esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 3 Proposed Clock Recovery Circuit and Method  
We are proposing a new methodology to optimize the channel spacing based on the total jitter seen by the receiver in gridless networks. As discussed in Section 2.2, when the timing recovery circuit is controlling the VCO directly, as in Figure 2, adjusting the loop bandwidth is limited by high-frequency noise injected by the VCO. Hence, the key limitation of existing clock recovery circuity is the lack of flexibility. In other words, the tracking bandwidth cannot be optimized when \"Data Clk\", shown in Figure 6, is corrupted due to leaked power from neighboring channels. We propose a clock recovery architecture that overcomes this limitation. As well, we present a network level method, embedded in SDN ecosystem that allows for better spectrum efficiency compared to legacy methods.
###### Abstract
For example, plasma spraying employs line-of-sight processing and high temperatures, which restrict the use of complex shapes and can lead to thermal degradation of PEEK [2; 30]. Another chemical modification that can be applied to PEEK material is sulfonation etching, which utilizes only sulfuric acid. This process constructs an integrated 3D porous network on the surface, is compatible with complex shapes, and provides a stable surface that can be further functionalized [31; 32; 33; 34; 35]. The sulfonation mechanism is an electrophilic substitution reaction that occurs on the phenyl ring between the two ether bridges in the chemical structure of PEEK, which leaves behind residual sulfur compound groups (SO\({}_{3}\)H) that can be removed by subsequent hydrothermal treatment [36; 37; 38; 39]. Figure 1 shows PEEK's sulfonation and sulfur group removal reaction mechanism. The resulting porous structure fabricated during sulfonation can be controlled by varying certain variables during the sulfonation process, such as time and temperature [38]. Porosity is significant for osseointegration because it improves cellular attachment and differentiation of osteoblasts [19; 40; 41; 42; 43; 44]. Pore sizes ranging from nano- to macro-scale all play a crucial role in facilitating cellular responses and influencing mechanical properties. Micropores ranging from 100 to 400 \(\upmu\)m allow osteoblast cells to migrate and proliferate while providing channels for nutrients and oxygen to flow through the cells [40; 42; 45; 46]. Nanopores smaller than 100 \(\upmu\)m in size facilitate increased initial protein adsorption, which is crucial for the cell-protein interactions that determine the fate of cells [40; 42; 46]. Micropores and macro-pores can improve the mechanical interlocking of surfaces by providing more material-to-bone contact [47; 48; 46]. An earlier study in our laboratory investigated cell proliferation, differentiation, and mineralization on optimized sulfonated and hydrothermallytreated PEEK surfaces [49]. This current research study further examines the optimized PEEK surfaces by evaluating their mechanical properties, bacterial resistance, and the live/dead cellular response of pre-osteoblastic MC3T3-E1 cells. Mechanical testing was performed to assess modification strength and mechanical interlocking ability through expulsion testing since expulsion is a known complication with spinal implants [50; 51; 52; 53; 54]. Cell viability on the optimized surfaces was observed following cell culture experimentation. A contact angle analysis was completed to determine the hydrophilic or hydrophobic nature of the surfaces after sulfonation and subsequent hydrothermal treatment. A bacterial study was conducted to check if the surface modification adversely affects bacterial growth and attachment. _Staphylococcus aureus (S. aureus)_ was selected as the focus bacterium of this study because it is present in approximately 67% of all orthopedic implant infections and is known to form biofilms [51; 55; 26; 50].
## 2 Preliminary  
### Federated Learning  
Federated Learning has emerged as a promising approach in machine learning, enabling decentralized training while addressing privacy and data ownership concerns. The Federated Averaging (FedAvg) algorithm proposed by [PERSON] et al. [1] is widely used for global aggregation in FL.  
FL leverages the power of local devices such as smartphones and tablets to perform model training while a central server aggregates the locally computed updates [4]. This distribution of the learning process brings several advantages [5; 6]. Firstly, it mitigates privacy risks by avoiding transferring sensitive data to a central location. Secondly, FL allows the utilization of device-specific data that would otherwise be challenging to access due to privacy or logistical constraints [7; 8; 9].  
However, FL also presents its inherent challenges [6; 10]. One such challenge is data heterogeneity, where the data distribution across different devices may vary significantly. This heterogeneity can affect the convergence and performance of FL models.  
Another challenge is the heterogeneity of computing resources among client devices. Some clients may have limited computational capabilities or unreliable network connections, which can lead to stragglers, slowing down the overall FL process.  
Moreover, communication efficiency is another crucial factor in FL. Since clients must frequently communicate with the central server to obtain the latest model updates, efficient communication protocols and strategies are necessary to reduce communication overhead.  
In the upcoming section, we will delve into various research works that aim to tackle these challenges, including data and computing resource heterogeneity and communication efficiency.
## 5 Discussion  
Most of the faults identified by variance and/or CNN can be interpreted using conventional and manual picking but these are more time-consuming and demand high interpretation skills and structural geology domain experience. The CNN-based approach has significant advantages over traditional fault detection techniques. With the traditional approach, we need to apply fault enhancement, e.g., variance, to optimize fault detection. Even though the CNN model can efficiently pick almost all lineaments, it is still dependent on the broad coverage of the training dataset. A pre-trained CNN model of Paradise software was trained on synthetic data. Out of the three architectures of this model known as aggressive, conservative, and mixed-angle engine, only the mixed-angle engine results were found to be geologically accurate. In terms of comparison of manually interpreted faults with ML, Figure 5A shows a vertical slice with many major historic normal faults that were manually interpreted (Figure 5C) on seismic inline 2940 to a lesser extent than the CNN results (Figure 5D) on the same inline. The CNN fault results provide cleaner fault anomalies by capturing all subtle faults that are unable to be mapped easily by a human interpreter. Moreover, from the critical analysis of the three cases demonstrated in Figures 8-12, the pre-trained CNN model shows slightly more continuous faults (Figure 7B) than the variance cube shown in Figure 7A. These findings highlight the effectiveness of the pre-trained CNN-based fault prediction in strengthening fault identification in seismic data. In addition, this pre-trained CNN model does not require any manual interpretation of faults and needs approximately three hours (which might vary depending on data size) to provide the fault prediction findings of the seismic volume. This approach was demonstrated in real-world applications of CNN using transfer learning in seismic fault detection. Exploring the feasibility of employing transfer learning, especially fine tuning a pre-trained CNN model with the actual seismic data of the Poseidon field, offers a promising avenue for further improving the fault prediction accuracy in this complex geological setting.
# Sustainable Additives for the Production of Hydrogen via Sodium Borohydride Hydrolysis
## 2 Materials and Methods
### Additives  
The influence of four additives on the kinetics and stability of hydrogen release was studied. Sodium carboxymethyl cellulose (CMC, Sigma Aldrich (St. Louis, MO, USA), average Mw \(\approx\) 700,000), polyacrylamide (PAM, Polysciences. Inc. (Warrington, PA, USA), Mw = 18,000,000), sodium dodecyl sulfate (SDS, Sigma Aldrich, \(\geq\)98.5%), and \(\beta\)-cyclodextrinn (BCD, Sigma Aldrich, \(\geq\)97.0%) were respectively added to the hydrolysis tests at a concentration of 1 wt%.  
PAM is often used as a viscosity modifier and friction reducer, and thus it could help to control the rate of hydrogen release [35]. For its part, CMC is a derivative of cellulose, non-toxic and generally non-allergenic, which dissolves rapidly in cold water. Its viscosity drops during heating, which could promote the hydrogen release rate [29]. In the case of SDS, this additive has been widely used to improve the storage capacity and induction time of multiple compounds [36; 37]. Finally, BCD is a non-toxic, green, and cyclic oligosaccharide that was selected due to its potential to provide a strong interaction between the metal in the catalyst and the solution, which can be beneficial to improve the reaction performance [38].  
Hydrolysis tests in the absence of additives (control test) were also performed for comparison.
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 2 Related Work
Reference [22] denoted a capacity-aware edge caching strategy by considering the limited fog cache capacity and the connectivity capability of the base station, which employs an alternating direction multiplier method (ADMM)-based algorithm to efficiently search for the best cache content placement at each fog node. Although the above caching strategies enhance the cache hit rate and degrade the transfer latency to a certain extent, most cache placement strategies give less consideration to the characteristics of cache objects and servers in a comprehensive analysis. To optimize the cache placement problem and address the issue of underutilized cache space and the frequent replacement of cache data, a comprehensive-utility-based cache placement optimization strategy is proposed in this paper, which analyzes the characteristics of cache data objects and servers, and this strategy is integrated into the CPOSCU algorithm. The Mobile edge caching strategies are summarized and categorized in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Optimization Goals** & **Reference** & **Applicable Objects** & **Key Research Points** \\ \hline Aiming for low latency and high energy efficiency & [10; 11; 12; 13] & Both latency and energy consumption directly impact Qos applications & A caching strategy based on an improved greedy algorithm; A prevalence-based cache placement strategy; A time-aware collaborative cache placement strategy based on a genetic algorithm; A mobile-aware caching strategy based on content placement. \\ \hline Aiming for low latency and high hit rate & [14; 15; 16; 17] & Both latency and hit rate directly affect Qos applications & An intelligent caching strategy based on the location of user requests; A task caching strategy based on multiple users and multiple servers; A content placement strategy based on a speedup algorithm; An intelligent caching strategy based on location prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary and classification of mobile edge caching strategies.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 4 Methodology
### Data Analysis  
In analyzing the data in this study, the PLS-SEM approach was used. Smart PLS version 3.2.8 is used as a tool. The choice of PLS-SEM as an approach in this study is because it is known to be able to overcome problems associated with small samples and sample data that are not normally distributed [113]. In the PLS-SEM, we have conducted the analysis data through two steps: (i) measurement model testing; and (ii) structural model testing based on [114]. Several tests were carried out before testing the structural model, namely, testing the validity and reliability of all constructs in the proposed measurement model. The provisions for evaluating the validity test results are the convergent validity of the outer loading parameter is greater than 0.70 and the Average Variance Extracted (AVE) value has a value greater than 0.50. Whereas, the results of reliability testing (internal consistency) are based on the Cronbach Alpha coefficient and composite reliability parameters equal to or greater than 0.70 [114].
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Sentimental Features  
Upon extraction of the textual entity feature from tourism review texts, an analysis was subsequently performed on the proportion of different sentiment polarity types of opinion phrases present in the review texts for all tourist attractions under investigation, as depicted in Figure 8. Globally, positive sentiments account for over 70% of the \"Animals and Plants\", \"Family and Education\", and \"Price and Consumption\" categories, indicating high satisfaction with natural resources, good family experiences, and reasonable pricing in Wuhan. However, positive sentiments account for only 23% of the \"Crowd Flow and Density\" category, suggesting frequent dissatisfaction due to overcrowding or long waiting times. Positive sentiments in the \"Services and Facilities\" category are also low at 50%, indicating inconsistent service quality requiring improvement.  
Figure 6: The proportion of theme categories of all attractions.  
To assess variations in tourists' emotional inclinations across different thematic dimensions of attractions, we employ the Emotional Superiority Index (ESI), which quantifies the deviation between an individual attraction's sentiment weight vector (SWV) and the  
Figure 8: The sentiment distribution at theme categories of all attractions.  
Figure 7: Thematic distribution vector of the top nine attractions.  
average SWV of all attractions. Following the extraction of thematic characteristics for scenic areas using the methodology detailed in Section 3.1.2, we analyzed the ESI for the top nine attractions listed on the popular Wuhan scenic spots ranking. The analysis results are presented in Figure 9. For each subplot, the sector azimuth encodes the ten thematic dimensions of the attraction, representing the multidimensional experiential qualities perceived by visitors. The radial length signifies the intensity of emotional superiority, where greater absolute values denote more pronounced emotional leanings--positive values reflect favorable sentiments, whereas negative values indicate unfavorable biases. Notably, all examined popular attractions demonstrate positive thematic emotional tendencies, with Wuhan University exhibiting the highest ESI, particularly in \"Transportation and Travel\" and \"History and Culture\", aligning with its convenient metro access and rich historical heritage. Other attractions show varying sentiment advantages in different themes. For instance, Maya Beach Water Park and Qingchuan Pavilion excel in \"Transportation and Travel\" and \"Services and Facilities\". East Lake Ocean Paradise stands out in \"Price and Consumption\" and \"Leisure and Entertainment\". Mulan Prairie only exceeds the average in \"Environment and Landscape\", indicating a need for improvements in other aspects. Tourists interested in \"History and Culture\" or \"Services and Facilities\" may prefer excluding Mulan Prairie from their travel plans to avoid suboptimal experiences.
## 4 Discussion
The Ruthana seeds had the highest antioxidant activity values (81.88% for the DPPH and 60.29 mg AAE/g for the TRP), which contain more polyphenols and flavonoids whereas the Qatarah seeds, had the lowest phenolic compounds and antioxidant activity (42.07% for the DPPH and 33.27 mg AAE/g for the TRP). Several studies revealed an association between the antioxidant activity of the samples and their phenolic content: the higher the levels of the total phenolic content and flavonoids, the greater the antioxidant activity [47,50,56]. Our results revealed that the antioxidant capacity of the date seeds was strongly and significantly correlated with the phenolic content (r = 0.718 for the DPPH and r = 0.788 for the TRP) and flavonoid contents (r = 0.748 for the DPPH and r = 0.743 for the TRP). Moreover, similar to phenolic compounds, the antioxidant activity of extracts is found to be affected by the solvent system used. The mixture extracts showed the highest antioxidant activity as assayed by the DPPH or TRP, while water extracts showed the lowest values. Our data agree with those reported by [50], who found that antioxidants are more dissolved in polar solvents and their aqueous mixtures due to the wide range of polyphenic compounds that can dissolve in them compared to water. These results indicate that solvent polarity increases the extraction efficiency of the antioxidant compounds and antioxidant activity compared to water or a pure solvent.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 3 Experiments
### Evaluated Indicators  
In this study, the Mean Average Precision (mAP) was used to evaluate the accuracy of the proposed model; it should be noted that the higher the mAP value, the better the detection performance of the model. In this section, we discuss the calculation of mAP.  
In Figure 14, \"Precision\" and \"Recall\" represent the ratio of the number of correctly detected defects to all detection results and the total number of defects, respectively.  
\[\text{AP}= \int\frac{1}{0}\text{p}(\text{r})\text{d}_{\text{r}} \tag{2}\]  
\[\text{MAP}= \frac{\sum_{\text{c}}\text{AP}}{\text{m}} \tag{3}\]  
We set different values to verify the detection results and obtained different precision and recall values. As shown in Figure 14, the PR curve was drawn in the coordinates according to the aforementioned different values; the area enclosed by the PR curve represents the AP value. Then, the mAP was calculated according to Equations (2) and (3), where m denotes the number of defect types.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis
### Gait Analysis
#### 5.1.2 Gait Analysis: Pace
Walking speed is considered the \"sixth vital sign\" for its capabilities, reliability, and sensitivity that it can measure to assess and monitor overall health [111; 112]. These led researchers to use walking speed to provide bio-feedback training, assess fall-risk and server as an indicator of a neurological disorder [28; 43; 49; 60; 61; 65].
## 3 Experiment Results  
### Experiment Conditions  
Under laboratory conditions, the six DOF motion errors of a rotary axis were measured using our measuring device, as shown in Figure 5. The performance of the device was studied through experiments. The target rotary axis was the SKQ-12200 produced by KEOLEA. Its angular positioning error was 40\", and the repeatability value was 20\". The reference rotary axis was the ANT95-360-R produced by Aerotech. Its angular positioning error was 10\", and the unidirectional repeatability value was 0.5\". The repeatability value of the tilt motion errors was 3\"; the repeatability values of the axial and radial motion errors were 0.5 and 1 \(\upmu\)m, respectively. The measuring interval was 30\", and the rotation speed of the target rotary axis was about 0.55\({}^{\circ}\)/s. The experimental temperature was fixed at 25 \(\pm\) 1 \({}^{\circ}\)C. The rotational velocity of the reference axis fluctuated around the velocity of the target rotary axis. The following errors were about 250\({}^{\prime\prime}\).
## Abbreviations  
AI artificial intelligence AMI advanced metering infrastructure API application programming interface CPPS cyber-physical power system CPS cyber-physical system CPSG cyber-physical smart grid DDoS distributed denial of service DER distributed energy resources DoS denial of service DT digital twin EV electric vehicles EVCS electric vehicles charging station FDI false data injection attack GPS global positioning system ICT information and communication technology IT information technology
## 4 Discussion
### Varying Performances of the Eight Satellite-Based GPP Models in the Alpine Meadow  
Performance varied slightly by model in the alpine meadow, which might be related to the accuracy of the input variables or the methods used to address the environmental stresses. For example, the precision of the MODIS-GPP was the lowest in the alpine shrub meadow, primarily due to the uncertainties in the model parameters, such as \(\varepsilon_{\text{max}}\) and fPAR [(117; 31; 118)]. The MODIS-GPP algorithm assumed the same \(\varepsilon_{\text{max}}\) for all grassland types, which was 53.7% lower than the ground-based \(\varepsilon_{\text{max}}\) (0.19 vs. 0.41 g C mol\({}^{-1}\) PPFD, respectively) in the alpine shrub meadow. The mean eight-day MODIS fPAR was 33.2% higher than the fPAR (represented by EVI) used in the other seven models from 2003-2006 (0.43 vs. 0.32, respectively). The performance of the MODIS-GPP algorithm could therefore be greatly improved with site-specific optimized parameters [(118)]. The GR model exhibited relatively poor performance in the alpine swamp meadow, most likely due to (1) the overweighting of the PAR effects (as discussed in Section 4.1) and (2) the underweighting of environmental stresses on GPP. The environmental stress in the GR model was represented by fPAR (EVI) only, while in all the other models it was represented by both fPAR and LUE scalars. Overall, the intermodel differences of the performances were smaller in the alpine meadow than in the temperate steppe, most likely due to the lower sensitivity of the input variables to the land cover properties in the dense alpine meadow than in the sparse temperate steppe [(119; 120; 31; 121)]. For example, the MODIS fPAR tends to overestimate the actual condition [(31)] of the sparse vegetation due to the background soil conditions.
## 1 Introduction
The vast majority of studies took radiation intensity as a constant value, while only one study took this value by varying the daytime hours according to the weather conditions of the Moroccan city, Ouarzazate [31]. A modified nanofluid, a \"hybrid nanofluid\" (HNF), which consists of two or more nanoparticles dispersed in a base fluid, was used in only two studies and showed attractive results compared with mono nanofluids [42; 43]. As seen in the previous section, there is limited research concerning the application of hybrid nanofluids on PTC performance in the literature, despite its positive aspects, and the ability of which can be summarized as producing better thermal performances than mononononofluids and achieving reasonable costs by mixing expensive nanomaterials with cheap ones under optimum mixing ratios [42; 45]. In addition, there are no works available that focus on using hybrid nanofluids and analyzing the energetic and exergatic changes in a PTC under the climatic conditions of Budapest as a case study. Thus, in this study, the thermal performance of the PTC LS2 model was investigated under two cases; the first one, by applying a constant radiation intensity, and in the second one, under the dynamic radiation intensity of typical sunny and winter days in Budapest. The radiation intensity data of this location were obtained using the Hottel model. The ambient temperatures were obtained from the weather station located on the roof of the Energy Department of Budapest University. In addition, the major effects of inserting HNFs of untested nanoparticles that consist of tungsten and alumina oxide-based Therminol VP1 were obtained under various concentration volumes (0-4%) for a volume fraction equal to 50:50. The improvement effect of tungsten oxide nanoparticles is only mentioned as a monononofluid for two solar application types; the evacuated tube [46] and the PTC [47].