esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Genetic Algorithm
#### 4.1.2 Crossover and Mutation  
Because the underground logistics network has only a few pipelines, the gene having a value of 0 is significantly more abundant than its counterpart in the chromosome of the GA, which makes it difficult to cross and mutate to effectively produce better offspring. Therefore, adjusted crossover and mutation methods are employed to overcome this disadvantage.  
The crossover method is shown in Figure 4. The genes of each offspring are obtained from the two parents. The procedure is as follows: First, two positions are randomly selected for both parents (A and B in Figure 4). Second, the gene segment of parent 1 in front of position A is copied to the posterior side of offspring 1, and the gene segment of parent 2 behind position A is copied to the anterior side of offspring 1. Similarly, the gene segment of parent 1 behind position B is copied to the anterior side of offspring 2, and the gene segment of parent 2 in front of position B is copied to the posterior side of offspring 2. To eliminate the poor crossover of chromosomes, this study adjusts the crossover frequency. Every time the algorithm runs, the chromosomes are randomly crossed 30 times to enhance the crossover effect. The adjusted crossover method improves solution convergence and the final result.  
Figure 4: GA crossover diagram.  
Figure 3: Position of underground network arcs on the chromosome diagram.  
The mutation method is described in Figure 5. It changes the value of a randomly selected gene segment in the chromosomes to ensure the diversity of the population and prevent premature convergence. We adjust the mutation method to change the gene segment rather than a single gene to increase the possibility of mutation. To enhance this effect, it mutates five times every time the algorithm runs.
# Challenges for the Implantation of Symbiotic Nanostructured Medical Devices
## 4 Choices of Membranes for Symbiotic Implanted Medical Devices
Though not directly for enzymatic biofuel cells and only for short-term in vivo usage, combining chitosan with alginate to produce an outer protective membrane was shown to improve the response of a glucose biosensor used for short-term in vivo sensing in a mouse [33]. An important consideration is the time-course for implantation experiments to test the biocompatibility of membranes. In many reports, the duration of implantation of membrane materials has been too short to evaluate the long-term application for designing an implanted biofuel cell. For example, a macromolecule cross-linked bioccomposite scaffold composed of hydroxyapatite, alginate, chitosan, and fucoidan was only assessed for a maximum of eight weeks when implanted in a rat [34]. Another potentially interesting membrane that was formed from alkyl cross-linking of chitosan was only tested for seven weeks implanted in rats [35]. An alternative is to use synthetic materials in designing a biocompatible system. [PERSON]'s team built vascular grafts made in polyvinyl alcohol (PVA) [36]. They showed that a cross-linked PVA combined with gelatin slowed down platelet adhesion phenomena and improved the endothelialization of a vascular prosthesis [37]. Inspired by [PERSON]'s results, we designed hydrogels of PVA formed by a freeze/thaw method [38], with the PVA membranes tuned to the needs of the enzymatic biofuel cell. Indeed, the semi-crystalline structure of PVA allowed for the modification of the porosity and mechanical properties by varying the parameters of the production process. We tuned the PVA membranes to the needs of the enzymatic biofuel cell, which relied upon duplex communication for its function. We analyzed the performance of PVA to provide an optimized coating that provided (i) a solid and easily handled membrane, (ii) a porous membrane optimized for the diffusion of glucose and oxygen to the bioelectrodes, and (iii) a protective membrane against proteins of larger dimensions than the glucose (that was required to permeate the PVA) [38]. The PVA membrane has advantages in creating a biocompatible system and in retaining the permeability to glucose. Our freeze/thaw protocol produced a PVA membrane that had a stable permeability to glucose and that was similar to chemically cross-linked PVA [39]. As previously described above, there is also an advantage in incorporating chitosan in the bioelectrodes for enzymatic biofuel cells, since the open architecture assists in immobilizing the enzymes [25; 27],and its three-dimensional structure encourages the ingrowth of cells. Indeed, the use of multiple cross-linking agents with combined collagen/chitosan gels [40] could be useful in combination with protective PVA membranes for fabricating stable implanted enzymatic biofuel cells.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 3 System Model  
### Vector Hydrophone  
Assume that in the initial state, \(n\) sensor nodes are randomly and non-uniformly distributed in the three-dimensional underwater area to collect the underwater environment information and send data to a sink node on the sea surface, where each node is equipped with a vector hydrophone. The receiving mode is directional reception, and the transmitting mode is omnidirectional transmission. The vector hydrophone is a composite of a conventional non-directional-type acoustic pressure hydrophone and a plasmonic vibration hydrophone [22; 23], which can simultaneously measure the orthogonal components of acoustic pressure and plasmonic vibration velocity. Therefore, compared to the acoustic pressure hydrophone, the vector hydrophone has more output signals that consist of acoustic pressure signal \(p(t)\) and two vibration velocity signals \(v_{x}(t)\), \(v_{y}(t)\), which is expressed as \[\left\{\begin{array}{l}p(t)=x(t)\\ v_{x}(t)=x(t)\cos\theta\cos\psi\\ v_{y}(t)=x(t)\sin\theta\cos\psi\end{array}\right., \tag{1}\]  
where \(\theta\) is the horizontal azimuth angle, and \(\psi\) is the pitch angle of the incident acoustic wave.  
The directional of the vibration speed sensor can be electronically rotated, and we define \(v_{c}(t)\) and \(v_{s}(t)\) as the combined directional after electronic rotation, and  
\[\left\{\begin{array}{l}v_{c}(t)=v_{x}(t)\cos\varphi+v_{y}(t)\sin\varphi=x(t )\cos(\theta-\varphi)\\ v_{s}(t)=v_{x}(t)\sin\varphi+v_{y}(t)\cos\varphi=x(t)\sin(\theta-\varphi) \end{array}\right., \tag{2}\]  
where \(\varphi\) is the guiding direction. A linear combination of \(p(t)\) and \(v_{c}(t)\) is usually used in hydro-acoustic communication to improve the signal-to-noise ratio (SNR) of the received signal. In this paper, we use \(p+2\cdot v_{c}\).
###### Abstract  
To resolve the dilemma in any post-processing strategy, i.e., the difficulty of monitoring the real-time time and frequency signals in a timely manner, real-time GPS time and the frequency transfer have recently become trending topics. Unfortunately, data interruption occurs when conducting real-time time transfer, sometimes from unexpected reasons. In this study, to ensure the stability and precision of real-time time transfer, an adaptive prediction model and a between-epoch constraint receiver clock model are applied as the mathematic models. The purpose of prediction is to solve the ambiguity from re-convergence when the data reappear. Moreover, compared to the conventional method, the between-epoch constraint receiver clock model is employed in this study to consider the correlation of epoch-wise clock parameters to avoid wasting useful information. The simulation data and real data are compared to verify the performance of the new approach. The simulation data for 165 days are designed with random daily interruptions of 10, 30, 60 and 90 min. Real data from 12 days is captured from the incomplete data in routine observation records. Ignoring the simulation data and real data, the investigation of six stations shows that the results with the between-epoch constraint receiver clock model were smoother than those with a white noise model. With an adaptive prediction model and the between-epoch constraint receiver clock model, the simulation results illustrate that the average root mean squares (RMS) values of all the stations are significantly reduced, i.e., by 66.03% from 0.43 to 0.14 ns, by 64.91% from 0.44 to 0.15 ns, by 57.47% from 0.43 to 0.18 ns, and by 51.67% from 0.44 to 0.21 ns for the 10, 30, 60 and 90 min data interruptions, respectively. The stability of all the stations is improved by at least 50%. The improvement increases to 100% for short-term stability. The real results show that the stability of four links is boosted by at least 5%. The model proposed in this paper is more effective in producing short-term stability than long-term stability.  
Keywords:real-time time transfer; precise point positioning (PPP); adaptive clock prediction; receiver clock offset model; data discontinuity +
Footnote †: journal: Applied Soft. 2019, 9, 1405; doi:10.3390/app9071405
## 4 Experimental Setup and DSP Stack
### DSP Stack  
Figure 5b,c displays the transmitter- and receiver-side DSP stacks, respectively. At the transmitter, 40,000 PAM-6/8 symbols were mapped from a pseudo random binary sequence (PRBS) as payload. A 2048-symbol training sequence was then added as a preamble, leading to 4.9% redundancy from the frame structure. After 2-times up-sampling, root raised cosine (RRC) filter with 0.05 roll-off was convoluted with the data sequence in order to restrict the signal spectrum into a rectangular shape. Before sending to the AWG, the signal was resampled to 34 Gbaud to match the sampling rate. No pre-equalization or pre-CD compensation was conducted.  
At the receiver, the stored waveform was resampled from 40 GSa/s to 68 GSa/s, which equaled 2 samples per symbol (SPSs). The first 128 symbols from the training sequence were used as a synchronization sequence, and synchronization was performed by calculating the cross-correction between transmitted and received sequences. The taps of the time-domain T/2-spaced finite impulse response (FIR) filter was updated based on the training sequence by the recursive least square (RLS) algorithm. Here, linear the feed forward equalizer (FFE) and Volterra nonlinear equalizer (VNE), up to the third-order, were employed for comparison. The memory length for first-, second-, and third-order kernels were set as 73, 29, and 5, respectively. Finally, BER was obtained after symbol decision and de-mapping with \(\sim\)\(5\times 10^{5}\) bit samples.
## 2 The Role of Phytochemical in Oxidative Stress
### Case Study: Antioxidant Activity of Flavonoids  
Flavonoids comprise a large group of phenolic compounds. Their basic chemical structure is the flavan nucleus, which consists of 15 carbon atoms arranged in three rings (C\({}_{6}\)C\({}_{3}\)-C\({}_{6}\)), as seen in Figure 1. The different classes of flavonoids differ in their oxidation level and substitution pattern of the C ring, while units within a class differ in their substitution pattern of the A and B rings [30].  
Nowadays, flavonoids have gained huge interest due to their broad pharmacological activity. The interest in flavonoids' health benefits has been raised due to the prominent in vitro antioxidant effects that have been stated. Specifically, the antioxidant activity of flavonoids and their metabolites depends upon the arrangement of the functional groups around the basic structure. Several in vitro investigations have been carried to elucidate the relationship between flavonoid structure and the corresponding antioxidant effects [31; 32; 33; 34; 35; 36]. Therefore, the chemical structures of flavonoids are predictive of their antioxidant potential in terms of their radical scavenging, hydrogen- or electron-donation, and metal-chelating capacities.  
The following mechanisms have been cited in literature to explain the antioxidant activity of flavonoids: (1) The scavenging of free radicals or ROS, (2) metal chelation, (3) inhibition of enzymes associated with free-radicals generation (e.g., oxidases), and (4) the activation of antioxidant enzymes [37; 38; 30].
# Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications
## 5 Conclusions  
In this paper, we propose a relay selection method for D2D multicast communication based on D2D channel conditions and social-trust levels. We derive the optimal social-aware relay selection method on the basis of the minimization of the multicast end-to-end delivery time. We further show that our method for social-aware relay selection exhibits an incremental performance with respect to the method that does not use the social domain information, and vice versa for the method in which the relay selection is only based on friendship informations. Numerical results demonstrate that the proposed mechanism can achieve better performance gain when the difference between the channel quality in the two-way source-relay-destinations is better with respect to the source-destinations channel condition.  
We have also highlighted the impact of friendship in the decision method. A larger number of friends in the neighborhood achieves a better average performance in terms of transmission delay. Our proposed method can be used in a cell where all the multicast participants are distributed within a small area to reduce the amount of network traffic necessary to deliver digital content to all users belonging to the same social community.  
Future developments can be represented by the extension of this method to multi-hop social-aware content distribution, in which each destination is interested in sharing the same digital content with a different community, for example, according to the novel paradigm of fog computing/networking and applications in which clusters of smart devices collaborate toward a common goal.  
Furthermore, we can use real-world human mobility traces such as Intel, Infocom06 or Brightkite [25] for future simulations by dividing the devices in the network into several groups to evaluate the system performance in terms of end-to-end delivery delay in realistic environments.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 2 Preliminaries
### Fast Healthcare Interoperability Resources  
Fast Healthcare Interoperability Resources (FHIR) is a standard for exchanging healthcare information electronically. It aims to facilitate the interoperability of healthcare systems by providing a consistent and flexible framework for representing clinical data.  
The key components of FHIR include:  
* Resources: FHIR defines a set of resources that represent specific healthcare concepts, such as patients, observations, medications, and diagnoses. Each resource has a well-defined structure and can be combined to form complex clinical records. In our experiments, we restricted the resources types that the LLMs might use to convert input text to Condition, Observation, MedicationStatement, Procedure, AllergyIntolerance, Encounter, Immunization, and EpisodeOfCare. We detail why we selected this curated set in Appendix A Section Resource Selection for FHIR Conversion.
* Interoperability: FHIR supports various data exchange formats, including JSON and XML, making it compatible with a wide range of healthcare systems and technologies.
* Extensibility: FHIR is designed to be extensible, allowing for the addition of new resources and the customization of existing ones to meet specific healthcare needs.  
Standardization: FHIR adheres to strict standards and protocols, ensuring consistency and reliability in data exchange across different healthcare systems.  
The adoption of FHIR has the potential to significantly improve healthcare outcomes by enabling seamless data sharing, enhancing clinical decision support, and facilitating research and public health initiatives. However, converting unstructured clinical data into the structured FHIR format is a complex task that requires sophisticated techniques and tools.  
In our implementation, we utilized the HAPI FHIR library, an open-source Java implementation of the FHIR specification. HAPI FHIR defines model classes for every resource type and data type specified by FHIR, ensuring robust data validation and facilitating the serialization of clinical document elements into standard FHIR JSON representations.  
In this study, we explore the use of LLMs to automate the conversion of unstructured clinical data into the FHIR format, with a focus on improving the accuracy and reliability of the generated FHIR bundles.
## Materials and Methods
### High-Performance Liquid Chromatography (HPLC)  
Four concentrations of standard solutions (0.8, 0.6, 0.4, and 0.2 mg/mL) were prepared from a stock solution containing 1 mg/mL of geranilin in the mobile phase to determine the amount of geranilin encapsulated in PEG-b-PLGA NPs. The solution was filtered, and reverse-phase HPLC was conducted to measure the amount of geranilin encapsulated in the NPs, of which protocol can be found in [PERSON] et al. [22]. The HPLC core components included a binary pump, an autosampler injector compartment, and a photodiode array detector. This is the applicable protocol of the physio-chemical technique for the determination of encapsulation content for geranilin when used with the percentage yield formula in McCall and Sirianni [18]. The reverse-phase used a C-18, 250 mm \(\times\) 4.6 mm i.d. 5 mm particle size Thermo Hypersil GOLD column (Thermo Scientific, Hemel Hempstead, UK). The mobile phase consisted of sterile deionized water (Solvent A) and acetonitrile (Solvent B), and the solvent gradient flow rate was set to 1 mL/min. The total volume of the sample injected was 20 \(\mu\)L, and the data showed a peak at 274 nm. For the analysis, 5 mg/mL of the polymeric NPs containing geranilin were prepared at the mobile phase. The standard solution and sample solutions were injected into the chromatograph, and the concentration of geranilin in the polymeric NPs was measured [23]. A standard curve was plotted by running HPLC using six different concentrations of geranilin. The encapsulated content was evaluated by comparing the area under the peak of the geranilin HPLC data to the standard curve.  
In Vitro Drug Release of poly(ethylene glycol)-block-poly(lactic-co-glycolic acid) (PEG-b-PLGA) Nanoparticles (NPs) Encapsulated with Geranilin  
A stock solution of 1 mg/mL of geranilin was prepared in phosphate-buffered saline (PBS) (Invitrogen, Waltham, MA, USA). Six concentrations of working solutions (0.4, 0.2, 0.1, 0.05, 0.025, and 0.0125 mg/mL) were obtained by dilution from the stock solution. Next, 5 mg/mL of NPs were loaded with geranilin and solubilized with PBS buffer in a dialysis bag. The dialysis bag was immersed in a falcon tube with 10 mL of PBS as the external medium. At every 15 min interval, 1 mL of the external medium was withdrawn, which was replaced with 1 mL of fresh PBS. The absorbance of the sample was recorded using a double beam spectrophotometer (LAMDA 25, Perkin Elmer, Waltham, MA, USA) [24].
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
###### Abstract  
Sign language plays a crucial role in bridging communication barriers within the Deaf community. Recognizing sign language sentences remains a significant challenge due to their complex structure, variations in signing styles, and temporal dynamics. This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. The system relies on sensor data from a glove with six-axis inertial sensors and five-finger curvature sensors. The proposed HGE-ACN framework integrates graph-based embeddings to capture dynamic spatial-temporal relationships in motion and curvature data. At the same time, the Adaptive Convolutional Networks extract robust glove-based features to handle variations in signing speed, transitions between gestures, and individual signer styles. The lightweight design enables real-time processing and enhances recognition accuracy, making it suitable for practical use. Extensive experiments demonstrate that HGE-ACN achieves superior accuracy and computational efficiency compared to existing glove-based recognition methods. The system maintains robustness under various conditions, including inconsistent signing speeds and environmental noise. This work has promising applications in real-time assistive tools, educational technologies, and human-computer interaction systems, facilitating more inclusive and accessible communication platforms for the deaf and hard-of-hearing communities. Future work will explore multi-lingual sign language recognition and real-world deployment across diverse environments.  
s +
Footnote †: journal: Applied sciences
## 3 Results  
### Grinding Efficiency and Grinding Ratio  
According to the mass loss of the rail sample and the grinding wheel before and after grinding under different pressure loads, the grinding efficiency and grinding ratio of the  
Figure 3: 2D diagram of passive grinding cutting process.  
grinding wheel to the rail sample were calculated; the results are shown in Figure 4. For the passive grinding, the grinding efficiency significantly improves with the increase of grinding load. This phenomenon is consistent with the traditional grinding principle of active grinding. With the increase of grinding pressure, the local contact pressure load on each abrasive intensifies, and more abrasives are involved in the cutting process. Therefore, the material removal from the rail is enhanced, and the grinding efficiency is improved. The purpose of rail polishing is to remove defects or rail fatigue layer from the rail surface, and the grinding efficiency directly determines the material removal from the rail. From this viewpoint, the higher grinding pressure load is beneficial to increase grinding efficiency.  
However, the grinding pressure load also has a significant impact on the grinding ratio of the grinding wheel. The grinding ratio reflects the durability of the grinding wheel. The grinding ratio decreases markedly with the increase of grinding load, which indicates that the service life of the grinding wheel reduces. The rail grinding does consider not only grinding efficiency but also the life of the grinding tool, hence an appropriate grinding pressure load to balance the grinding efficiency and the service life of grinding wheel should be considered.  
As shown in Figure 4, when the grinding load is lower than 200 N, the grinding ratio decreases rapidly with the increase of load; the decreasing trend of grinding ratio slows down when the pressure load increases to the range of 200 N to 280 N. However, when the grinding load further increases over 280 N, the grinding ratio drops sharply once again. At the grinding load of 320 N, the grinding ratio decreases to approximately half that of 240 N. Accordingly, a balance between grinding efficiency and service life of the grinding wheel can be obtained by appropriate grinding load in the range of 200 N to 240 N.  
It is universally acknowledged that the grinding pressure also affects the morphology, residual stress and temperature of rail surface in the grinding process. As a consequence, to optimize the grinding parameter of the grinding pressure load, the effect of grinding pressure on the roughness, residual stress and grinding temperature of the grinding rail surface should be considered as well.  
Figure 4: Grinding efficiency and grinding ratio of the grinding wheel to the rail sample under different pressure.