esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling the Viscosity  
Many useful models of the viscosity of amorphous materials have been proposed for the temperature range (2) (see the overviews [1; 2; 3; 4; 5]), among which we can note the two most developed and frequently used models, namely the Vogel-Fulcher-Tammann (VFT) equation [9; 10; 11], and the Williams-Landel-Ferry (WLF) equation [12; 13]:  
\[\eta=\eta_{0}\;exp\bigg{(}\frac{B_{0}}{T-T_{0}}\bigg{)}, \tag{3}\]  
Figure 1: The viscosity regimes of chalcogenide glasses and melts [6].  
\[log\ a_{T}=-C_{1}\frac{T-T_{r}}{T-T_{r}+C_{2}}, \tag{4}\]  
where \(\eta_{0}\) and \(B_{0}\) are temperature-independent constants and material-dependent parameters, \(a_{T}\) is the relative viscosity \(a_{T}=\eta(T)/\eta\big{(}T_{g}\big{)}\), \(C_{1}\) and \(C_{2}\) are empirical temperature-independent constants, \(T_{r}\) is the reference temperature typically taken as the glass transition temperature \(T_{r}=T_{g}\). The validity of these equations has been demonstrated in many works for various glass-forming systems, e.g., see Refs. [1; 2; 3; 4; 5; 14; 15; 16; 17; 18; 19; 20]. We note that the activation energy of viscous flow starts to decrease exactly at temperatures exceeding \(T_{g}\), see e.g., Figure 1 of Ref. [5] and Figures 1, 2, and 4 of Ref. [19]. The WLF equation is known to correctly describe the molecular kinetic processes in the glass transition region and provide fairly exact temperature dependence of relaxation time \(\tau(T)\) and the viscosity \(\eta(T)\) within the glass transition range of temperatures for amorphous materials of various natures such as polymers, organic, and inorganic glasses, and metallic amorphous alloys [14; 15; 16; 17; 18; 19; 20]. It is known that the WLF equation can be derived in [16] without specifying the functional dependence \(\eta(T)\) based on the series expansion of function \(ln(\eta(T))\) near the \(T_{g}\) with respect to the small dimensionless parameter \(\lambda=(T-T_{g})/T_{g}<<1\). This led to following Equation (5) in terms of natural logarithms (\(ln\)) rather than decimal (\(log\)) logarithms while assuming \(T_{r}=T_{g}\):  
\[ln\ a_{T}=-\bigg{(}\frac{A^{2}}{B}\bigg{)}\frac{T-T_{g}}{T-T_{g}+\bigg{(}\frac {A}{B}T_{g}\bigg{)}},\text{ where }A=-\left.\frac{\partial ln\ \eta}{\partial\big{(}T/T_{g}\big{)}}\right|_{T=T_{g}},\ B= \frac{1}{2}\left.\frac{\partial^{2}ln\ \eta}{\partial\big{(}T/T_{g}\big{)}^{2}}\right|_{T=T_{g}} \tag{5}\]  
From here, the WLF equation constants \(C_{1}\) and \(C_{2}\) acquire a physical meaning, expressed through the derivative parameters \(A\) and \(B\):  
\[C_{1}=\frac{A^{2}}{B},\text{ and }C_{2}=\frac{A}{B}T_{g}, \tag{6}\]  
Additionally, we observe that the VFT and WLF equations are equivalent if we suppose that:  
\[\text{Either }B_{0}=C_{1}C_{2}ln10\text{ in }(\ref{eq:
## 2 Wireless Communication for Maritime Applications
### Communication between Sensors and Relays
#### 2.1.1 WiFi HaLow  
A Wireless Local Area Network (WLAN-IEEE 802.11) is adequate for monitoring and data acquisition applications. It boasts a built-in roaming functionality that is useful for applications with moving devices [9]. In the 2.4 GHz band, a WLAN achieves a range of 200 m (and up to 500 m in free line of sight). In the 5 GHz band (IEEE 802.11a), this value comes down to around 50 m. A WLAN allows for high data throughput, but power consumption is also considerable [9].  
Wi-Fi HaLow (IEEE 802.11.ah) was developed considering the necessity of low-power modules with low data throughput, such as the ones used in most MIoT applications. It operates in the sub-GHz RF spectrum region, which offers less attenuation over distance and with obstacles than 2.4 or 5 GHz frequencies. Consequently, it can provide significantly higher working ranges, connecting devices approximately 1 km away from the access point. Additionally, power-saving features allow for multiyear battery operation [10].  
A Wi-Fi HaLow network is compatible with existing Wi-Fi networks without a loss of RF performance on either side, i.e., it can be deployed with existing wireless networks already in place [10].  
The IEEE 802.11 ah standard defines data rates that range from 150 kbps (at a 1 km range) to 86 Mbps and supports up to 8191 devices per SSID, making Wi-Fi HaLow suited for a wide variety of applications with a multitude of different requirements [10; 11].
###### Abstract  
Despite advances in remote sensing-based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP\({}_{\text{MOD}}\)) is less well understood over rice-wheat-rotation cropland. To improve the performance of GPP\({}_{\text{MOD}}\), a random forest (RF) machine learning model was constructed and employed over the rice-wheat double-cropping fields of eastern China. The RF-derived GPP (GPP\({}_{\text{RF}}\)) agreed well with the eddy covariance (EC)-derived GPP (GPP\({}_{\text{FC}}\)), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m\({}^{-2}\) d\({}^{-1}\). Therefore, it was deemed reliable to upscale GPP\({}_{\text{CC}}\) to regional scales through the RF model. The upscaled cumulative seasonal GPP\({}_{\text{RF}}\) was higher for rice (924 g C m\({}^{-2}\)) than that for wheat (532 g C m\({}^{-2}\)). By comparing GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{EC}}\), we found that GPP\({}_{\text{MOD}}\) performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPP\({}_{\text{MOD}}\) was calibrated by GPP\({}_{\text{RF}}\), and the error range of GPP\({}_{\text{MOD}}\) (GPP\({}_{\text{RF}}\) minus GPP\({}_{\text{MOD}}\)) was found to be 2.5-3.25 g C m\({}^{-2}\) d\({}^{-1}\) for rice and 0.75-125 g C m\({}^{-2}\) d\({}^{-1}\) for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.  
random forest; gross primary productivity; eddy covariance; MOD17A2H; rice-wheat rotation cropland 2021
## 3 Methodology
### Adaptation Process of Fitting the Mathematical Model to a Regression Model  
A specific step-wise process was followed to adapt the mathematical model presented in Section 3.1. Initially, the training dataset was prepared by including all necessary inputs. Equation (1) requires the estimation of two variables, \(w\) and \(lux\). All \(w\) values are presented in Table 2. Light levels in the range of 100 to 300 lux are typically needed for everyday tasks. Moreover, depending on the activity, the light level is more frequently required to be in the 500 to 1000 lux range. The necessary light level may even reach 1500-2000 lux for precise and thorough work, especially near a window [29]. Correspondingly, the \(lux\) values that had to be fitted as inputs ranged from 0 to 2000 lux.  
The scale of the illuminance level in lux was based on the European Lighting Standard \"EN12464-1\" [30], which is depicted in Figure 4. Accordingly, we did not use all the values from 0 to 2000 as inputs for the model, but various steps were implemented based on this scale. Specifically, from 0 to 50, step 10 was used; f  
Figure 3: Types of regression.  
used; from 100 to 150, step 25 was used; from 150 to 1000, step 50 was used; and from 1000 to 2000, step 100 was used. Eventually, for all \(w\) and \(lux\) values selected as inputs, visual comfort values based on Equation (1) were estimated. These visual comfort values were utilized as the ground truth to train the regression model. All the dataset values were utilized for training; the dataset was not split into training and validation sets, as the model could be tested with all values from 0 to 2000 that were not used for the training process.  
After the training process, the pre-trained model was extracted and used for the testing process. Finally, the pre-trained model was tested using as inputs the values from 0 to 2000 that were not used for the training process, and the results were compared to the corresponding outputs from the mathematical model. The aforementioned process for adapting the mathematical model to a regression model is depicted in full in Figure 5.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 5 Discussion and Future Outlook
### The Efficiency of BFL  
The model training efficiency of federated learning is mainly related to the allocation of network resources and the reception efficiency of devices. A key aspect of federated learning (FL) is the need for a centralized aggregator to maintain and update the global model. However, orchestrating centralized aggregators is infeasible due to many operational constraints [62] and hinders model performance efficiency of federated learning. Model compression, slice transfer, etc., have been proposed to solve this problem [109], but it is unclear whether existing methods can achieve a balance between efficiency and accuracy of federated learning.
# In Vitro Preliminary Evaluation of Bacterial Attachment on Grooved and Smooth Healing Abutments
## 2 Methods
### Scanning Electron Microscopy  
Pictures of the grooved and smooth abutment surfaces taken under SEM are shown in Figures 4-7, and a difference in shape between the two bacterial strains may be appreciated. The bacterial adherence of both strains is clearly larger in the grooved areas of the marked abutments as compared to in the smooth ones.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Abutment Type**} & \multicolumn{2}{c}{\(F.n\) **Counts (CFU)**} & \multicolumn{2}{c}{\(Pg\) **Counts (CFU)**} \\ \cline{3-6}  & \multicolumn{2}{c}{} & \multicolumn{2}{c}{**Bacteria/Field**} \\ \cline{2-6}  & & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{3}{*}{Abutment Type} & Smooth & 10.1500 & 3.33791 & 192.1750 & 66.70708 \\ \cline{2-6}  & Grooved & Within grooves & 1043.5500 & 397.52536 & 4026.3000 & 347.55053 \\ \cline{2-6}  & Between grooves & 11.4500 & 4.92133 & 175.7000 & 44.72210 \\ \hline \hline \end{tabular}.  
\end{table}
Table 1: Bacterial counts (mean and SD) in the four experimental groups.  
Figure 3: (**a**) _Fusobacterium nucleation_ count on smooth surface, in the grooves and in between; (**b**) _Perplyomonas gingivalis_ count on smooth surface, in the grooves and in between. CFU (colony-forming unit).  
Figure 4: _Fusobacterium nucleatum_ attached on the border-line between the smooth (dark side of the figure) and the grooved parts (bright side, the left side of the figure). The larger number of bacteria within the groove as compared to that in the machined area of the healing cap (right side) is evident (magnification, \(\times\) 2500).  
Figure 5: High number of _Fusobacterium nucleatum_ found within the grooved part of the healing cap (magnification, \(\times\) 2500).  
Figure 6: _Porphyromonas gingivalis_ growth on the machined surface. Note the low bacterial count in this area (magnification, \(\times\) 2500).
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 2 Preliminaries  
Throughout this paper, the following notations are adopted. \(W:\ X\to Y\) indicates a Binary Input Additive White Gaussian Noise (BI-AWGN) channel with input alphabet \(X\) and output alphabet \(Y\). \(W_{X}(x)\) is the probability that \(x\in X\) is sent across \(W\). \(W(y|x)\) is the transition probability from the input \(x\) to the received output of the channel \(y\in Y\). The input alphabet \(X\) are always \(\{0,1\}\), the output alphabet and the transition probabilities may be arbitrary. The notation \(\mathbf{u}_{1}^{N}\) is used as shorthand for denoting a row vector \((u_{1},u_{2},\ldots,u_{N})\), \(\mathbf{u}_{i}^{j}\) denotes the sub-vector \((u_{i},u_{i+1},\ldots,u_{j})\) from \(\mathbf{u}_{1}^{N}\), \(1\leq i\leq j\leq N\). Bhattacharyya parameter \(Z\) is used as a measure of reliability, where it is the upper limit on the maximum-likelihood (ML) decision error probability of the binary input channel. The mathematical asymptotic notation \(O\) has been used to describe the limiting behaviour of the function complexity.
## 5 Results and Analysis of the Optimization Algorithm
### Results of the Support Vector Machine Optimization
After several iterations, the Sigmoid kernel function finds the best \(c\) and \(g\) parameters. The classification of the test samples was re-completed using the \(c\) and \(g\) parameters, and the final accuracy was actually as high as 98.8%. The final classification effect is shown with Figure 14b. The high accuracy indicates that Sigmoid does have good and generalization ability in the range of values of \(c\) and \(g\). On the other hand, it also shows that the range of values of \(c\) and \(g\) is relatively correct.
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 5 Conclusions  
In this chapter, an optimized supercell radial gradient seismic metamaterial is proposed in which the supercell structure is composed of three kinds of unit cells filled at different angles and the optimized supercell structure is formed by adjusting the soil parameters. The finite element method is used to calculate the energy band curve, mode, and transmission spectrum of the triangular filled unit cell attenuation seismic Lamb wave. The influence of the geometric and material parameters on the band gap is studied using geometric and material parameter analysis. The band gap characteristics of the optimized supercell structure are studied and verified using frequency response. The attenuation characteristics of the optimized supercell structure for seismic surface wave are further studied and verified in the time domain.  
The results show that the geometric parameters and material parameters of the radial gradient seismic metamaterial can effectively adjust the band gap range of the unit cell. The cutoff frequency of the first band gap is more significantly affected by the parameter change than the initial frequency. The mechanism of the band gap is the local resonance caused by different materials acting as oscillators. The optimized supercell radial gradient seismic metamaterial structure can achieve good attenuation of seismic Lamb waves in an ultra-low-broadband range of 2.35 Hz-20 Hz. The widening mechanism of the band gap is the coupling of oscillator mass and system stiffness introduced by different filling rates and material parameters. At the same time, for the surface wave in the ultra-low-frequency  
Figure 14: (**a**) Seismic wave time\(-\)history maps; (**b**) seismic wave spectrum curve; (**c**) x\(-\)direction acceleration response; (**d**) z direction acceleration response.  
range of 3.5-9 Hz, the optimized supercell radial gradient seismic metamaterial barrier also has an effective attenuation effect.  
Conceptualization, Q.Y. and K.S.; methodology, Q.Y. and K.S.; software, Q.Y. and K.S.; validation, Q.Y., K.S. and Y.L.; formal analysis, L.L.; investigation, Y.L. and J.B.; resources, Q.Y. and L.L.; data curation, K.S.; writing--original draft preparation, K.S. and Y.L.; writing--review and editing, L.L. and J.B.; visualization, K.S. and Y.L.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China (No. 51405368), the Natural Science Foundation of China Shaanxi Province under Grant No. 15 JK1405, and the Natural Science Foundation of China Shaanxi Province under Grant No. 2017J7S024.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 3 Performance of Communication Signal
### Communication Capacity  
When the navigation signal does not exist, the whole communication capacity is expressed as  
\[T_{total}=T_{c}N_{c} \tag{20}\]  
where \(T_{c}\) is the number of frames in one unit band during the time length \(T\) and \(N_{c}\) is the number of unit bands.  
For method 2, the capacity loss of the communication signal is determined by the number of frames and the unit bands that the navigation has occupied. It is expressed as follows,  
\[V_{loss}=\frac{T_{N}N_{N}}{T_{total}} \tag{21}\]  
where \(T_{N}\) is the number of frames that navigation signal has occupied during the time length \(T\) and \(N_{N}\) is the number of occupied unit bands.  
For method 3, the capacity loss is determined by the number of unit bands that the navigation signal has occupied, which is shown as follows,  
\[V_{loss}=\frac{N_{N}}{N_{c}} \tag{22}\]
###### Abstract
[PERSON] and [PERSON] et al (2007, 2015) comprehensively evaluated hydrocarbon source rocks in the Huanghekou Depression and concluded that the E\({}_{2}\)s\({}_{3}\) and E\({}_{3}\)s\({}_{1}\) formations exhibit favourable characteristics, such as good organic matter (OM) types and high thermal maturity, making them highly promising prospects for hydrocarbon generation [13; 14]. [PERSON] et al. (2011) examined the hydrocarbon accumulation levels and degree of accumulation in the study area, focusing on its structural, fault-related, and sequence-related aspects. The findings suggest that the oil and gas exhibit late-charging characteristics [15]. The formation of source rocks with excellent hydrocarbon quality is widely acknowledged to be controlled by the sedimentary environment. In recent years, abundant research has been conducted on sedimentary environment factors that influence OM formation. Changes in the sedimentary environment have direct implications for water body depth, lake basin development, plant community evolution, and detrital material transport [16; 17]. Currently, various researchers are employing diverse methodologies for sedimentary environment reconstruction, including biomarker analysis, inorganic geochemical techniques, palaeontological fossil examinations, and palynological studies. Commonly utilised parameters for sedimentary environment assessment include palaeoclimatic conditions, bathymmetric depth variations, salinity fluctuations, redox environment changes, and provenance determination [18; 19; 20; 21; 22]. The Huanghekou Depression has abundant oil and gas resources, and the Shahejie Formation is a key layer for oil and gas exploration. Previous studies on sedimentary environments have primarily relied on single indices, such as biomarkers, trace elements, and isotopes; moreover, extensive studies on the correlation between the enrichment of organic matter and the environments in which it is deposited are insufficient [6; 13]. In light of prior research and considering the tectonic context of the region, this study aimed to analyse the sedimentary environment of the source rocks in the Huanghekou Depression within the Shahejie Formation using organic and inorganic geochemical methods. This study elucidates the mechanism underlying OM enrichment in this formation and establishes a model for its formation and enrichment.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
###### Abstract  
New versions of HTTP protocols have been developed to overcome many of the limitations of the original HTTP/1.1 protocol and its underlying transport mechanism over TCP. In this paper, we investigated the performance of modern Internet protocols such as HTTP/2 over TCP and HTTP/3 over QUIC in high-latency satellite links. The goal was to uncover the interaction of the new features of HTTP such as parallel streams and optimized security handshake with modern congestion control algorithms such as CUBIC and BBR over high-latency links. An experimental satellite network emulation testbed was developed for the evaluation. The study analyzed several user-level web performance metrics such as average page load time, First Contentful Paint and Largest Contentful Paint. The results indicate an overhead problem with HTTP/3 that becomes more significant when using a loss-based congestion control algorithm such as CUBIC which is widely used on the Internet. Also, the results highlight the significance of the web page structure and how objects are distributed in it. Among the various Internet protocols evaluated, the results show that HTTP/3 over QUIC will perform better by an average of 35% than HTTP/2 over TCP in satellites links specifically with a more aggressive congestion algorithm such as BBR. This can be attributed to the non-blocking stream multiplexing feature of QUIC and the reduced TLS handshake of HTTP/3.  
satellite networks; HTTP/1.1; HTTP/2; HTTP/3; QUIC; TCP congestion control; BBR; cubic
## 5 State of the Art on IoT Systems for Industrial Process Control
### Comparative Analysis of Process Control Applications  
The in-depth analysis of IoT and cloud computing highlights the crucial role of these tools in optimizing industrial operations and resources. The research mentioned provides a diverse panorama of applications and methodologies adopted to improve the interaction between machines and systems, with a strong focus on data collection and analysis. In particular, using a private cloud provides a solid foundation for making data immediately accessible and usable for making more informed decisions. At the same time, adopting an end-to-end approach for data management in cyber-physical systems demonstrates that continuity and interconnectivity between data are essential to ensure efficiency and operational speed. The importance of data analytics is further supported by machine learning and mining techniques that enable the detection of anomalies in systems. This aspect is particularly critical in industries where even a small imperfection can have significant consequences. In monitoring and control, using IoT technologies for plant supervision represents a growing need in the current industrial landscape. Integrating SCADA systems with NB-IoT networks leads to better failure prevention and smarter and more responsive asset management. The innovation of digital twinning marks a significant step towards integrating the physical and virtual worlds. This fusion certainly revolutionizes the monitoring of operations and the training and validation of intelligent control systems. Finally, the importance of adopting quality control and monitoring metrics, now facilitated by advanced technologies such as AI, allows for the optimization of interactions in smart factories. The conclusion highlights a central theme: Despite the different methodologies and research objectives, there is a convergence in the approach to IoT and cloud computing as tools for innovation and continuous improvement. The digitalization of industrial processes and advanced data analysis techniques are crucial to improving efficiency and the ability to respond to emerging issues in production. In this context, smart factories are the industry's future, ready to respond to market challenges with agility and precision. Table 5 summarizes the characteristics of each analyzed reference, with the application performance shown in the last column.