esa-sceva/satcom-chunk-collection huggingface dataset:
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 2 Related Work
This model deeply integrates the advantages of TCN, attention mechanisms, and BiLSTM to overcome the constraints of existing solutions in multiple dimensions. First, in terms of temporal information processing, BSTCA-HAR employs BiLSTM as one of its core components. Through its unique bidirectional structure, BiLSTM captures both forward and backward dependencies in sequential data, effectively enhancing the ability to model long-term temporal dependencies. This feature makes the model more accurate in parsing complex activity patterns, efficiently bolstering its capacity to capture prolonged temporal correlations. Second, to meet the need for automated and efficient feature extraction, BSTCA-HAR integrates a TCN module. TCN, with its unique causal convolution mechanism, can effectively expand the receptive field without increasing the model's complexity, thereby automatically extracting features at different temporal scales. Furthermore, the model incorporates an attention mechanism to further enhance its feature extraction capabilities. This enables the model to adaptively assess the significance of features across varying temporal intervals, increasing its sensitivity to key features and enhancing both feature extraction ability and overall model performance. In summary, the BSTCA-HAR model constructs an efficient end-to-end HAR system that not only simplifies the data preprocessing process but also significantly improves the model's recognition performance and generalization ability in complex scenarios. It effectively addresses the shortcomings of existing solutions in feature extraction, temporal dependency modeling, and feature importance adjustment. Experimental results show that the BSTCA-HAR model performs excellently on multiple publicly recognized datasets with good generalization capability, verifying its effectiveness and advancement in addressing current HAR issues.
# Experimental Tests on Solvent Solutions for the Recycling of Coated Polypropylene in Food Packaging
## 2 Material and Methods  
### Sampling  
In order to investigate the recycling procedures of plastics for food packaging, three different types of plastic polypropylene film samples were supplied by Jindal Films Europe Brindisi s.r.l.: two of these samples had a surface coating treatment, but one did not. Polypropylene (pp) is one of the most common thermoplastic polymers; it is obtained by the polymerization of monomeric units of propylene. Polypropylene has good mechanical characteristics, resistance to chemical agents, is an excellent thermal and electrical insulator, and does not absorb water [19]. Due to these properties, it is one of the most common materials in the production of films for food packaging. Although the barrier properties, especially against oxygen, are poor, polypropylene covers about 18% of the world food packaging market. To be suitable for the purpose of food packaging, polypropylene films are coated with aluminum or multiple types of lacquers, such as acrylic lacquer. Both coating types greatly reduce pp's permeability to gases and allow the foods' organoleptic properties to last longer [20].  
Samples of polypropylene film before and after metallization by aluminum and lacquering processes were picked directly from the industrial coils. The three samples were denominated as follows: \"basic film\" (Figure 1A), consisting solely of polypropylene, and \"metallized film\" (Figure 1B) and \"lacquered films\" formed of polypropylene coupled, respectively, to aluminum and lacquer.  
The thickness of the polypropylene film samples was measured with a mechanical micrometer (Manutan, 0-25 mm).  
The samples were assigned univocal codes as follows: BF (basic film, 18 um thick) for polypropylene film without an aluminum or lacquer covering layer, AC (aluminum-coated, 19 um thick) for metallic film with aluminum, and ACRC (acrylic-coated, 30 um thick) for lacquered film with acrylic lacquer.  
The images related to the lacquered film specimens are not shown in the figure because they are all transparent and there are no appreciable differences with the naked eye.  
Figure 1: (**A**) Picture of polypropylene film specimen intended for metallization (BF); (**B**) picture of metallized polypropylene film specimen (AC).
## 5 Conclusions  
Based on six flux sites in northwest China, this study examined the differences in seasonal and interannual carbon fluxes across the typical ASA ecosystems in northwest China. The results indicate that the carbon fluxes of these dryland ecosystems have clear seasonal dynamics and vary inter-annually, and all ecosystems function as carbon sinks on the annual time scale. Moreover, we also assessed the performance of these remotely-derived BESS, MODIS, and GOSIF GPP products, and got some evidence. Except for at the S_Ar and S_Dsl sites, some distinct advantages of the GOSIF GPP were picked up, not only in the seasonal dynamic but also in annual mean difference. The universal GOSIF-GPP relationship and less impact of land cover helped the GOSIF GPP algorithm. Furthermore, both the MODIS and BESS products largely underestimated the GPP during the growing period. Compared with the MODIS GPP, the BESS GPP was more reliable for swamp meadow and alpine grassland ecosystems. However, large uncertainties in the BEES, MODIS, and GOSIF GPP products remain. Therefore, precise input parameters should be used to improve the products, such as accurate land cover types and LUEmax at high spatial resolution. Meanwhile, the MODIS GPP will be greatly improved by considering the differences in sun and shade leaves, and C3/C4 plants. The accuracy of the BESS GPP can be also improved by adding the soil moisture effect in water-limited areas, and the accuracy of the GOSIF GPP could be further improved with high-quality meteorological data. All these analyses would be helpful for providing reliable GPP products in the ecologically-fragile dryland ecosystems and supporting policies for sustainable development.  
Conceptualization, Q.G. and X.T.; methodology, Q.G. and X.T.; validation, Q.G.; formal analysis, Q.G. and M.W.; data curation X.W.; writing--review and editing, Q.G, H.Z., M.M., and L.Y. All authors have read and agreed to the published version of the manuscript.  
This study was jointly supported by the Fundamental Research Funds for the Central Universities (XDJK2020C015); Foundation of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (GTYR201908); and Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (WSGS2020007). This work used flux data acquired from the Heihe Watershed Allied Telemetry Experimental Research (Hi-WATER). All scientists and technicians maintaining the flux site management, data collection, and long-term observations are greatly appreciated. We also thank the principal contributors of the MODIS products, the Distributed Active Archive Center of the Oak Ridge National Laboratory and the Earth Observing System Data, and the BESS products from the Environmental Ecology Lab at Seoul National University.  
No potential conflict of interest was reported by the authors.
## 2 Emerging Applications of 6G
### Smart Cities and Intelligent Transportation Systems  
The concept of smart cities exploits the data generated by a multitude of IoT devices to improve the quality of life of people. These collected data are used to automate transportation, healthcare, factories, and many other parts of an urban area [28]. Intelligent transportation systems (ITS) are an impotent part of smart cities, where IoT applications are used to improve the transportation system of a city. Such applications can range from optimally managing traffic congestions within city limits to the safety of vehicles and pedestrians, and managing logistics associated with goods and services transportation [29]. As defined by the European Telecommunications Standards Institute (ETSI), the application layer of ITS mainly focuses on three types of services: road safety, traffic efficiency, and other applications [30]. Road safety applications, such as informing a hard brake to fellow motorists or identifying the failure of a critical function such as steering, requires latency in the range of 50-100 ms, while traffic efficiency applications such as emergency vehicle warnings should adhere to delay constraints of 100-500 ms. In addition to latency require ments, with recent trends towards using big data and different data analytic techniques and algorithms, the capacity of supporting networks should also improve in parallel [31, 32].  
The QoS requirements of emerging applications discussed in this section are summarised and listed in Table 1. In the next section, we provide a brief overview of the latest WLANs that can cater for latency, reliability, throughput, and other QoS requirements of these emerging technologies.
## 2 Materials and Methods  
### Sample Collection  
Samples were collected from 26 stations during an oceanographic expedition on board the R/V Investigator (IN2016_v04) in September 2016. Sample sites were located on two shore-normal longitudinal transects at the southern (Tr_S: 36.9\({}^{\circ}\) S, 150.9\({}^{\circ}\)-154.25\({}^{\circ}\) E) and middle (Tr_M: 32.4\({}^{\circ}\) S, 152.87\({}^{\circ}\)-154.10\({}^{\circ}\) E) section of the voyage, as well as a Lagrangian drift along the east Australian current (30.65\({}^{\circ}\)-32.99\({}^{\circ}\) S), and two sites in the northern sector of the voyage (Tr_N: 28\({}^{\circ}\) S-155\({}^{\circ}\) E) (Figure 1) (Supplementary Table S1). For each site, water from specific depths up to 4000 m was sampled, using a rosette equipped with 24 \(\times\) 12 L Niskin bottles and a CTD fitted with additional optical sensors (Fluorometry, Oxygen). Sixty samples were collected for the microbial fraction (\(>\)0.2 \(\upmu\)m) and six samples for the viral fraction (\(<\)0.2 \(\upmu\)m) (Supplementary Table S1).  
For the larger fraction, 2 L of seawater were filtered through a 0.2 \(\upmu\)m dia. pore size Polyethersulfone (PES) filter (Sterivex, Millipore, Burlington, MA, USA) and stored at \(-80\)\({}^{\circ}\)C [24]. Usually 3-4 depths were sampled at each station (2-3 in the euphotic zone and the others in the abiotic zone). For the viral fraction, 20 L of 0.2 \(\upmu\)m filtered seawater was treated with 1 mL of iron chloride (10 g/L) for 1 h [26; 27], to flocculate the viral particles at room temperature. The flocculate was collected by filtration on a 0.8 \(\upmu\)m dia. pore size polycarbonate filter (147 mm dia., ATTP14250, Millipore), and stored at 4 \({}^{\circ}\)C until subsequent processing (within two months of sampling). A total of 5 viral samples were collected from the surface (\(-2\) m depth), and one sample was collected at 3000 m. Two of the surface samples were collected in a warm core eddy (CTD02 and CTD03).
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 5 Experimental Results and Discussion
### Results and Performance Analysis
#### 5.2.1 Impact of Complexity Control Parameter  
The ability of the proposed algorithm to trade-off the coding efficiency to the encoding complexity by adjusting the complexity control parameter \(\delta\) is a crucial advantage compared to the state-of-the-art methods, which use fixed thresholds and rigid decision trees to derive the CU size decision. Increasing \(\delta\) allows the encoder to increase the coding efficiency, at the expense of increased encoding time and vice-versa. Thus, \(\delta\) becomes an engineering design parameter which is tuned depending on the user requirement. In this case, the experimental results presented in this paper correspond to \(\delta\) values maintained at \(\delta=100\%\) and \(\delta=20\%\).
## 3 Materials and Methods  
The proposed methodological framework builds on and improves the data modeling methodology introduced in [35] (MR-GCN-LSTM), as shown in Figure 2. This multi-stage behavioral prediction process involves the following: first, modeling the video as a series of spatial scene graphs; second, generating a multi-relational interaction graph from this spatio-temporal information to represent the evolution of spatial relationships between entities over time; finally, designing a dynamic multi-relational graph convolutional network (DMR-GCN) to predict vehicle behavior using a graph-based learning model.  
The proposed methodological framework's performance enhancement is due to three key innovations:  
1. The dynamic multilevel relational graph (DMRG) design accurately captures the complex dynamics of vehicle lane changes by representing different spatio-temporal granularities through multiple layers and updating edge weights in real time;
2. The method for generating temporal interaction graphs focuses on capturing temporal changes and complex interaction patterns such as vehicle acceleration, sharp braking, and behaviors under challenging traffic conditions (e.g., rainy days and nights). This comprehensive approach enhances the model's understanding of dynamic vehicle interactions, improving prediction accuracy and adaptability to diverse traffic scenarios;
3. The DMR-GCN structure includes a dynamic multi-relational graph convolutional network (DMR-GCN), dynamic scene perception, and an interactive learning mechanism.  
Figure 2: The pipeline of overall network architecture.  
DMR-GCN employs an adaptive adjacency matrix updating mechanism and a multi-relational feature fusion mechanism to achieve dynamic relational capture and comprehensive feature processing, enhancing adaptability to real-time data. The interactive learning mechanism allows the model to adjust to changing traffic environments, improving vehicle behavior prediction accuracy and real-time performance.
## Materials and Methodology
### Extraction of Water Surface Areas from SWI, VH, and VV Images Using Different Methods  
In order to prove that the SWI image is better than the original image of S1A (including the VH image and VV image) to extract water, four classification methods were used, i.e., the threshold classification method (TCM), IsoData unsupervised classification (IUC), Minimum Distance Classification (MDC), and Support Vector Machine (SVM). The test zone was a rectangle with an area of 110 km \(\times\) 70 km location in the Poyang Lake region.  
Similar to Figure 2 and its theory, the threshold values were confirmed as \(-\)23 dB and \(-\)17 dB based on VH and VV imagery, respectively. For a single band, a pixel was classified as water if its value  
Figure 2: Frequency histogram of SWI imagery on 27 September 2016. The dashed line represents water and the unbroken line represents non-water.  
was less than \(-23\) dB in VH imagery or \(-17\) dB in VV imagery, respectively. For double bands, a pixel was classified as water if its value was less than \(-23\) dB in VH imagery and \(-17\) dB in VV imagery.  
IUC is barely affected by subjectivity [43; 44] and the classification results utilizing IUC demonstrate the ability of the imagery to distinguish between water and non-water areas. Water was extracted from the original image of S1A and SWI image using the same default parameters of IUC model in ENVI software.  
MDC and SVM are supervised classification methods that are used in many studies [23; 45; 46]. The resamples (1937 pixels) used in MDC and SVM were randomly selected based on the ground-truth water extracted from OLI. The resamples are the same when extracting water from the original image and the SWI image using MDC and SVM.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 1 Introduction
Progress in diffuse optics and related imaging technologies, including photoacoustic (PA) imaging, which allows high-resolution imaging of blood vessels deep inside the body [21; 22], may promote reconsideration of the role of DOT and its image reconstruction. In such a changing situation, it is worth reviewing what has been achieved in DOT image reconstruction for research in diffuse optical imaging and related fields. This review provides useful information to select algorithms for clinical applications of DOT and will assist researchers working in emerging DOT-related research fields, including AI, high-performance computation, and different optical imaging technologies, to expand their research into DOT. The general framework and theoretical aspects of DOT image reconstruction are described in detail in the literature [23; 24]. Previous review articles [2; 25; 26; 27] have provided highly informative guides for implementing DOT image reconstruction. Here, we review the methods for the forward and optimization processes with some studies on DOT image reconstruction, including some recent reports that were not included in previous reviews. The authors tried to cover as wide a range of topics and studies in this review as possible, although this may have inflated rather than simply fledged this review. Figure 1 illustrates the flow of image reconstruction for DOT and related topics for each of the processes mentioned in this review.
## 2 Brief Background of USV Technology
### Key Technological Components  
The effectiveness of USVs is largely determined by advancements in key technologies, such as hull design, propulsion systems, communication networks, and onboard sensors [12] (Figure 2):  
* Hull design influences hydrodynamic efficiency, stability, and stealth capabilities [16], with materials ranging from lightweight composites [33] to reinforced aluminum [34]. Optimized hull shapes minimize drag and enhance fuel efficiency, allowing for improved endurance and speed.
* Propulsion systems vary from electric motors [35] for silent operations to hybrid and diesel engines for long-range missions [36]. Electric propulsion, powered by lithium-ion batteries or hydrogen fuel cells, is gaining popularity due to its low noise signature and reduced environmental impact.
* Communication technologies, including satellite links, 4G/5G networks, and radio frequency transmissions, enable real-time control and data transfer. These advancements ensure continuous connectivity between USVs and command centers [37].
* Sensor integration, comprising radar, LiDAR, cameras, and sonar, enhances situational awareness and autonomous navigation. Multi-sensor fusion enables USVs to create a comprehensive understanding of their surroundings, improving collision avoidance and target tracking [38; 39]. The convergence of these technologies continues to push the boundaries of what USVs can achieve in various maritime sectors.  
Figure 2: Key technological components from a typical USV [12].
## 2 Experimental Methodology
### Experimental Set Up  
The experiments for this study were conducted under a range of well-controlled, fully turbulent, and rough flow conditions to evaluate the performance of the instrumented particle at a 7 m useful length Armfield flume in the Water Engineering Lab at the University of Glasgow. The purpose of these experiments was to assess the transport rates and hopping characteristics of an instrumented particle of different solid densities, for a range of flow rates above entrainment threshold conditions (just above the threshold of motion and well below continuous transport). The flow rate through the test section was controlled by adjusting the frequency of the pump using a controller for the pump inverter. The observations of this experiment were obtained from tests carried out using a flume with the following channel specifications: 10 m full length, up to 0.4 m deep, and 0.9 m wide. The flume walls comprise smooth transparent glass, which allows observing particle transport from the side.  
The bed surface is hydraulically roughened using gravel and glass beads of similar size, in a well-packed arrangement (Figure 3). Specifically, the flat bed surface was roughened using two sets of well-packed glass beads, having a rectilinear (rhombooidal or tetrahedral) arrangement to render the bed surface relatively uniform and avoid complexities of bed formations affecting transport characteristics. Two bed surface roughnesses have been simulated using two distinct particle sizes to compose each (see bed surfaces A and B in Figure 3a,b, respectively). Bed surfaces A and B comprise unisize well-packed particles having nominal diameters of 1.5 cm and 3.15 cm, respectively, resulting in distinct macroscopic hydraulic roughness. The test section over which the instrumented particle is transported by saltation has dimensions of at least 150 cm and 50 cm in length and width (Figure 3), respectively, for each bed surface. At the downstream end of the flume, the test section is located at 1.75 m from the tailgate of the flume. Figure 4 shows photographs taken from video produced from the top cameras over each of the test sections, during actual experimental runs. The instrumented particle being transported over each bed surface roughness is also clearly shown (black sphere, with white marks to also help visually validate the rate of instrumented particle rotations).  
Figure 3: Illustration of the used bed surfaces: (**a**) A and (**b**) B, each comprising of well-packed tetrahedral arrangement of unisize marble beads of 1.5 and 3.15 cm, respectively, resulting in distinct bed surface roughness.
## 3 Position Estimation of the Occluded Parts
### Geometric Feature Extraction Method  
The first-order differential chain code sequence reflects the number of changes in the chain code's pointing values. \(a_{i}^{\prime}\
eq 0\) indicates that the contour's point may not be on the same line as the next contour's point and may be a corner of the contour. Thus, the contour corner points' potential locations can be originally determined using the differential chain code.  
Figure 8: Four typical examples of contour noise.  
Figure 7: Eight-directional pointing value Freeman’s chain code.  
In Equation (14), \((X,Y)\) denotes the feature point set of the contour after the pose transformation. The cumulative distance error between \((X,Y)\) and the set of measured contour feature points is calculated as follows:  
\[E_{s}=\sum_{i=1}^{K}\sqrt{\left(x_{i}^{\prime}-X\right)^{2}+\left(y_{i}^{\prime}- Y\right)^{2}},i=1,2,3 \tag{15}\]  
All geometric feature points on the part contour are traversed in the counterclockwise direction. As the distance judgment threshold between matched point pairs, the initial value of cumulative error is utilized. Matched point pairs with low curvature similarity or a curvature similarity greater than the error threshold are eliminated, and matched point pairs that satisfy the geometric feature restrictions are removed. RANSAC (random sample consensus) is utilized to fit the pose transformation matrix of the matched point pairs.