{2}{*}{**Aging 1**} & \multicolumn{3}{c}{**Shear Bond**} & \multicolumn{3}{c}{**Weibull Parameters 3**} & \multicolumn{3}{c}{**Failure Mode 4**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**Megan (SD)**} & \multicolumn{2}{c}{**\(m\) (95\% CI)**} & \(\sigma_{0}\) & \(r^{2}\) & \(\sigma_{0,05}\) & **A** & **M** & **C** \\ \hline \multirow{4}{*}{Panavia} & NA & 19.7 (4.3)\({}^{\rm aA2}\) & 4.9 (4.5–5.3) \({}^{\rm aA2}\) & 21.4 & 0.98 & 11.7 & 15 & 0 & 0 \\  & TC & 17.2 (5.0)\({}^{\rm aA}\) & 3.7 (3.3–4.0)\({}^{\rm bA}\) & 19.0 & 0.97 & 8.5 & 15 & 0 & 0 \\  & MLC & 13.4 (4.1)\({}^{\rm bAc}\) & 3.3 (3.2–3.5)\({}^{\rm bA}\) & 14.9 & 0.99 & 6.1 & 11 & 4 & 0 \\  & TC/MLC & 12.7 (3.9)\({}^{\rm cA}\) & 3.4 (2.3–3.7)\({}^{\rm bA}\) & 14.2 & 0.99 & 5.9 & 6 & 9 & 0 \\  & MLC/TC & 11.4 (3.3)\({}^{\rm cA}\) & 3.6 (3.3–3.9)\({}^{\rm bA}\) & 12.7 & 0.98 & 5.5 & 7 & 8 & 0 \\ \hline \multirow{4}{*}{RelyX} & NA & 22.0 (4.2)\({}^{\rm aA}\) & 5.7 (5.3–6.1)\({}^{\rm aA}\) & 23.8 & 0.99 & 14.1 & 15 & 0 & 0 \\  & TC & 20.5 (4.5)\({}^{\rm aA}\) & 4.9 (4.4–5.4)\({}^{\rm aB}\) & 22.4 & 0.97 & 12.2 & 15 & 0 & 0 \\ \cline{1-1}  & MLC & 16.6 (3.8)\({}^{\rm bB}\) & 4.7 (4.4–5.0)\({}^{\rm bB}\) & 18.1 & 0.99 & 9.6 & 13 & 2 & 0 \\ \cline{1-1}  & TC/MLC & 11.7 (3.1)\({}^{\rm cA}\) & 4.1 (3.8–4.4)\({}^{\rm bB}\) & 12.8 & 0.98 & 6.2 & 9 & 6 & 0 \\ \cline{1-1}  & MLC/TC & 10.2 (2.9)\({}^{\rm cA}\) & 3.9 (3.4–4.3)\({}^{\rm cA}\) & 11.3 & 0.97 & 5.3 & 8 & 7 & 0 \\ \hline \hline \end{tabular}  
* NA: non-aged; TC: thermal cycling: MLC: mechanical load cycling; TC/MLC: TC then MLC; \({}^{\rm c}\) MLC/TC: MLC then TC. \({}^{\rm 2}\) For each cement, the same lowercase superscript letters (a, b, and c) within the same column show no significant differences among the aging conditions (\(p>0.05\)). For each aging method, the same uppercase superscript letters (A and B) within the same column show no significant differences between the two resin cements used (\(p>0.05\)). \({}^{\rm 3}\)\(m\): shape parameter was considered to be significantly different when the CI (confidence interval) values did not overlap. \(\sigma_{0}\): scale parameter (in MPa); \(r^{2}\): correlation coefficient; \(\sigma_{0,05}\): probability of failure at 5% (in MPa). \({}^{\rm 4}\) A: adhesive failure; M: mixed failure; C: cohesive failure.  
\end{table} 
Table 3: Shear bond strength (in MPa) of the two resin cements to the zirconia surfaces, together with Weibull parameters and failure modes for each group (\(n=15\)). Figure 3: Representative SEM images of the fracture mode (RelyX U200) (magnification: upper: 30\(\times\); lower 2000\(\times\)): (**a**) and (**d**): mechanical load cycling (MLC); (**b**) and (**e**): thermal cycling (TC)/MLC; (**c**) and (**f**): MLC/TC. Figure 2: Weibull cumulative failure probability curves for the shear bond strength (SBS) values: (**a**) Panavia F2.0 and (**b**) RelyX U200.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 2 Overview of CCUS in China
### CCUS Development Plan in China  
China has approximately 40 CCUS demonstration projects in operation or under construction (Figure 2), with an annual CO\({}_{2}\) capture capacity of 3 million tonnes [50]. Most of these projects are small-scale CO\({}_{2}\) capture demonstrations in the electricity, coal, and petroleum industries. In China, every five years is set as one development period for CCUS, and a specific short-term development plan is designed for each development period. According to the \"Overall CCUS Technology Development Roadmap in China\" released in 2019, the four development periods from 2030 to 2050 are planned as follows. By 2030, the existing technologies are expected to move into the commercial stage to undergo industrialization; by 2035, certain new technologies are expected to be implemented at large scale; by 2040, breakthroughs in CCUS systemic integration and risk management technology are expected to be achieved; by 2050, the CCUS technology will be extensively deployed, and multiple industrial CCUS clusters will be completed.  
\begin{table}
\begin{tabular}{c c} \hline \hline \begin{tabular}{c} **CO\({}_{2}\) Storage Location** \\ \end{tabular} & \begin{tabular}{c} **CO\({}_{2}\) Storage Capacity (Billion Tonnes)** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Songliao Basin \\ Tarim Basin \\ Bohai Bay Basin \\ Subei Basin \\ Ordos Basin \\ \end{tabular} &
\begin{tabular}{c} 694.5 \\ 552.8 \\ 490.6 \\ 435.7 \\ 335.6 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dominate CO\({}_{2}\) storage location and storage capacity in China [50].  
\begin{table}
\begin{tabular}{c c} \hline \hline \begin{tabular}{c} **Country** \\ **(Million Tonnes)** \\ \end{tabular} & \begin{tabular}{c} **CO\({}_{2}\) Emissions in 2020** \\ **(Ten Billion Tonnes)** \\ \end{tabular} & \begin{tabular}{c} **CCUS Theoretical Storage Capacity** \\ **(Ten Billion Tonnes)** \\ \end{tabular} \\ \hline \begin{tabular}{c} China \\ United States \\ United Kingdom \\ Australia \\ India \\ Japan \\ \end{tabular} & \
\begin{tabular}{c} 10,667.89 \\ 4712.77 \\ 329.58 \\ 391.89 \\ 2441.79 \\ 1030.78 \\ \end{tabular} &
\begin{tabular}{c} 121–413 \\ 230–2153 \\ 7–7.8 \\ 22–41 \\ 39–60 \\ 14 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: CO\({}_{2}\) emissions and CCUS theoretical storage capacity in some countries [50; 55; 56].  
Figure 2: _Cont._