esa-sceva/satcom-chunk-collection huggingface dataset:
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 4 Experiments
### Implementation Details  
At evaluation, the 2D keypoints were obtained from both ground truth values and the MediaPipe framework for comparative analysis. MediaPipe is an open-source platform developed by Google that offers advanced and robust hand pose estimation capabilities. The proposed normalization and weakly supervised method are generic and adaptable to different 3D pose estimators. To demonstrate this, two representative 3D pose estimators were utilized as backbones: (1) ST-GCN [3] (1-frame), a pioneering network that leverages GCN-based architecture to encode global and local joint relations, and (2) VPose3D [23] (1-frame), a fully-convolutional advanced network. The specific model settings were referenced from PoseAug [43].  
The models were trained by the PyTorch deep learning framework. During the training process, the AdamW optimizer with a weight decay of 0.05 was used to optimize the network. The input image size was \(224\times 224\), and the initial learning rate was set to 0.001. An RTX3090 GPU was employed for training the model, utilizing a batch size of 512 across a total of 500 epochs. Mean Squared Error (MSE) was employed as the loss function for model training. These settings remain consistent for both the weakly supervised training and the normalization strategy. Regarding the weakly supervised training, 200,000 samples were generated for each individual.
# Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications
## 3 Results and Discussion
### Challenges in Digitalization of the Food System
#### 3.5.1 Challenges Related to Infrastructure and Cost  
The papers reviewed reported very substantial challenges to digitalization of the food system in terms of infrastructure and cost. Knowledge of these challenges provides insights into the preparedness of practitioners and can serve as input for researchers and inventors developing cost-effective technologies. The infrastructure-cost challenges mentioned included challenges relating to hardware and software, such as the required equipment, storage capacity, rapid detection sensors, and other facilities, which need to be resilient to the harsh agro-food supply chain environment. Poor infrastructure and internet connection in rural (remote) areas, lack of financial resources, high costs of transferring large amounts of data to the cloud, unsustainable energy expenses required for computing power, and high costs of the technologies and of infrastructure development were other challenges identified as threats to the sustainability of digitalization of the food system. It was frequently pointed out that all stakeholders need to work hand-in-hand to solve these challenges [41; 50; 59; 60; 61; 61; 102; 103; 104].
## 3 Results
### Functional Characterization of RgGES Genes  
To verify the biochemical activity of the RgGESs from _R. glutinosa_, _RgGES1_ and _RgGES2_ were subcloned into a pET28a plasmid to construct pET28a-_RgGES1-2_ vectors, and subsequently expressed in _E. coli_ BL21(DE3) cells. The recombined RgGES proteins were purified using a His-tag (Supplementary Figure S7) and further incubated with the substrate GPP. The enzymatic products were detected by GC-MS. In comparison with the control reaction, both RgGES1 and RgGES2 catalyzed GPP to produce a new peak (Figure 10), which showed identical retention time and mass fragmentation patterns to those of the authentic standard geraniol, suggesting RgGES1 and RgGES2 converted GPP to geraniol.  
Figure 8: Phylogenetic analysis of the putative RgGESs with other known terpene synthases from different subfamilies. The tree was constructed using the Neighbor-Joining method in the MEGA 7.0. TPS a–g stand for terpene synthase subfamilies. Accession numbers of various TPSs are provided in Supplementary Table S15.  
Figure 9: Amino acid sequence alignment of RgGESs and other GESs. The CLC Sequence Viewer program was used to conduct the multiple sequence alignment. Highly conserved motifs of TPSs were indicated with boxes, including DDoxD, RxR, and NSE/DTE. ObGES, _O. hsalicum_, AAR11765.1; CrGES, _C. roseus_, AFD64744.1.
## 2 Materials and Methods
### Inclusion Criteria  
Types of studies to be included: Randomized controlled trials (RCTs) and studies published in English were considered eligible for inclusion.  
Participants/population: Healthy adult patients diagnosed with peri-implant mucositis (Pi-M) in at least one dental implant.  
Intervention(s)/exposure(s): Use of probiotic therapy as an adjunct to submarginal mechanical instrumentation (mechanical prophylaxis and/or debridement (ultrasonic devices, curettes, and polishing) with probiotic therapy (MD + P).  
Comparator(s)/control: Only submarginal mechanical instrumentation therapy (mechanical prophylaxis and/or debridement without probiotic therapy (MD))) or in combination with placebo (MD + placebo).  
Outcome measures/primary outcome(s): Changes in bleeding on probing (BOP), plaque index, and probing pocket depth (PPD). Changes in subgingival plaque bacterial population.  
Secondary outcome(s): Adverse effects as secondary outcomes.  
_Exclusion criteria_: (1) Studies without mechanical debridement or professional plaque removal; studies that only used probiotics; (2) studies in which patients received other adjunctive therapies (e.g., laser/probiotics or use of antibiotics/nonsteroidal anti-inflammatory drugs) or any type of periodontal decontamination or treatment within the last 3 months; (3) patients with systemic disorders such as diabetes mellitus, liver or kidney diseases, or cardiovascular or autoimmune diseases that may influence treatment outcomes; (4) heavy smokers (\(\geq\)10 cigarettes per day); (5) pregnant or lactating women; (6) studies including individuals presenting peri-implantitis; (7) studies with letters, case reports, and short communications; and (8) animal model and in vitro studies.  
_Search strategy_: The search included all articles indexed in PubMed/MEDLINE, LI-LACS, ScienceDirect, and Web of Science. Articles published in the English language up to January 2023 were searched. We used the following search descriptors: \"mucositis\" OR \"peri-implant mucositis\", each combined with Boolean operators (OR, AND)--\"probiotic\" OR \"prebiotics\" OR \"lactobacillus\" OR \"bacillus\" OR \"enterococcus\" OR \"bifidobacterium\" AND \"mechanical curettage\" OR \"scaling\" OR \"periodantal debridement\" OR \"mechanical debridement\" OR \"plaque removal\" OR \"nonsurgical mechanical debridement\".  
After electronic searches, manual searches were conducted on the reference lists of selected articles.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Communication Medium
#### 3.5.2 Underwater Communication  
Table 1 lists transmission rates, distances and propagation speeds of the main media that can be used for underwater communication. It shows that electromagnetic wave cannot propagate well in seawater. Transmission rates at very low frequency (VLF) and extremely low frequency (ELF) are only about 300 bps for transmission ranges shorter than 50 m. Blue/green laser can provide much higher transmission rates, but is absorbed by seawater and susceptible to obstacles, and cannot propagate over long distances, either. Relatively, acoustic wave can propagate over long distances at kbps transmission rates (e.g., tens of kbps for 2-km transmission distances) but at much slower propagation speeds, and cannot support real-time applications over long distances [20].
## 2 Methodology
### Hybrid Bag of Features (HBoF)  
To generate the health state-based feature matrix, two different sets of features are considered. For the characteristics of the AE domain, the amplitude (F1), rise time (F2), and duration (F3) of the signals are computed. For the threshold value, the rms of the signal is considered. The specifics of the AE features are demonstrated in Figure 3.  
For statistical analysis, from the frequency domain, the numerical features obtained are root mean square (F4), kurtosis (F5), skewness (F6), shape factor (F7), impulse factor (F8), root mean square (F9), kurtosis (F10), and skewness (F11). Thus, in total, 11 features are extracted to create the designed HBoF. In Table 1, the numerical details of these statistical features (F4 to F11) are described.
## 2 Study Sites and Database Description
### French Study Site
#### 2.1.2 Sentinel-2 Images  
Eleven free optical images were obtained from the Sentinel-2A (2A) satellite on dates close to S1 images (less than 2 weeks). The S2A images were downloaded from the Theia (French land data service center) website, the French land data center ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/))). The Theia website provides S2A images that are corrected for atmospheric effects and ortho-rectified [42; 43]. Currently, Sentinel-1 (S1) and Sentinel-2 (S2) sensors provide images with revisit time of 6 and 5 days, respectively. In theory, it is possible to obtain one S1 image and one S2 image with difference in time less than one week. If the optical image closest to the radar image is cloudy, the optical image acquired two weeks before or after the radar image acquisition date could be used because in agriculture areas the vegetation does not change a lot within one or two weeks.  
First, the NDVI maps were derived from the S2A images. Then, from each calibrated S2A image the NDVI pixel values within each reference plot were averaged to characterize the vegetation condition of that plot. Finally, for each reference plot, to derive the NDVI values for the S1 acquisition dates, a linear interpolation was performed using the NDVI value corresponding to the S2A image  
Figure 1: Location of the French study site.  
acquired before the radar acquisition date and the NDVI value of the S2A image acquired after the radar acquisition date. For our reference plots, the NDVI values ranged between 0.13 (almost bare soil) and 0.92 (vegetation height \(\sim\)85 cm).
## 3 Results and Analysis
### Relationship between Chlorophyll and Spectral Reflectance in Peanut at Different Densities
#### 3.3.2 Spectral Characteristics of the Peanut Leaves at Different Chlorophyll Levels  
There were significant changes in the peanut leaf spectral reflectance at different chlorophyll levels. As shown in Figure 4, a high correlation was observed between the chlorophyll content and the original spectrum. Generally, leaf spectral reflectance decreased in the VIS due to the strong absorption of chlorophyll but increased significantly in the NIR region due to the influence of blade structure and moisture.  
Figure 3: Chlorophyll content of peanuts at different growth stages under different plant densities.  
According to the above results, the leaf chlorophyll content was the highest in S2 when compared with the other two single-grained treatments (Figure 4). For double-grained treatments, the leaf chlorophyll content was the highest in D2 (Figure 5). When combined with the original spectral reflectance curve of the peanut leaves, it was observed that differences in chlorophyll content had a greater effect on the spectral response of peanut leaves in different bands. The leaf spectrum showed different trends in different bands with increasing chlorophyll content. The higher the chlorophyll content of the peanut leaves in the VIS (350-700 nm) and NIR (700-1300 nm) bands, the greater the reflectivity.  
Figure 4: Spectral reflectance curves of peanuts of different plant densities under single seeding conditions.  
Figure 5: Spectral reflectance curves of peanuts of different plant densities under double seeding conditions.
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 4 Results
### Hollow Plate
#### 4.1.3 Simultaneous Parametric and Topology Optimization of a Hollow Plate  
At this design workflow, an automatic loop was created where a simultaneous PO and TO of the Hollow Plate was executed. The taken design space from each iteration was further used, at the same optimization level, as the topology region of the TO. The processes described in this workflow are (8) simultaneous parametric size/shape and topology optimization with no redesign (PO + TO_NR), (9) simultaneous parametric size/shape and topology optimization with redesign (PO + TO_R), and (10) simultaneous parametric size/shape and topology optimization with redesign, and parametric shape optimization PO + TO_R_PO. The same factors, as well as their rules, from the process (4), were also applied in the process (8). In addition, the percentage of mass reduction in TO was used as a new factor in a range from 10 to 90 with a value increment (step) equal to 10. The optimized value of this factor was 70%. The results were evaluated according to the structure's compliance, mass, and maximum stress and always with respect to the aforementioned stress rule. A redesign based on the result of process (8) was conducted in process (9). Finally, a parametrization of the geometry and a new round of a size optimization was implemented in process (10). Figure 13 illustrates the design solutions of the processes in this workflow.  
Figure 12: The four design processes in the PO workflow: (4) parametric size/shape optimization (PO), (5) parametric size/shape, and topology optimization with no redesign (PO_TO_NR), (6) parametric size/shape, and topology optimization with redesign (PO_TO_R), and (7) parametric size/shape, topology optimization with redesign, and parametric shape optimization (PO_TO_R_PO).  
Figure 13: The optimized design by the PO within the simultaneous PO and TO workflow, as well as its design results: (8) simultaneous parametric size/shape and topology optimization with no redesign (PO + TO_NR), (9) simultaneous parametric size/shape and topology optimization with redesign (PO + TO_R), and (10) simultaneous parametric size/shape and topology optimization with redesign, and parametric shape optimization PO + TO_R_PO.  
An overview of the results from all the implemented design processes in the Hollow Plate case study is presented in Table 3.  
It can be observed that the mass of the structure is decreasing as we are following the processes from (1) to (10), while the optimization time is increasing. On the other hand, there is not a clear pattern in the maximum stress results. However, they are always stuck to the given stress rule (\(\sigma_{\text{max}}\leq 125\) MPa).
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 3 Research History of PHM
### Traditional PHM Research  
Initially, PHM was proposed by the U.S. military, relying on intelligent information technology, which they used in the operation and maintenance of aero-engines. [PERSON] et al. proposed sensor selection and localization with a new methodology in PHM systems, which dramatically improved the detection capability of the sensors and minimized the number of required sensors [3]. [PERSON] applied data-based fault prediction algorithms to the study of UAV (Unmanned Aerial Vehicle) PHM systems, specifically by selecting particle filtering algorithms to approximate fit the implied fault mapping mechanism to the system data and to perform fault prediction [4]. [PERSON] et al. analyzed the drawbacks of improving the detection by increasing the number of sensor locations, targeting military aircraft, and integrating MEMS sensors with standard commercial microcontrollers to propose an innovative design for PHM data loggers with the advantages of low cost, low power consumption, and lightweight design [5]. [PERSON] et al. addressed the problem that maintenance technicians could not perform maintenance operations until after the equipment had failed and applied the PHM technology to designing an aircraft maintenance decision-making system based on real-time condition monitoring and successfully realized fault prediction, reporting, maintenance actions, and full-cycle health management [6]. [PERSON] et al. proposed that the maturity of PHM focuses on two key performance indicators (KPIs): the NFF (the ratio of undiscovered faults, i.e., the probability of unverified fault detection) and the probability of detection (POD) (which can be accomplished by calculating the global anomaly thresholds that intrude during the simulation of each of the different types of degradation) [7]. [PERSON] et al. focused on analyzing the relationship between the failure analysis (FA) technique and the PHM system and proved that the FA technique can provide a large amount of information for the PHM system [8]. [PERSON] et al. proposed a new idea for designing an aviation PHM framework based on a PHM big data center. They demonstrated the key technologies, scientific issues, and the application system for their engineering solutions in detail [9]. All of these provide a reference for the research and development of aviation PHM systems.  
Many scientists' research and application of PHM systems before 2015 mainly focused on optimizing the sensor performance and improving system analysis and prediction strategies. With the rise of big data and the continuous enhancement of computing power, including the continuous optimization and improvement of various deep learning algo  
Figure 5: Fault prediction and repair.  
rithms such as neural networks (NNs), support vector machines (SVMs), random forests, etc., machine learning (ML), which is the core part of artificial intelligence (AI), has been gradually used in the study of PHM and has demonstrated significant advantages.
## 1 Introduction  
As Industry 4.0 advances, inspection robots have become increasingly common in intelligent factories. Integrating automated equipment, robotics, industrial networks, and artificial intelligence technologies has led to notable improvements in production efficiency and operational management. Nevertheless, personnel safety remains a critical concern for enterprises. In particular, compliance with safety dress codes, such as the proper wearing of helmets and vests, is closely related to operational safety and the personal safety of employees. Since improper use of safety equipment can lead to severe accidents, developing a reliable and efficient system for monitoring safety attire in intelligent manufacturing environments is critical. Conventional methods for safety attire inspection mainly depend on manual inspections or rule-based computer vision approaches [1]. However, manual inspections are inefficient and prone to human error. The automated attire detection based on visual inspection can real-time identify the wearing status of safety helmets, protective clothing, and other personal protective equipment. A certain automobile manufacturing company in Nanchang, Jiangxi, China, reduced its accident rate by 30% and lowered manual inspection costs by 40% through the implementation of this technology. Vision-based methods often face reduced accuracy in complex factory settings because of lighting changes, occlusions, and varying perspectives [2; 3]. YOLO-based detection methods can automate safe attire detection. However, conventional YOLO networks have poor adaptability in complex environments. This paper proposes the SFA-YOLO model to address this problem. The main contributions of this paper are as follows:  
1. This study constructs a self-built safety attire detection dataset for detecting safety attire in intelligent factory environments.
2. This research proposes an SFA module to enhance the detection speed and feature representation capability of the network.
3. Comparative and ablation experiments are conducted to verify the effectiveness of the proposed method.
4. Generalization experiments demonstrated that the proposed method exhibits strong generalization ability in the field of industrial inspection and can be extended to other detection tasks.
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 2 Solid-State Ligand Exchange  
Ligand exchange is mainly classified as two different processes, namely, the solid-state and solution-phase ligand exchange procedures. Firstly, the ligand exchange procedures are mainly conducted in solid-state, which involves the following four steps (Figure 1): (i) fabrication of as-cast CQD thin films on the target substrate by spin-coating the CQD solution dissolved in a non-polar solvent; (ii) introduction of the polar solvent, including short organic ligands, such as 1,2-ethanedithiol (EDT) or mercaptopropionic acid (MPA) on the CQD films; (iii) soaking the CQD films with the polar solvent, including short organic ligands during optimized times, as well as the elimination of the solution by following spin-coating; (iv) washing the CQD film by the casting of neat polar solvents to remove exchanged long ligands and excess new short ligands. Generally, approximately 40 nm-thick CQD films are produced from the first step of the solid-ligand exchange process [8; 9], and the thickness can be controlled by repeating the process based on a layer-by-layer approach for achieving the desired thickness in solar cells with optimal light harvesting. Among the various studies on CQD photovoltaics produced from solid-state ligand exchange processes, some of the research progress that has been reported so far are summarized in the sections below.  
Figure 1: Schematic of solid-state ligand exchange and film fabrication. Initially, colloidal quantum dots (CQDs) were deposited on the substrate, and then ligand-exchanged using short organic ligands. CQD film fabrication is completed by repeating this method.
#### 4.2.4 Analysis of Overall Time Spent on Human-Operated Adjustment  
The safe separation discussed above is predicated on a conservative estimate of human-operated adjustment time. However, in real-world operations, the combined duration of the controller's monitoring, decision-making, and communication; the pilot's response and execution of instructions; and the aircraft's maneuvering is likely to be quicker. To illustrate this, Figure 15 presents simulations of collision risk under various scenarios with differing overall human-operated adjustment times. It is evident that, as the total time for human-operated adjustment decreases, both the risk of collision between the two aircraft and the required safe separation distance of the trailing aircraft from the intersection also diminish, as detailed in Table 8. These findings underscore the importance of the controller's and pilot's proficiency in emergency situations, as well as the aircraft's maneuverability in ensuring the safety of airway operations.  
Figure 14: Graph of collision risk variation with trailing aircraft distance to intersection under uniform distribution: (**a**) graph of conforming to the same speed distribution; and (**b**) graph of modifying speed distribution parameters for the trailing aircraft.