esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Apart from biological factors, like soft tissue interposition between bone ends, lack of blood supply, and infection, an adverse biomechanical environment at the fracture site is one of the main factors contributing to non-union and mal-union [17]. Mechanical properties at the fracture site have a large impact on healing, and need to be considered when optimizing surgical procedure [18]. There are 2 main pathways through which bones heal: through primary healing or secondary healing [19]. Primary healing occurs when the bone fragments are anatomically reduced and rigidly fixed with a small fracture gap between them (less than 0.01 mm for contact healing and less than 0.8 mm for gap healing), whereas secondary healing occurs when there is relative motion between the bone fragments forming callus tissue [20]. Primary healing requires the strain across the fracture site to be less than 2% [14]. Fixation is one of the main techniques used to stabilize a fracture and assist healing, through either an internal fixation method or an external fixator. Internal fixation utilizes devices like intermedullary nails and bone plates whereas external fixation uses pins or wires to connect an external frame to the bone fragments. The advantages of using external fixation are mainly simplicity of application, adjustability, increased access to the wound for better care and low cost [21; 22]. The ability to apply a wide range of forces on the bone is also an advantage and can be managed by selecting the frame configuration, while it is also preferred for high energy poly-trauma patients with open wounds who may suffer from a large amount of soft tissue damage [21]. In addition to these benefits, external fixators are also preferred in developing regions with limited resources especially due to their low cost and ability to be fabricated locally, both for definitive and temporal fixation [23]. Unfortunately, most orthopedic surgeons reach higher levels of expertise in methods of internal fixation than external fixation due to the number of fractures treated with internal fixation [24]. Stability of the external fixation is necessary to maintain alignment at the fracture site. Failure to maintain alignment could cause loss of anatomical reduction, potentially leading to mal-union, non-union, or failure [25]. A large number of studies have shown that changes in the mechanical properties of a fracture site would affect the time taken for a fracture to heal, change the types and proportions of tissue formed and control whether or not healing would occur (mal-union and non-union) [26; 27].
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 1 Introduction
### Overview of Transmission Efficiency Improvement Methods  
Traditional energy enhancement methods have optimized energy use by addressing the issues of signal interference, communication degradation, and bandwidth issues. Changing the characteristics of the carrier signal is the primary technique used for transmitting encoded data effectively to a destination with minimal energy requirements. Amplitude, frequency modulation, and phase shift keying are regular techniques that also provide reliable data communication between destinations that are great distances apart with minimal signal adjustments. Error control coding is also one of the traditional methods used to improve redundancy in WSNs in terms of the transmission of data to a destination by correcting errors.  
The convolutional codes and hamming codes are some of the techniques that are widely used for such redundancy improvements. These methods improve the reliability of the data transmitted, even in noisy environments. Frequency- and time-based multiple access algorithms are methods that provide the simultaneous transmission of data from multiple sources to destinations connected to WSNs. Certain signal processing algorithms and equalization techniques have also been implemented in WSN systems to improve transmission efficiency. Equalization methods have been utilized in some applications where the transmission of data is dynamic due to channel conditions. Figure 1 indicates an overview of general transmission efficiency improvement techniques used for WSNs.
## 3 Coordinated Multicast Transmission Design
```
0: An initialization power allocation \(\mathbf{\Lambda}^{(0)}\), the beam domain channel statistics \(\mathbf{\Omega}_{k,u,\ell}\), the preset threshold \(\epsilon\)
0: Beam domain power allocation pattern \(\mathbf{\Lambda}\)
1: Initialize \(\overline{\mathcal{R}}^{(-1)}=0\), and iteration index \(i=0\)
2: Calculate \(\overline{\mathcal{R}}^{(i)}\triangleq\sum_{u=1}^{U}\min_{k\in\mathcal{K}_{u} }\left\{\overline{\mathcal{R}}_{k,u,1}\left(\mathbf{\Lambda}^{(i)}\right)- \mathcal{R}_{k,u,2}\left(\mathbf{\Lambda}^{(i)}\right)\right\}\)
3:while\(\overline{\mathcal{R}}^{(i)}-\overline{\mathcal{R}}^{(i-1)}\geq\epsilon\)do
4: Update \(i\gets i+1\)
5: Calculate \(\mathbf{\Lambda}^{(i)}\) via solving (24)
6: Calculate \(\overline{\mathcal{R}}^{(i)}\)
7:endwhile
8: Return \(\mathbf{\Lambda}=\mathbf{\Lambda}^{(i)}\)
```  
**Algorithm 1** Beam Domain Coordinated Multicast Power Allocation Algorithm
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### Pre-Configured Protection Schemes
#### 2.3.3 The P-Cycle Scheme
In [91], the authors developed an approach that was a combination of GA methods with ILP and used it to create and solve p-cycle network design problems involving 200 or more nodes. In [92], the authors proposed a new p-cycle-based dynamic multicast protection scheme that achieved both fast restoration and high capacity efficiency. In [93], the authors evaluated the performance of the flooding-based distributed cycle pre-configuration utilizing a proposed removal of loop back (RLB) approach with various classical p-cycle enumeration algorithms and allocation strategies. **Dual-failure Protection**: The p-cycle scheme can also be used to achieve dual-failure protection. In [94], the authors developed a new linear programming model for the design of p-cycle networks that have a specified minimum dual-failure restorability level. In [95], the authors proposed a new optimization solution method for the design of dual failure survivable p-cycle based WDM mesh networks that guarantee quantified service availability under different dual failure probability distributions. In [96], the authors investigated a novel p-cycle-based dedicated protection architecture that could be used to enable high levels of dual failure restorability for select services. In [97], the authors proposed methods for achieving high dual-failure restorability in p-cycle networks optimally designed only to withstand all single failures or have minimized amounts of additional capacity for dual-failure considerations.
# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 3 Results  
### Experimental Results and Analysis  
The parameter optimization results of the test set are shown in Figure 10. The abscissa is the parameter \(C\), the ordinate is the parameter \(\sigma\), the red number is the recognition accuracy of the test set, and the contour line indicates that the identification accuracy of the test set is the same under different parameter pairs (\(C\),\(\sigma\)). As can be seen from the figure, from left to right, it represents the process of gradual improvement of SA parameter optimization accuracy. When the identification accuracy is 99%, the test set identification accuracy reaches the maximum. At this time, the SA-SVM model established by parameter pair (\(C\),\(\sigma\)) has the highest identification accuracy.  
The locomotion pattern recognition effect of level ground walking, stairs ascent and stairs descent are shown in Figures 11 and 12. The abscissa represents the sample number and the ordinate 1 represents level ground walking, 2 represents stairs ascent and 3 represents stairs descent, * represents actual category, \(\circ\) represents forecast category. It is obvious from the figure that the locomotion pattern recognition effect is good in the movement process, but there are misjudgments when the locomotion patterns are switched.  
Figure 10: Test set parameter optimization result diagram.  
Figure 9: Locomotion pattern states transition diagram.  
The SA-SVM classifier recognition accuracy rate is about 94%. The SA-SVM classifier with FSM recognition accuracy rate is about 97%.
# Instantaneous Networking Service Availability for Disaster Recovery
## 2 Related Work  
We classify the studies related to our work into three categories: (1) recovery of communication failures, (2) information sharing system that collects and provides supporting information to users in disaster, and (3) statistical study of communication features, system availability, and reliability.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Reinforcement Learning
#### 3.4.1 Clique  
CLIQUE is a role free clustering which use a RL algorithm called Q-Learning [50]. On the proposed algorithm, nodes should know the identity of the cluster to which they belong. Contrary to traditional clustering approaches, CLIQUE does not have a conventional phase to select CHs by nodes within a cluster, but rather uses a Q-Learning strategy. The RL used on CLIQUE allows nodes to learn and independently decide whether or not to act as a CH. CH would be the cluster node which has the best routing cost to all sinks. CLIQUE assigns Q-values to each possible action; the learning process allows the agent (node) to select and execute an action (routing) and receives afterwards the corresponding reward used to update the Q-value. CLIQUE is compared to the Traditional Random Cluster head Assignment (TRC) which uses an a-priori probability at each node to decide whether or not to become a CH. The results obtained after simulations have shown that CLIQUE has a better performance than TRC in terms of energy consumption (FND), data delivery, and communication overhead.
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 2 Conventional Approaches
### Pathway Analysis Methods  
A powerful method to describe peculiar features of the cell metabolism is pathway analysis (PA), which provides a graphical representation of the relationships among the actors (mainly enzymes and metabolites) of precise catalyzed reactions. Therefore, PA is highly employed for the interpretation of high-dimensional molecular data [74]. In fact, taking advantage of the already acquired knowledge of biological pathways, proteins, metabolites and also genes can be mapped onto newly developed pathways with the objective to draw their collective functions and interactions in that specific biological environment [75]. Although PA was initially developed for the interpretation of transcriptomic data, in the last decades, it has become a common method in metabolomics, being particularly suited to find associations between molecules involved in the same biological function for a given phenotype [76; 77; 78].  
PA methods include several tools allowing deep statistical analyses in metabolomics known as enrichment analysis. They grant the functional interpretation of the achieved results mainly in terms of statistically significant pathways [79]. These tools can handle heterogeneous and hierarchical vocabularies and may be classified into two distinct collections. The first encompasses \"non-topology-based\" (non-TB) approaches, which do not consider the acquired knowledge concerning the character of each metabolite in the considered pathways [80]. Non-TB approaches include the over-representation analysis (ORA) as the first generation technique and the functional class scoring (FCS) as the second generation.  
Figure 9: Linear SVM model highlighting the classification of two classes (red and blue). Figure reprinted from Ref. [68] under the terms of the HighWire Press license.  
Finally, the second collection includes topology-based methods (see Figure 10) that are adopted to determine those pathways that are significantly impacted in a given phenotype.  
This latter approach can be classified depending on the considered pathways (e.g., signaling or metabolic), inputs (e.g., subset or all metabolites and metabolites \(p\)-values), chosen mathematical models, outputs (e.g., pathway scores and \(p\)-values) and the wanted implementation (e.g., web-based or standalone) [81; 82]. Note that PA methods were originally developed for genes, but they can be successfully applied for every biomolecule/metabolite [83].
## 2 Results and Discussion  
Effect of SBR Content on the Glass Transition Temperature (\(T_{\rm g}\)) and Morphology of NR/SBR Blends  
Glass transition temperature (\(T_{\rm g}\)) is essential for tire tread rubber, as it is the factor which largely determines the wear resistance, wet skid resistance, rolling resistance, and low temperature driving performance of the tire. For an immiscible binary blend, two glass transition temperatures different from those of each component in the blend can be observed, and their \(T_{\rm g}\) variation is related with the compatibility of the blend [17; 32]. Here, dynamic mechanical analysis (DMA) was employed to study the SBR content dependence of \(T_{\rm g}\) for NR/SBR blends in the dual cantilever mode during gel-to-glass-like transition. Figure 2a shows the loss modulus (\(E^{\prime}\)) as a function of temperature for NR/SBR blends with different weight ratios, respectively, while Figure 2b shows their \(T_{\rm g}\) values as a function of SBR content. Here, two obvious \(T_{\rm g}\) peaks can be observed for the NR/SBR blends with the weight ratios of 50/50 and 25/75, while, for the NR/SBR (75/25) blend, only one remarkable \(T_{\rm g}\) peak with a shoulder peak can be observed. Furthermore, the \(T_{\rm g}\) values for the NR and SBR phases in the NR/SBR blends are close to those for pure NR and SBR, indicating that the weak compatibility between NR and SBR may be attributed to the high styrene content (40%) of SBR in our study. On the other hand, the temperature distance between the two glass-to-gel-like transitions in the NR/SBR (50/50) blend is a little narrower than those in NR/SBR (75/25) and (25/75) blends, indicating that the compatibility of NR with SBR may be affected by their blend morphology.  
The morphology of the NR/SBR blends with different weight ratios was observed by transmission electron microscope (TEM, as shown in Figure 3. The dark and bright regions in the TEM images correspond to the SBR and NR domains, respectively. The morphology of the NR/SBR blends may be apparently associated with their blend composition. The NR/SBR (75/25) blend shows the sea-island structure with small SBR droplets in the NR matrix. Moreover, the SBR domains are nearly spherical, with a diameter of smaller than 1 \(\mu\)m, and the domain boundary is more indistinct, resulting in weak glass transition of the SBR phase observed in their DMA curve. The NR/SBR (50/50) blend exhibits a co-continuous morphology, while the NR/SBR (25/75) blends show the coexistence structure of droplet and percolation for NR domains in the SBR matrix, owing to the higher viscosity of the NR phase. Hence, the domain size of irregular droplets in the NR/SBR (25/75) blend is also larger than that of the spherical droplets in the NR/SBR (75/25) blend.  
Figure 2: (a) Loss modulus \(E^{\prime\prime}\) as a function of temperature for NR/SBR blends. (b) Glass transition temperature \(T_{\rm g}\) as a function of SBR content for NR/SBR blends.
###### Abstract  
_\"Sargassum\"_ is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of _Sargassum_ aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a socio-economic issue for the population and the coastal ecosystem. Satellite ocean color data, such as those provided by the Sentinel-3/OLCI satellite sensor (Copernicus), can be used to detect the occurrences of _Sargassum_ and to estimate their abundance per pixel using the Maximum Chlorophylll _Index_ (noted MCI). Such an index is, however, ineffective if the algae are located beneath the sea surface, which frequently happens, considering the rough Caribbean oceanic waters. The objective of this study is to propose a relevant methodology that enables the detection of underwater _Sargassum_ aggregations. The methodology relies on the inversion of the radiative transfer equation in the water column. The inverted model provides the immersion depth of the _Sargassum_ aggregations (per pixel) and their fractional coverage from the above water reflectances. The overall methodology has been applied to Sentinel-3/OLCI data. The comparison with the MCI method, which is solely devoted to the sea surface retrieval of _Sargassum_ aggregations, shows that the proposed methodology allows retrieving about twice more _Sargassum_ aggregation occurrences than the MCI estimates. A relative increase of 31% of the fractional coverage over the entire study area is observed when using the proposed method in comparison to MCI method. For the satellite scenes considered here, the rate of _Sargassum_ aggregations immersed between 2 m and 5 m depth ranges between 30% and 51% over the total amount (i.e., surface + in-water), which clearly demonstrates the importance of considering the retrieval of in-water aggregations to gain understanding on _Sargassum_ spatial variability in the oceanic and coastal ecosystems.  
Sargassum aggregations; ocean color remote sensing; Sentinel-3/OLCI satellite sensor; radiative transfer modelling; tropical Atlantic Ocean 2022145230[[https://doi.org/10.3390/rs14205230](https://doi.org/10.3390/rs14205230)]([https://doi.org/10.3390/rs14205230](https://doi.org/10.3390/rs14205230))
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 3 Coverage Hole Detection and Healing
### Coverage Hole Healing Based on PiP Algorithm  
After defining the location of the cell that contains a coverage hole using the VD, the next step is to find other sensors inside this cell in order to reapply the coverage algorithm and select the suitable sensors to deliver full coverage in the RoI. To complete this stage, we will use the PiP algorithm.  
The PiP algorithm allows us to check, by way of a program, whether a particular point is inside a polygon or outside of it. A common way to approach the problem is to count how many times a line drawn from the point (in any direction) crosses the edge of the polygon. If the line and the polygon intersect an even number of times (or not at all), the point is outside. If they cross an odd number of times, the point is inside. This is true even for complex shapes that have a lot of coordinates and a very precise edge [28]. This process can be seen in Figure 2.  
Figure 1: Coverage holes detection method.  
Figure 2: The principle of the point in polygon algorithm.  
After having selected the sensors which are inside the cell of the coverage hole, we will apply to these sensors the algorithm of coverage already applied in the previous phase of this project (from function 3 to 8).
## 4 Discussion
Our results show compatible data to commercial assays and other ELISA assays with plant-derived recombinant viral proteins [45; 62; 63]. During exposure to SARS-CoV-2, several factors may influence antibody detection. The timing of the sample is crucial since IgM usually appears within the first week and peaks two to three weeks after the onset of symptoms. IgG is usually found after IgM and peaks at about the same time [64]. However, the antibodies may not follow this usual pattern of seroconversion in SARS-CoV-2 infection, which is affected by several factors, such as the presence of memory B cells from hCoVs and cross-reactive serum antibodies [65]. The gold standard test for early diagnosis of COVID-19 is the RT-PCR molecular test. A positive control for standardizing the RT-PCR test was developed by [PERSON] et al. based on Cowpea mosaic virus VLPs (virus-like particles) expressed in plants. These CPMV-derived VLPs packed artificial RNA containing all SARS-CoV-2 genome regions and can be used as reliable controls [66]. Further, for early diagnosis and disease treatment, specific monoclonal antibodies against SARS-CoV-2 are useful. The plant-produced recombinant mAbs SARS-CoV-2 have been reported as potential candidates for diagnostic reagents or therapeutics [67; 68]. However, our study has some limitations. While the specificity of the HEV/RBD-based iELISA is good, we could not determine if the hepatitis E capsid proteins would be recognized by anti-HEV antibodies if they are present in the serum of patients diagnosed with COVID-19. HEV capsid protein retains its ability to be recognized by HEV/RBD (Figure S4, supplementary), which affects the specific recognition of anti-RBD antibodies in 5.9% to 21.7.5% of the population [69]. Further, we could not assess the cross-reactivity of N protein with anti-coronaviruses that differ from SARS-CoV-2 and the influence of the plant-glycosylation model of N and HEV/RBD proteins. Nevertheless, it has been demonstrated that plant expression systems may produce functionally active SARS-CoV-2 proteins at high levels [13; 27; 45; 55; 70].