esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### MAC Layer Design for WBANs
#### 4.4.3 Reliability  
The reliable data transmission is another key issue addressed in the MAC layer. Since redundancy of devices is not a possibility, given the unique role played by each node in the WBAN, the MAC protocol must provide solutions to increase reliability. This can be achieved by supporting retransmission mechanisms or by increasing diversity through techniques such as cooperative network coding (NC).  
Retransmission mechanisms:A usual approach is to employ ACK packets to acknowledge the correct data reception (e.g., [82, 83, 87]). In case of erroneous reception, the data packet can either be discarded or a retransmission can be requested. In [80], for example, extra slots are reserved for packet retransmissions and their optimal number is calculated as a function of the packet error rate.  
Diversity techniques based on NC:Cooperation between nodes is a key mechanism that can be used to enhance diversity. Exploiting the broadcast wireless nature, nodes in the vicinity of the transmitter (relays) can actively contribute to the communication by forwarding any overheard packets to the destination. Recently, cooperative NC techniques have been studied as a potential way to enhance reliability in WBANs.  
The work presented in [99] shows the potential throughput improvement under error-prone channels through the application of network coding, as a function of the number of redundancy packets, the employed relays and the number of sink nodes. More advanced schemes, such as cooperative diversity coding where both coded and uncoded data packets are transmitted, can yield ever better results [100]. A tree topology has been assumed in [101], where multiple nodes communicate with the coordinator through a small number of relays. The nodes send their uncoded data to the relays, which in turn forward a set of both coded and uncoded packets to the destination. The obtained results show that network coding can offer higher reliability with respect to traditional redundancy schemes where multiple retransmissions of uncoded packets take place. Similar conclusions are drawn in [102], where the coding decisions take into account the reliability requirements of different traffic flows.  
Finally, a novel cloud-assisted MAC protocol, CLNC-MAC, that employs NC and guarantees the successful delivery of all generated data to the destination through a relay network, regardless of the channel conditions, has been proposed in [30]. The key concept behind CRNC-MAC is to employ cloud resources for the relay coordination, to ensure that all crucial information from the source is propagated without losses through the network (that may include multiple hops), thus enabling the successful decoding at the destination. This is achieved by employing a central coordinating entity, namely the cloud controller, which has the task of verifying the reception of all required information by the relays at a hop by hop basis, requesting retransmissions whenever necessary.
## 5 Implementation and Evaluation
### Evaluation Metrics  
For this study, evaluation metrics have been used to measure the performance of our models, including the confusion matrix, accuracy, precision, recall, and F1 scores.  
Accuracy: Accuracy measures the overall correctness of the model by calculating the ratio of correctly predicted instances (both true positives and true negatives) to the total number of predictions.  
\[Accuracy=\frac{TF+TN}{TP+TP+FP+FN} \tag{1}\]  
where _TP_ (_true positive_): the model correctly predicted the positive class; _TN_ (_true negative_): the model correctly predicted the negative class; _FP_ (_false positive_): the model incorrectly predicted the positive class; and _FN_ (_false negative_): the model incorrectly predicted the negative class.  
Precision: Precision measures the accuracy of the positive predictions. It indicates how many of the predicted positive results were correct. High precision means that when the model predicts a positive class, it is usually correct.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
Recall: Recall measures the ability of the model to identify all relevant instances (positives). It indicates how many actual positive cases were correctly predicted. High recall means the model captures most of the actual positives.  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
F1 Score: The F1 score is the harmonic mean of the precision and recall. It provides a single metric that balances both precision and recall, especially useful when the class distribution is imbalanced. A high F1 score indicates a good balance between precision and recall.  
\[F1\_Score=2*\frac{Precision*Recall}{Precision+Recall} \tag{4}\]  
Figure 9: Activity distribution of the experiment in the 20 MHz bandwidth.
## 1 Introduction
It introduces lateral displacement and rapid changes in direction, which place greater demands on the knee's ability to stabilize, mimicking the type of biomechanical challenges encountered during sports-specific activities, thus providing more ecologically valid insights into functional joint stability post-reconstruction [22; 23]. However, it is still a subjective measurement, and its combination with motion capture systems can be helpful to provide more objective data that could integrate rehabilitation and return-to-sports protocols, potentially improving clinical outcomes. Previous studies [24; 25] have shown that the association of force plates and motion capture methods can identify risk patterns for ACL injury, such as asymmetry in the number of hops, peak knee flexion, vertical impact force, flight and contact time, and jump distance between legs. Even though marker-based methods are the gold standard for this type of analysis, it introduces a subjective variable, being highly dependent on the correct position of the sensor, which is influenced by the researcher's experience and the morphology of the individual's landmarks. In addition, the signal can be affected by skin and movement artifacts or not recognized by the system [26]. Recent markerless technologies eliminate these disadvantages by creating a 3D representation of the skeleton through images captured by various cameras, providing information on joint angles of the entire body in a more objective manner, being less time-consuming and easier to use [26]. Although markerless systems have demonstrated strong correlations with marker-based systems for sagittal plane kinematic analysis, their correlations in the frontal and transverse planes have been comparatively weaker. Therefore, the interpretation of data from planes of motion involving smaller angular displacements should be exercised with caution [27; 28]. While not being exempt from faults, it is a promising technology that has already been validated many times, including against marker-based systems [29; 30; 31]. This study aimed to examine potential differences in knee kinematics between ACLR using HT or PT autografts during the side-hop test. Additionally, it sought to determine whether the choice of autograft influences knee functionality beyond 12 months postoperatively. It was hypothesized that (i) operated limbs would show longer periods of contact with the ground between hops and (ii) HT autografts would show greater valgus knee alignment during the contact of the limb with the ground, (iii) as well as greater flexion deficits. The novelty of this study lies in its contribution of new data comparing HT and PT autografts at 12 months postoperatively within a single clinical setting, utilizing a functional performance test. Additionally, this study introduces the application of markerless motion capture technology to the investigation of knee kinematics following ACLR. The implementation of standardized and automated data analysis protocols further enhances the reproducibility and comparability of the results, supporting their potential utility as reference values in future research.
# Adjustable Structure for Feedback Active Headest System Using the Virtual Microphone Method
## 3 ANC Algorithms for Active Headrest
### Remote Microphone Method  
The RMM ensures that the disturbance signals at the virtual error microphones can be evaluated by the primary noise signals at the physical monitoring microphones via the pre-modeled observation filters. A block diagram of the RMM in the tuning and control stages is shown in Figure 4.  
Figure 3: Block diagram of the auxiliary filter method (AFM). (**a**) Tuning stage. (**b**) Control stage.  
In the tuning stage shown in Figure 4a, temporary microphones were placed at the target positions. The disturbance signals measured by the physical monitoring microphones and the target microphones were used to estimate the observation filters and can be expressed as  
\[\hat{\mathbf{O}}_{mv}=\mathbf{R}_{d_{m}d_{v}}(\mathbf{R}_{d_{m}d_{m}}+\beta_{ \mathrm{O}}\mathbf{I})^{-1}.\left(m=1,2,...,M;\,v=1,2,...,V\right) \tag{11}\]  
where \(\mathbf{R}_{d_{m}d_{v}}\) is the cross-power spectral density matrix of \(\mathbf{d}_{m}(n)\) and \(\mathbf{d}_{v}(n)\) and where \(\mathbf{R}_{d_{m}d_{m}}\) is the auto-power spectral density matrix of \(\mathbf{d}_{m}(n)\). \(\mathbf{I}\) is the identity matrix, and \(\beta_{\mathrm{O}}\) serves as the regularization factor. \(\mathbf{d}_{v}(n)=[d_{v}(n),d_{v}(n-1),...,d_{v}(n-L_{\mathrm{O}}+1)]\) and \(\mathbf{d}_{m}(n)=[d_{m}(n),d_{m}(n-1),...,d_{m}(n-L_{\mathrm{O}}+1)]\) represent the vectors of the disturbance signals at the \(m\)th physical and \(v\)th virtual error microphones, respectively. \(L_{\mathrm{O}}\) is the tap length of the observation filters. In the control stage shown in Figure 4b, the disturbance at the \(m\)th physical monitoring microphone can be calculated as  
\[\hat{d}_{m}(n)=e_{m}(n)-\mathbf{\hat{s}}_{m}(n)\mathbf{y}(n), \tag{12}\]  
where \(e_{m}(n)\) denotes the error signal obtained from the \(m\)th physical microphone. The disturbance signal at the \(v\)th virtual microphone is then evaluated with the pre-trained observation filters and is expressed as  
\[\hat{d}_{v}(n)=\sum_{m=1}^{M}\hat{\mathbf{O}}_{mv}\mathbf{\hat{d}}_{m}(n). \tag{13}\]  
Therefore, the estimated virtual error signal used to update the control filter in Equation (6) can be denoted as  
\[\hat{e}_{v}(n)=\hat{d}_{v}(n)+\mathbf{\hat{s}}_{v}(n)\mathbf{y}(n). \tag{14}\]
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 2 Literature Review  
### Development of 5G  
IoT is a new communications paradigm that will expand current Internet technologies and enable machine to machine (M2M) communication. The next-generation 5G radio access technology will be a key component of the networked society. 5G will support considerable numbers of connected devices and meet the real-time and high-reliability communication needs of mission-critical applications [9]. 5G will provide wireless connectivity for a range of new applications, such as cloud-based image computing, the Internet of Vehicles, smart manufacturing, smart energy, wireless healthcare, unlimited home entertainment, the Internet of Drones, social livestreaming networks, personal artificial intelligence assistance, and smart cities.  
5G-related investments are constantly growing. According to a recent report by TechNavio, the global market size of 5G equipment will grow by US$16.28 billion from 2019 to 2023, with a compound annual growth rate of approximately 71% [20]. Currently, numerous countries are planning to use the expired 3G spectrum for the development of 5G. For example, China has planned for its 5G system to use the 4800-5000 MHz and 300-3600 MHz bands, in addition to millimeter waves, to comply with the International Telecommunication Union's IMT-2020 standards. The United Kingdom announced the 5G Testbeds and Trials Programme in 2018, which involved launching large-scale 5G trials in the United Kingdom [21]. Private companies (e.g., Gigaclear) have begun to allow large- and medium-sized enterprises to use paid trials for high-speed 5G networks. Verizon Wireless, a wireless communication service provider in the United States, has collaborated with Samsung (South Korea) to deploy and test 5G networks in five U.S. cities. These cases suggest that 5G technology is receiving increasing attention from governments and technology companies.  
The increased global attention toward 5G technology is primarily because of its higher data transmission rate and faster response time. The long-distance transmission of a large amount of data to modems in buildings is currently achieved using optical signals transmitted through fiber optics. These signals are then converted into electric signals. In the future, the optical signals will directly enter terminal machines and even memory and processing chips. This will require the close integration of photonic components and complementary metal-oxide-semiconductors. Therefore, photonic technology in 5G has recently been given increased attention [4; 16; 17; 18]. The importance of photonic technology in 5G is explained further in the following section.
## 1 Introduction
Additionally, we also investigate the limits of applicability of the GO model for the scope of simulation of unconventional GNSS-R systems, which are conceived to work in far-from-specular acquisition geometries, where classical GO is expected to fail. GNSS-R processing chain and receive antenna beamwidth are accounted for as well for the evaluation of the sea-reflected signal SNR. Similar analyses were presented in [27; 28]. Here we extend such analyses considering a wider set of acquisition geometries, and, additionally, provide a more general expression for the received signal strength which allows for more accurate results as discussed in detail in the following sections. The main outcomes of our study could support a fast and reliable simulation of sea-reflected GNSS signals in arbitrary imaging geometries, including the backscattering direction. It is worth mentioning that all previous works focusing on unconventional GNSS-R systems do not analyze the effects of the signal processing chain, which, however, is recognized as a key factor influencing ship target detectability as well. This research line is left to future investigations. The remainder of this paper is organized as follows: Section 2 briefly describes the BA-PTSM which is used in this work for modeling sea surface scattering in both near- and far-from-specular observation directions; Section 3 presents the power budget analysis for the sea-reflected GNSS signal; Section 4 describes and discusses numerical results of the SNR analysis for spaceborne and airborne GNSS-R. Finally, main conclusions are drawn in Section 5. Figure 1: Conventional (LHCP-forward-scattering, red box) vs. unconventional (RHCP-backscattering, green box) GNSS-R.
## 2 Related Work
### Grid-Based Clustering Method  
Grid-based clustering method includes power-efficient gathering algorithm (PEGASIS) [33] and GROUP [34] protocols. Unlike the multi-cluster structure of the LEACH protocol, PEGASIS uses signal strength to measure the distance between nodes. It assumes that every node can communicate with the sink node. It is twice as fast as the LEACH protocol to improve the lifetime of the network. The cluster network established by the GROUP protocol is dynamically random. The grid seed is selected first, then the grid seeds along the radius identify the CH nodes in the grid. Its selection operation is based on the remaining energy in the node.
## 3 Results and Discussion  
The beverages were produced based on integral exploitation of several fruits and vegetables: lentil sprouts, carrot, pumpkin, raspberry, strawberry, and apple. The whole raw materials were lyophilized and processed into powders. They were combined in eight variants, and their functional properties were evaluated. The composition and antioxidant properties of functional powders dedicated for production of beverages are shown in Table 1. It was found that functional additives accounting for 10% of dry mass of powdered beverage significantly diversified low-molecular-weight antioxidants and antioxidant properties. The total phenolic content in the basal (control) composition was estimated at 3.6 mg GAE/g. The functional additives significantly increased the amount of phenolics in a range of 4.9-6.9 mg GAE/g (an increase by 36-92%, respectively). The highest phenolic content was recorded for MO2 enriched with the raspberry powder (6.9 mg GAE/g). The content of carotenoids was similar in all samples, and there were no significant differences between the control and enriched powders. The highest increase in the vitamin C content was determined in the samples enriched with the strawberry powder (MO3, 2.0 mg/g; an increase by 186% compared to the control). A slight increase compared to the initial composition was also recorded in the MO4 and MO6 samples enriched with apples, as well as raspberry and apple powder. The increase in phenolics and vitamin C observed after partial replacement of sprouted lentil flour with lyophilized fruit was reflected in elevation of antioxidant capacity. The highest antiradical activity was recorded for the MO4 sample (6.2 mg TE/g). Surprisingly, compared to the control sample, the MO2, MO5, and MO8 samples had a lower ability to quench free radicals by 8%, 28%, and 36%, respectively. The reducing power ranged from 2.2 to 4.2 mg TE/g for the basal powders (MO1) and raspberry-enriched powders (MO2), respectively. With respect to the control, the increases in reducing abilities were between 23% and 91%. The content of bioactive compounds in each mixture is a sum of individual components, which indirectly depends on their variety, harvest season, method of extraction, or solvent used. [PERSON] and [PERSON] (2016) showed that the quality of cassava flour biscuit was improved by supplementation with apple powder. Similarly, addition of mango peel powder into biscuits resulted in higher polyphenol and carotenoid contents and improved antioxidant activity ([PERSON] et al., 2017). It was previously indicated that the activity of phenolics depends on conditions such as pH and interactions of phenolics with other dietary constituents released during digestion, e.g., iron, other minerals, dietary fiber, or proteins. All these factors are known to influence polyphenol solubility and availability ([PERSON] et al., 2017). The decrease in the antioxidant ability (MO2, MO5, MO8) observed in the present study may be a result of antagonistic interactions between the ingredients of various food matrices. This was previously reported by
## 1 Introduction
Artificial compressibility is applied to recover a set of equations of the hyperbolic type and derive the exact Riemann problem solution by means of standard wave patterns [25]. We stress that the artificial compressibility acts only at the local Riemann problem level, while the set of governing equations still lacks the pressure time derivative. The coupling between pressure and velocity introduced by the Riemann solver allows for equal degree pressure-velocity formulations. Moreover, thanks to this coupling, the resulting space-discretized set of equations becomes a set of differential-algebraic equations (DAEs) of index 1, thus implicit high-order time integration schemes can be applied [26]. The assessment of the DG-VDI formulation conducted in this work focuses on the positivity of the discrete density field, the control of the Gibbs-type phenomena emerging from high-order representations of interfaces, and on the effectiveness of the time integration strategy. Density positivity is crucial when dealing with multi-component flow problems featuring high-density ratios, e.g., water-air. The approach proposed in Section 3.3 consists of defining the density variable \(\rho\in\mathbb{R}^{+}\) as the sole function of a discretized variable denoted as working density \(\widetilde{\rho}\in\mathbb{R}\) and enforcing density positivity by introducing a suitable variable transformation \(\rho=f(\widetilde{\rho})\). Since the transformation introduces additional nonlinearities, accurate space and time integration strategies are required to guarantee mass conservation. Gibbs-type phenomena emerge from high-order representations of sharp diffuse interfaces between components. Taking inspiration from shock-capturing techniques for compressible flows, we introduce an artificial mass diffusion source term within the set of equations which is activated by spurious oscillations, see Section 3.4. Despite being treated as a source term, this artificial diffusion is conservative since it leaves untouched the mean value of the conserved variable within each mesh element. In Section 3.5, the set of DAEs is integrated in time with high-order single-step multi-stage ESDIRK schemes [27; 28]. These nonlinear temporal schemes can be designed to have optimal stability properties, thus preventing Courant-related time step restrictions. Section 4 presents the assessment in terms of space, time, and mass conservation accuracies of sets of governing equations on three test cases of known analytical solutions, and the simulation of the impingement of a 2D liquid (water) droplet on a thin film immersed in air. In Section 5, conclusions are given.
# The Modification of Titanium Surface by Decomposition of Tannic Acid Coating
## 1 Introduction
Polyphenolic acids are naturally occurring organic compounds composed of numerous phenolic rings in their structures, with prevalent carboxylic and hydroxyl groups. They are characterized by antibacterial and antioxidant properties. Moreover, they are biocompatible. Therefore, the modification of the titanium surface through the deposition of this type of active substance may lead to the enrichment of its features with antibacterial, antiviral and antioxidant properties, as well as the improvement of the biocompatibility of titanium plates [13]. Tannic acid is a type of natural tannin--a water-soluble polyphenol compound, which can be extracted from grapes, tea extract, red wine, etc. The presence of pyrogallol and catechol groups imparts antibacterial, antiviral and antioxidant properties to the tannic acid, enabling it to be an efficient surface modifier for titanium-based metallic biomaterials. The presence of tannic acid on the titanium surface can improve the osseointegration properties by reducing the reactive oxygen species generation and renders it capable of adhering to various metallic surfaces via metal ligand co-ordination. Owing to its improved molecular weight, as compared to gallic acid, tannic acid can effectively act as a bio-polymer cross-linker as well as an active additive metal for coating applications. The aim of the study is to deposit tannic acid on the titanium surface using the vacuum deposition method. The method has not yet been used in the context of tannic acid-based coating. The morphology of obtained coatings was studied by means of a scanning electron microscope. The surface free energy of the developed surfaces was assessed by means of static contact angle studies, and total tannic acid release was also determined. Moreover, the blood compatibility of obtained materials was studied to consider materials that are safe for medical application.