esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment
### Dual-Energy X-ray Absorptiometry (DXA)  
Dual-energy X-ray absorptiometry (DXA) is a commonly used imaging technique for assessing body composition, including fat, lean tissue, and bone mineral density. While DXA is primarily utilized for assessing overall body composition, it can also be employed in the evaluation of upper-limb lymphedema [55].  
In particular, DXA provides quantitative measurements of tissue composition, providing regional analysis of the upper limb, allowing for the differentiation of fat and lean tissue distribution across specific regions of interest [56]. Moreover, it can help identify localized changes in tissue composition and assess the extent and severity of lymphedema in different limb areas [56].  
Interestingly, by comparing the two sides, DXA can help quantify and monitor the asymmetry of tissue composition and assess the progression or response to treatment of upper-limb lymphhedema [57]. On the other hand, DXA involves relatively low levels of radiation exposure compared to other imaging modalities, such as computed tomography (CT). The radiation dose associated with DXA is considered minimal and well within acceptable limits for diagnostic imaging purposes [58].  
While DXA offers advantages in assessing upper-limb lymphhedema, it is essential to consider its limitations. DXA does not directly visualize the lymphatic system or provide information on lymphatic flow dynamics. Therefore, it is typically used in conjunction with other imaging techniques, such as lymphoscintigraphy or magnetic resonance lymphography, to provide a comprehensive evaluation of lymphhedema [59]. Additionally, DXA may only be readily available in specific healthcare settings, and its use in lymphhedema assessment may require specific protocols and expertise [55].
## 2 Materials and Methods
### Time Series GPP Dataset  
A satellite-based light-use efficiency model--the vegetation photosynthesis model (VPM)--was used to simulated time series GPP dataset from 2000 to 2018 for the study area. The VPM was driven using the MODIS MOD09A1 surface reflectance dataset, the MCD12Q1 land cover dataset, the MYD11A2 land surface temperature dataset, and NCEP reanalysis II climate data. The final 500m resolution GPP of each 8-day period during 2000-2018 was obtained. The details of the simulation processes using the VPM have been reported in previous studies [33; 34].  
Based on the 500 m 8-day period GPP dataset, the annual GPP for each year between 2000 and 2018 was obtained by aggregating all the data in a relevant year. In addition, the GPP\({}_{\text{max}}\) for each year between 2000 and 2018 was obtained by extracting the peak value for each pixel in each year. Since the vegetation in our study area usually has obvious characteristics of seasonal change, the temporal dynamic curve of GPP generally shows a unimodal shape. Therefore, in this study, the growing season is defined using a relative threshold method, as suggested in previous studies [35; 36]; the threshold is set as 20% of the GPP\({}_{\text{max}}\) in a given year. The carbon uptake period (CUP) for each year between 2000 and 2018 was obtained by counting the number of days in which the GPP was higher than the value of 20% of the GPP\({}_{\text{max}}\) in each year.
# Rearrangeable and Repackable S-W-S Elastic Optical Networks for Connections with Limited Bandwidths
## 4 RNB and RPNB SWS1(\(q\), \(p\), \(n\)) Switching Fabrics with \(2\leq m_{\max}\leq n\)
### RNB SWS1(\(q\), \(p\), \(n\)) switching fabrics with \(2\leq m_{\max}\leq n\)
Recall that once an existing connection departs, connections carried on some links may not remain consecutive, and this implies that the \(m\) available common adjacent FSUs of \(\mathrm{I}_{i}\) and \(\mathrm{I^{\prime}}_{q\cdot(j-1)+i}\) (or \(\mathrm{O}_{j}\) and \(\mathrm{O^{\prime}}_{q\cdot(j-1)+i}\)) may not be found for a connection \((i,j,m)\). However, Routing Algorithm 2 (for Rearrangements) rearranges all inconsecutive connections such that all of them are repacked in adjacent FSUs from FSU 1. This leads to \(p\geq q^{2}\) being sufficient for an RNB SWS1(\(q\), \(p\), \(n\)) switching fabric with \(2\leq m_{\max}\leq n\) under Routing Algorithm 2 (for Arrivals and Rearrangements) even if existing connections depart. Thus, this theorem, which follows from applying Routing Algorithm 2, is true. Theorem 2 shows the proposed RNB SWS1(\(q\), \(p\), \(n\)) switching fabric with \(2\leq m_{\max}\leq n\) requires fewer middle switches than the SNB SWS1(\(q\), \(p\), \(n\)) switching fabric given in [7] as \(\frac{q^{2}-1}{2(q-1)}\leq m_{\max}\leq\frac{nq}{2q-1}\) (Equation (1)). Thus, the upper bound of the proposed RNB SWS1(\(q\), \(p\), \(n\)) switching fabric for \(m\)-slot connections where \(1\leq m\leq m_{\max}\) with \(2\leq m_{\max}\leq n\) reduces by around half the number of middle switches relative to the number in the SNB SWS1(\(q\), \(p\), \(n\)) switching fabric as \(\frac{q^{2}-1}{q-1}\leq m_{\max}\leq\frac{nq}{2q-1}\).
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-MEC Testbeds
#### 3.1.3 5 GINFIRE University of Bristol Testbed  
The testbed of the University of Bristol, developed within the 5 GINFIRE project, was deployed for smart city use cases [37]. The testbed was developed using open source and licensed solutions. In this testbed, the MEP and MEPM are deployed using the CloudBand of Nokia, which implements the MEC functions and the VIM. The SDN architecture is based on the NetOS controller, whereas OSM is deployed to orchestrate the VNFs. Inter-Digital provides media services, including content distribution, which are utilized for the 5G smart tourism initiative. The media services are managed using OpenStack. The testbed involves mini-scale sensor nodes and equipment such as a Raspberry Pi. The necessary data are acquired and processed at the data center.
## 3 Proposed Method
### Preprocessing for Probability Map Distribution Matching
The normalized residual vector \(\widetilde{\mathbf{x}}_{i,norm}\) can be expressed as shown in (3):  
\[\widetilde{\mathbf{x}}_{i,norm}=\frac{\widetilde{\mathbf{x}}_{res}}{1-\widetilde{\mathbf{ x}}_{il}}(\widetilde{\mathbf{x}}_{res}-\widetilde{\mathbf{x}}_{res}\odot\widetilde{\mathbf{x }}_{i,obs}), \tag{3}\]  
where \(\odot\) denotes element-wise multiplication, \(\widetilde{\mathbf{x}}_{i,obs}\in\mathbb{R}^{C}\) represents the one-hot encoded version of \(\mathbf{x}\), changing the representative label to 1 and other labels to 0 for each spatial position \(\mathbf{i}\). By adding \(\widetilde{\mathbf{x}}_{min}\) and \(\widetilde{\mathbf{x}}_{norm}\), from (1) and (3), the preprocessed SSIN output \(\overline{\mathbf{x}}_{i}\) is defined as follows in (4):  
\[\overline{\mathbf{x}}_{i}=\widetilde{\mathbf{x}}_{i,min}+\widetilde{\mathbf{x}}_{i,norm}+ \mathbf{N}_{\mathbf{t}}(m,\sigma), \tag{4}\]  
where \(\overline{\mathbf{x}}_{i}\) is preprocessed data which the discriminator takes. By concatenating \(\widetilde{\mathbf{x}}_{i}\) for all spatial positions \(\mathbf{i}\), \(\overline{\mathbf{x}}\in\mathbb{R}^{H\times W\times C}\) is obtained. If the representative values of \(\widetilde{\mathbf{x}}_{i}\) are smaller than \(\tau\) (4) becomes \(\overline{\mathbf{x}}_{i}=\widetilde{\mathbf{x}}_{i}\). Random noise \(\mathbf{N}_{\mathbf{t}}(m,\sigma)\in\mathbb{R}^{C}\) with a truncated normal distribution is added to blur the other unknown factors that may confuse the discriminator, discerning the static/dynamic domain boundary. The truncated normal distribution resamples that value whose values exceed the range of \((m-2\sigma,m+2\sigma)\). The standard deviation \(\sigma\) is set as \(m/2\) so that the range of \(\overline{\mathbf{x}}\) does not go negative. After preprocessing the SSIN output, the static label map \(\mathbf{y}\) matches the probability distribution of \(\overline{\mathbf{x}}\). At each pixel \(\mathbf{i}\), the data position of \(\overline{\mathbf{x}}_{il}\) and \(\overline{\mathbf{x}}_{ik}\) is swapped where \(\mathbf{l}\) is the representative label at \(\overline{\mathbf{x}}_{i}\) and \(\mathbf{k}\) is the representative label at \(\mathbf{y}_{i}\), and the preprocessed static label map \(\overline{\mathbf{y}}_{i}\) can be expressed as indicated in (5):\[\overline{\mathbf{y}}_{i} =\mathbf{y}_{i}\odot\overline{\mathbf{x}}_{i,\mathbf{max}}+\left(\max(\mathbf{y}_{i},\widetilde{\mathbf{x}}_{i,\mathbf{oh}})-\mathbf{y}_{i}\right)\odot\overline{\mathbf{x}}_{i\mathbf{ k}} \tag{5}\] \[\quad+\left(1-\max(\mathbf{y}_{i},\mathbf{x}_{i,\mathbf{oh}})\right)\odot\mathbf{x }_{i}+\mathbf{N}_{\mathbf{t}}(m,\sigma),\]  
where \(\overline{\mathbf{y}}_{i}\) is preprocessed data of \(\mathbf{y}_{i}\odot\) is the elemental-wise multiplication, and \(\overline{\mathbf{x}}_{i,\mathbf{max}}\) is the largest value among \(\overline{\mathbf{x}}_{i}\). Concatenating \(\overline{\mathbf{y}}_{i}\) for all spatial positions, \(\overline{\mathbf{y}}\in\mathbb{R}^{H\times W\times C}\) is obtained and evaluated by the discriminator.
## 1 Introduction
### Survey of Surveys  
This study builds upon and extends previous surveys on text summarisation by incorporating a comprehensive bibliometric analysis, examining research trends, collaborations, and thematic evolution in the field. Unlike prior works, such as [PERSON] and [PERSON] [14] (2017) and [PERSON] et al. [15] (2021), which primarily focused on summarisation techniques and advancements in deep learning models, this paper integrates quantitative methods using tools like VOSviewer (V_1.6.18) and RawGraphs (V_2.0) to visualise co-authorship networks, citation patterns, and keyword co-occurrences. By analysing 983 publications, including several recent and influential papers, this study provides new insights into emerging areas such as domain-specific summarisation, real-time applications, and ethical considerations, which were not extensively covered in earlier reviews. Additionally, it discusses underexplored topics like multimodal [16] and multilingual [17] summarisation, dialogue summarisation, and explainability as a structured framework for future research. This integrative approach makes this survey unique, bridging the gap between traditional reviews of summarisation techniques and a broader analysis of the research landscape, while incorporating the latest advancements [18; 19] and trends in the field. This survey of surveys in Table 1 aims to provide a comprehensive overview of significant studies that have examined various text summarisation techniques or pertinent to text summarisation methodologies. By analysing existing surveys, we can identify the focus areas, methodologies, and findings presented in each, providing insight into the field's progression and future research directions. To further categorise these reviews, we present a classification system based on various parameters that designate research areas as 'less explored areas' (L),'moderately explored areas' (M), and 'highly explored areas' (H). For understanding where research efforts have been concentrated and where opportunities for further exploration lie, this classification is helpful.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Scope/References** & **[20]** & **[21]** & **[22]** & **[23]** & **[24]** & **[25]** & **This Survey** \\ \hline
**Taxonomy/Overview** & M & M & H & H & H & H \\ \hline
**Publication Connections** & L & L & H & M & M & H & H \\ \hline
**Topics** & M & L & M & H & M & H \\ \hline
**Keyword Analysis** & L & L & L & M & M & H & H \\ \hline
**Techniques** & H & M & M & L & H & H \\ \hline
**Key Innovators and Scholarly Networks** & M & M & L & M & H & H \\ \hline
**Citation Insights** & L & M & L & L & M & M & H \\ \hline
**Future Directions** & M & M & M & H & H & M & H \\ \hline \end{tabular}
\end{table}
Table 1: Summary of relevant literature based on various scores and references.
## Result and Discussion
### Scanning Electron Microscopy  
Direct observations of the size and size distribution of the nanocomposites were made via SEM analysis. As observed in Figure 9A, the pure PS particles were spherical with a very smooth surface and a particle size diameter of approximately 120 to 140 nm. In contrast, the PS-OMMT nanocomposites (Figure 9B-D) showed spherical particles with obviously decreased diameters of approximately 60 to 70 nm. These results were in agreement with the results shown Figures 4 and 8. Furthermore, relatively smooth particle surfaces were obtained, and a certain degree of adhesion  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Code** & **Z-Average (nm)** & **PDI** \\ \hline PS & 129.5 \(\pm\) 6.5 & 0.204 \\ PS-OMMT-1 & 64.58 \(\pm\) 2.91 & 0.082 \\ PS-OMMT-3 & 62.83 \(\pm\) 2.19 & 0.079 \\ PS-OMMT-5 & 63.38 \(\pm\) 2.92 & 0.102 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particle size distribution of PS and PS-OMMT nanocomposites.  
Figure 8: Particle size distribution curves of PS, PS-OMMT-1, PS-OMMT-3 and PS-OMMT-5.  
between these particles was also observed due to the MMT layers located either on the surface or partially embedded inside the PS particles, as shown in Figure 4. This particle morphology indicated that the OMMT played a significant role in the formation of nucleation centers during the process of emulsion polymerization, thereby restricting particle growth. However, as the amount of clay increases (above 5 wt %), the clay layers agglomerate and form intercalated structures, leading to an increase in the particle size and PDI.
# New Space Engineering Design: Characterization of Key Drivers
## 2 Methodology
### Contextualization Within Application Domains  
Behind the selected engineering design drivers lie broader market directions and opportunities that define current system requirements within the New Space domain. In parallel with the proposed categorization, three representative application areas have been selected and analyzed to illustrate the role that the identified drivers and enablers play in space system design, and to provide insights into the main market trends:  
* LEO constellations of small satellites;
* In space operations and servicing;
* Space exploration.  
These application domains, together with their associated design drivers and corresponding design enablers, form a complex network of interdependent, cause-and-effect relationships. Advances in enabling technologies make specific applications possible, while, conversely, technological development and mission requirements are often driven by evolving market demands.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 3 Results
### Results of Target Detection
#### 3.2.2 Comparison of Results from Different Object-Detection Models  
We selected four target-detection models, YOLOv3, YOLOv4, YOLOv5 and YOLOv7, to test the same dataset. As shown in Table 5, the evaluation metrics of each algorithm were obtained and the data were compared and analyzed to select the most suitable segmentation model for this experiment.  
From the detection results of different detection models for the region near the pruning point in the table, YOLOv7 exhibits the highest FPS value of 37.98, indicating a high detection speed. However, it demonstrates a lower F1 score. Conversely, YOLOv5 showcases the highest mAP value of 81.06% and an F1 value of 0.80, suggesting superior detection performance for the present study. Accurate detection of the area near the pruning point is essential for identifying the pruning point of grapevine branches. Therefore, YOLOv5 was selected as the target-detection model for detecting buds. The results of this algorithm under different lighting conditions are illustrated in Figure 10.  
As shown in Figure 11, compared to directly acting on the original image to identify buds using a target-detection method, our theory significantly improves the ability to recognize the buds, providing a better basis for determining the location of the pruning point later.
## 4 Compromise between Mechanical Integrity and Degradation Rate  
As mentioned previously, the purpose of a biodegradable metal implant is to assist in the patient's healing process by providing mechanical support to the injured area and then biodegradation away thereafter. As certain medical issues only require a temporary aid for eventual healing (e.g., cardiovascular, orthopedic, and pediatric care), the requirement for a metal implant is to have a non-permanent presence. In ideal cases, a biodegradable metal implant would have an optimal middle ground between keeping its mechanical integrity for a certain duration and thereafter, progression of its deterioration after a period of time, as represented in Figure 2.  
After implantation, the inflammatory process (i.e., swelling of the bone's fractured vessels) begins within a day to a week after fracture. Then, new connective tissues, blood vessels, cartilage and soft spongy bone begin to develop a soft callus within 2 to 6 weeks. By the 12 th week, osteoblasts would have already caused the soft callus to harden [24]. The hard callus stage ends when the fragments are firmly healed by a new woven bone, typically within 3 to 4 months. The time taken for the fracture healing period is dependent on the fracture site (i.e., bones located in the lower limbs would take a longer time to heal as their sizes are larger). The recovered bone will continue to remodel itself over the next period of months or years as a result of the stresses derived from physical activity. It is expected that the biodegradable metal implant should retain its mechanical integrity until this stage. The woven bone, via the osteonal remodeling process, will be gradually substituted by the lamellar bone, returning the bone to its original morphology. For different applications (e.g., stents), they should have different mechanical integrity and predicted corrosion rate to accommodate the healing process [2; 24]. For example, a coronary stent made from a magnesium alloy is expected to last for a period of 6-12 months during the process of remodeling the blood vessel, and then completely degrade in the next 12-24 months. Meanwhile for orthopedic biomaterials, 3-4 months is needed from the formation of fracture callus to the formation of new bone, which leads to the eventual solidification of the bone thus, restoring a majority of the bone's intrinsic strength [14].
## 5 Experiments
### Experimental Results
**Contribution of Hypergraph Neural Networks:** Diverging from the MI optimization strategy, the \(S^{2}\)HHGR model employs SSL objectives within a hypergraph neural network framework. This approach enables \(S^{2}\)HHGR to intricately model high-order relationships between groups, users and items, thereby yielding notable performance gains over other baselines. **Empirical Validation of DAGA:** Central to our findings is the exceptional performance of DAGA, which surpasses all baseline models across the evaluated metrics and datasets. Notably, DAGA achieves this with a relatively simpler architecture, employing a disentangled multi-head self-attention encoder in conjunction with SSL objectives and an attention-based aggregation mechanism. This configuration not only simplifies the model but also accentuates the effectiveness of disentangled representations in group recommendation tasks. In summary, the empirical insights gleaned from the performance comparison validate the efficacy of attention mechanisms, graph-based representations and, particularly, the novel disentangled representation approach adopted by the self-attention mechanism in DAGA. The findings underscore the potential of disentanglement and SSL in advancing the state of the art in group recommendation.
## 1 Introduction
### Plasma Properties  
An ionized gas is an ensemble of positively and negatively charged particles. The term plasma was applied to these systems by [PERSON] [2] and should not be confused with blood plasma. Over the past twenty years, atmospheric pressure plasmas have been the subject of numerous reviews regarding the state of the art of the devices used to generate these plasmas and characteristics of the plasmas [3; 4; 5; 6]. In the present study, a plasma jet was used to generate a cold (low-temperature) non-equilibrium (non-thermal) atmospheric pressure plasma and will be designated as APP.  
APP is a relatively new approach that is extensively studied for its biomedical applications [7]. These include sterilization, blood coagulation, cancer treatment, cell metabolism modification, hospital hygiene, antifungal treatment, dental care, skin disease and wound healing [8; 9; 10; 11; 12; 13].  
Tissue engineering and regenerative process involves a variety of cellular activities such as cell proliferation, differentiation and apoptosis. Exposure to APP promotes repair of muscle defects through control of cell proliferation and differentiation, suggesting that APP exposure has the potential for use in muscle tissue engineering and regenerative therapies [14]. APP exposure has been shown to induce differentiation and growth of developing mouse limb buds [15], as well as differentiation of bone marrow stem cells [16].