esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dispersion-Managed Link and WDM System Modeling
### Dispersion-Managed Link
The first span and the last span play this role, and they are called the pre-dispersion calibrator (DC) and post-DC, respectively. However, in this research, only the pre-DC play the role of NRD controller of the total link by varying its own length; that is, the DCF length in the first span is varied to decide the NRD of the former half link, and simultaneously, the DCF length of the last span is fixed to make the NRD of the latter half link 0 ps/nm.
# A Next-Generation Core Network Architecture for Mobile Networks
## 2 Related Work  
The scientific community and various standardization bodies (e.g., 5 GPP) have focused their attention and efforts towards new access networks for next-generation cellular networks [2; 3; 4]. In particular, many approaches focus on harmonizing different access technologies (e.g., LiWi, WiFi, LTE) [5], while others focus on improving spectrum efficiency [6; 7] also by deploying micro-cells and pico-cells [8; 9] with significant focus being given to mm-wave [10; 11].  
In the cellular core network, on the other hand, changes have been more incremental. In recent times, the most significant innovation has been the introduction of SDN and NFV. By leveraging SDN and NFV, relevant efforts focus on re-organizing the core network of operators and virtual operators on the cloud [12] and in many other ways [13; 14], but without changing the architecture which remains  
Figure 3: New virtualized cellular architecture.  
the same, although virtualized. In [15], the authors go in the opposite and more drastic direction of completely removing the core network, by having eNodeBs directly accessing the public Internet.  
We re-think the cellular core network and propose a new virtualized P2P architecture. This architecture still leverages SDN and NFV but does not follow the same thirty-year-old blueprint used by existing and past cellular networks. In doing so, we provide a new level of network security and flexibility as well as novel services that would not be possible otherwise.
## 4 Results and Discussion
### RCCF Performance Evaluation
#### 4.2.3 Confidence Calibration  
The confidence estimation component of RCCF provides reliable uncertainty quantification. Documents with confidence scores above 0.8 demonstrate 94.3% classification accuracy, while those below 0.4 show only 67.8% accuracy, enabling effective filtering and quality control in automated document processing systems.
## 4 Fractional-Order PID Adaptive Impedance Control
### Stability Analysis  
The simplified model for the interaction between the manipulator and the environment is shown in Figure 6. In the diagram, the fractional-order impedance control model is  
Figure 5: Fractional-order PID adaptive impedance control schematic diagram.  
represented as \(\upsilon(s)=\frac{k_{p}+\frac{k_{i}}{\bar{x}}+k_{d}s^{\mu}}{m_{r}s^{2}+b_{r}s+k_{r}}\), and among them, \(0<\lambda,\mu\leq 1\). It is typically assumed that there is no error in the robot's position control, which means \(R(s)=1\). The environmental interaction model is represented as \(K(s)=k_{e}\). According to the superposition theorem, the closed-loop output of the control system \(\epsilon(s)\) is given by Equation (40), as shown below:  
\[\epsilon(s)=\frac{F_{d}(s)\big{(}k_{d}s^{\lambda+\mu}+k_{p}s^{\lambda}+k_{i} \big{)}+X_{d}(s)\big{(}m_{r}s^{\lambda+2}+b_{r}s^{\lambda+1}+k_{r}s^{\lambda} \big{)}+k_{e}X_{\epsilon}(s)\big{(}k_{d}s^{\lambda+\mu}+k_{p}s^{\lambda}+k_{i} \big{)}}{m_{r}s^{\lambda+2}+b_{r}s^{\lambda+1}+(k_{r}+k_{p}k_{e})s^{\lambda}+k_ {d}k_{e}s^{\lambda+\mu}+k_{i}k_{e}} \tag{40}\]  
**Lemma 1** ([37]).: _A commensurate order system described by the following rational transfer function \(G(s)=Q(\omega)/P(\omega)\) where \(\omega=s^{\alpha}\), \(\alpha\in\mathbb{R}^{+}\) and (0 \(<\alpha<2\)) are stable if \(\mid\)arg\((\omega_{i})\mid>\alpha\pi/2\) with \(\omega_{i}\) is the \(i\)-th root of \(P\) (\(\omega\)) = 0._  
When \(\lambda=\mu=1\), the integer-order PID control system output is as shown in Equation (41):  
\[e^{\prime}(s)=\frac{F_{d}(s)\big{(}k_{d}s^{2}+k_{p}s+k_{i}\big{)}+X_{d}(s) \big{(}m_{r}s^{3}+b_{r}s^{2}+k_{r}s\big{)}+k_{e}X_{e}(s)\big{(}k_{d}s^{2}+k_{ p}s+k_{i}\big{)}}{m_{r}s^{3}+(b_{r}+k_{d}k_{e})s^{2}+(k_{r}+k_{p}k_{e})s+k_{i}k_{e}} \tag{41}\]  
Obtain the characteristic equation of the integer-order system from Equation (41) to derive its stability conditions:  
\[m_{r}s^{3}+(b_{r}+k_{d}k_{e})s^{2}+(k_{r}+k_{p}k_{e})s+k_{i}k_{e}=0 \tag{42}\]  
According to the Routh-Hurwitz criterion (here, for ease of derivation, we set \(m_{r}=1\)), we can obtain the stability conditions for Equation (42) as follows:  
\[(b_{r}+k_{d}k_{e})(k_{r}+k_{p}k_{e})-k_{i}k_{e}>k_{d}k_{e}(k_{r}+k_{p}k_{e})-k _{i}k_{e}=k_{e}^{2}\bigg{(}\frac{k_{r}k_{d}}{k_{e}}+k_{d}k_{p}-\frac{k_{i}}{k_{ e}}\bigg{)}>0 \tag{43}\]  
According to Lemma 1, by comparing the stability conditions and boundaries of the two control systems, we can find that when \(0<\alpha<1,\mid\)arg\((\omega_{i})\mid>\pi/2>\alpha\pi/2\). It implies that the stability region of the fractional-order system is larger than that of the integer-order system. When the integer-order system is stable, its stability conditions (Equation (43)) will definitely apply to the fractional-order system [38], ensuring the stability of the proposed strategy.
# SPME Method Optimized by Box-Behnken Design for Impact Odorants in Reduced Alcohol Wines
## 2 Materials and Methods
### Optimization of Sample Extraction Conditions  
Three model wines were prepared with tartaric acid (0.008 M), potassium hydrogen tartrate (0.011 M), and ethanol (5, 8 or 13% \(v/v\)), with the compounds of interest added at concentrations that approximately match the mid-point of the calibration curves. Sample extraction temperature (\({}^{\circ}\)C), time (min), and volume (mL), in addition to the ethanol content (% \(v/v\)), were optimized using a three-factor Box-Behnken design (Table 2). The optimum sample incubation temperature, SPME fiber exposure time, and sample volume combinations at each ethanol concentration werebased on a previously described method [13]. A quadratic equation with second-order interactions for the three factors was used to determine the maximum predicted signal for each analyte using the following equation:  
\[\hat{y}=b_{0}+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}+b_{12}x_{1}x_{2}+b_{13}x_{1}x_{3}+ b_{23}x_{2}x_{3}+b_{11}x_{1}{}^{2}+b_{22}x_{2}{}^{2}+b_{33}x_{3}{}^{2} \tag{2}\]  
\(\hat{y}=\) predicted response; \(b_{0}\) is the intercept or average response;  
\(b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}=\) linear terms associated with each factor (temp, time, sample vol.);  
\(b_{12}x_{1}x_{2}+b_{13}x_{1}x_{3}+b_{23}x_{2}x_{3}=\) second-order interaction terms between each factor;  
\(b_{11}x_{1}{}^{2}+b_{22}x_{2}{}^{2}+b_{33}x_{3}{}^{2}=\) quadratic terms for each factor;  
\(x_{1}=\) factor extraction temperature;  
\(x_{2}=\) factor extraction time;  
\(x_{3}=\) factor sample volume in a 20 mL vial.  
The significance of each experimental factor coefficient (\(b\)) in the quadratic equations was determined using a Students \(t\)-test following the calculation of a coded design matrix and the predicted responses and residuals [24], as described in the Supplementary Materials (Tables S1 and S2), with insignificant factors dropped from the final modelled response. The predicted maximum response and optimized conditions for each analyte were then used to determine the final global optimized sample incubation time, fiber exposure time, and sample volumes at each ethanol concentration. This was completed according to previously published methods [22] in which a desirability function is used to maximize the sampling sensitivity for analytes with low detector responses within the calibration range (presented separately for 5, 8 and 13% \(v/v\) ethanol concentration in the Supplementary Tables S3-S5, respectively). All statistical testing and modeling were conducted in Matlab R2007a (The Mathworks, Natick, MA, USA).
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Ground Mobile Sink-Based Frameworks
#### 2.3.3 Energy-Efficient PSO-Based Routing with Mobile Sink (EPMS)  
[PERSON] et al., proposed EPMS, an energy-efficient clustering scheme for WSNs with mobile sinks [63]. They used a particle swarm optimization (PSO) algorithm for clustering. PSO is an optimization method inspired by swarm intelligence that falls under the category of artificial intelligence. Initially, the sensor nodes forward their position and energy information to the BS. Using this information, the PSO algorithm divides the entire network into clusters and defines the K particles. By creating a region partition line, the entire network is divided into 2K K sub-regions. The fitness value \(F\) for K particles can be computed as follows:  
\[F=\alpha\sqrt{\sum_{i=1}^{2}(c_{i}-f_{i})^{2}}\ +\beta\sqrt{\sum_{i=1}^{2} \biggl{(}\frac{E_{i}}{c_{i}}-\frac{E_{sum}}{N}\biggr{)}^{2}} \tag{4}\]  
where \(F\) denotes the fitness value, \(c_{i}\) is the number of sensor nodes in region \(i\), \(N\) is the total number of sensor nodes, \(f_{i}\) is the number of CHs in each cluster, \(E_{i}\) is the total energy consumption in region \(i\), and \(E_{sum}\) is the total energy. Here \(\alpha\) and \(\beta\) are the weight factor and the relation is, (\(\alpha+\beta\)) = 1. The fitness values are evaluated, and the parameters of the particles are updated accordingly. The algorithm iterates until F converges to the minimum values, and \(M\) clusters are created. Subsequently, CH selection is performed for each cluster based on the residual energy of the sensor nodes. The node with the maximum residual energy in a cluster is selected as the CH. The selected CH is maintained until the next round of energy saving in the network. The data-collection round is completed when the mobile sink visits all the CHs in the network.  
Three types of data packets are used in the EPMS. First, the Hello packet--which consists of a cluster ID number, average residual energy, CH location, and time period--is used to determine the CH that sends data to the mobile sink. The second packet, Message-c, contains data for directly transmitting to the sink node. Finally, the Message-m packet is used to send information to the CH. The CH sends the collected data to the mobile sink when it moves to the CH. This process is repeated for all the CHs in the network.  
Advantages: The CH selection is carried to the next data transmission round, which helps to balance the network energy consumption. Unlike other studies, the packet delivery ratio and latency are evaluated for EPMS.  
Limitations: Clustering and CH selection are performed solely based on the residual energy of the sensor nodes. The transmission distance is not considered for CH selection, but it can have a negative impact on data gathering from the CH.
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Experimental Results
#### 3.3.2 Qualitative Comparison
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **mDice** & **mIoU** & \(\mathbf{S_{m}}\) & \(\mathbf{E_{m}}\) & **MAE** \\ \hline \multirow{5}{*}{ColonDB} & U-Net [9] & 0.504 & 0.440 & 0.710 & 0.781 & 0.059 \\  & U-Net++ [17] & 0.482 & 0.412 & 0.693 & 0.764 & 0.061 \\  & SFA [20] & 0.457 & 0.341 & 0.628 & 0.753 & 0.094 \\  & PraNet \({}^{\dagger}\)[27] & 0.711 & 0.644 & 0.820 & 0.872 & 0.043 \\  & E- U-Net [22] & 0.740 & 0.663 & - & - & - \\  & MSBNet [25] & 0.741 & - & 0.826 & 0.875 & 0.040 \\ \hline \multirow{5}{*}{ETS} & **COMMA \({}^{*}\)** & 0.712 & 0.645 & 0.823 & 0.864 & 0.045 \\  & **COMMA \({}^{\ddagger}\)** & **0.754** & **0.689** & **0.849** & **0.897** & **0.037** \\ \hline \multirow{5}{*}{ETS} & U-Net [9] & 0.399 & 0.340 & 0.684 & 0.740 & 0.036 \\  & U-Net++ [17] & 0.401 & 0.348 & 0.683 & 0.776 & 0.035 \\  & SFA [20] & 0.298 & 0.221 & 0.557 & 0.632 & 0.109 \\  & PraNet \({}^{\dagger}\)[27] & 0.628 & 0.571 & 0.794 & 0.841 & 0.031 \\  & E- U-Net [22] & 0.651 & 0.582 & - & - & - \\  & MSBNet [25] & 0.606 & - & 0.772 & 0.841 & 0.023 \\ \hline \multirow{5}{*}{CVC-T} & **COMMA \({}^{*}\)** & 0.709 & 0.643 & 0.845 & 0.887 & 0.018 \\  & **COMMA \({}^{\dagger}\)** & 0.711 & 0.648 & 0.844 & 0.887 & **0.015** \\ \cline{1-1}  & U-Net [9] & 0.711 & 0.631 & 0.843 & 0.875 & 0.022 \\  & U-Net++ [17] & 0.708 & 0.629 & 0.839 & 0.898 & 0.018 \\ \cline{1-1}  & SFA [20] & 0.468 & 0.334 & 0.641 & 0.817 & 0.065 \\ \cline{1-1}  & PraNet \({}^{\dagger}\)[27] & 0.871 & 0.801 & 0.925 & 0.972 & 0.010 \\ \cline{1-1}  & E- U-Net [22] & 0.886 & 0.813 & - & - & - \\ \cline{1-1}  & MSBNet [25] & 0.866 & - & 0.917 & 0.966 & 0.010 \\ \hline \multirow{5}{*}{} & **COMMA \({}^{*}\)** & 0.871 & 0.801 & 0.924 & 0.980 & 0.011 \\ \cline{1-1}  & **COMMA \({}^{\ddagger}\)** & **0.906** & **0.843** & **0.945** & **0.988** & **0.006** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of COMMA generalization performance with state-of-the-art methods on CVC-ColonDB, ETIS, and CVC-T datasets. \({}^{*}\) and \({\dagger}\) indicate that the ResNet and Res2 Net backbone encoders were employed, respectively. methods could not detect polyp regions with boundaries elaborately; in contrast, the proposed method was able to discriminate the polyp regions and boundaries. As a result, COMMA segregated the polyps in low-contrast and varied-size contexts by leveraging explicit boundaries and complementary representation.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 4 Results and Discussions
The parameters \(U/U_{\text{ref}}\) and \(\Delta\sigma_{u}/U_{\text{ref}}\) are used as the evaluation criteria of the simulation results of the RANS model in Case 1, \(\Delta U/U_{\text{ref}}\) and \(\Delta k/k_{\text{ref}}\) are used in Case 2 (\(\Delta U=U-U_{\text{ref}}\); \(\Delta k=k-k_{\text{ref}}\)), where \(U_{\text{ref}}\) and \(k_{\text{ref}}\) denote the wind speed and TKE at the reference site upstream, respectively, and \(U\) and \(k\) are used in Cases 3-4. Three error statistics methods are used in this paper, including the single-point relative error (e), mean absolute error (MAE), and relative root mean square error (RRMSE), which are defined by the following expressions:  
\[\mathbf{e}=\frac{|S_{i}-E_{i}|}{|E_{i}|}\bullet 100 \tag{5}\]  
\[\text{MAE}=\frac{1}{n}\frac{n}{1}\left(|S_{i}-E_{i}|\right) \tag{6}\]  
\[\text{RRMSE}=\sqrt{\frac{1}{n}\sum_{1}^{n}\left(S_{i}-E_{i}\right)^{2}}/ \frac{1}{n}\sum_{1}^{n}E_{i} \tag{7}\]  
where \(S_{i}\) and \(E_{i}\) denote the simulated and experimental values at the ith sample point, and \(S_{\text{avg}}\) and \(E_{\text{avg}}\) are the mean values of these two, respectively; \(e\) quantifies the numerical difference between the simulated and experimental values; MAE represents the overall deviation between the simulated and experimental values; RRMSE indicates the accuracy of the turbulence model.
## 2 Literature Review
### Synthesis of Research Findings  
Older adults with an impairment, fall history, cardiovascular disease, heart failure, stroke, chronic illness, or experienced with technology were more willing to adopt new technologies to compensate [45, 46, 63, 65, 89]. Older women with those experiences were early adopters of the devices [46]. IoT solutions built on newer technologies were the least preferred by older adults to age in place longer. They rejected remote home monitoring, smartphones, apps to control appliances, motion activated cameras, voice activated assistants, and emergency alerting systems. They preferred to continue using old, outdated technologies instead of adopting the newer [15, 43, 63, 85, 125]. Most of the IoT solutions proposed or experimented with required newer technologies and user familiarity with them [81, 83, 86, 87].  
Most of the technical, experimental, and pilot solutions proposed or studied appeared biased that older adults would find the IoT technologies beneficial, and would accept them without hesitation [26, 71, 126]. Older adults required training, technical support, and coaching with new technologies before they adopted [15, 43, 84, 88]. The high cost of new technologies was shown to be an adoption barrier [83, 84, 90]. Most of the researchers did not properly align the technology acceptance model with the use case of their study, and many did not explicitly limit their target population to older adults [45, 74, 91, 92].  
Older adults concerned about safety were interested in purchasing water leak detectors, backup generators, and automatic shutoff technologies for the stove [46]. Older adults experiencing health decline or a loss of independence were more likely to adopt the solutions to compensate for their condition [15, 46, 63, 65]. These findings confirmed that effort expectancy, performance expectancy, facilitating conditions, habit, and price value are significant and consistent predictors of older adults' behavioral intention to use technology. The findings legitimized the methodological strengths of this study using the UTAUT2, explicitly recruiting older adults as the target population, and including participants who did not have Internet access in the home [30].
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 3 The Proposed System Architecture
### The IoT WSANCS Architecture
#### 3.1.2 Network Topology  
The WSANCS is implemented using a start topology because of its improved ability to contain errors and their tolerance to proximity and common-mode failures [29; 30; 31; 32; 33]. A quantitative comparison of the error-containment capabilities was presented by [PERSON] [34]. In the proposed topology, each SA-N has a XBee end-device used to connect it to the S-N which has the XBee coordinator. The XBee technology defines a point-to-point (PTP) connection between a coordinator and an end device. The coordinator receives a signal from any SA-N and can route them to the other SA-N. The S-N works as a server and it controls and manages entire function of the network. Some of the advantage of using this topology are:  
1. It is easy to locate problems because if an end device fails, it affects only one sensor node.
2. It is easy to extend the network without disturbing the entire WSANCS.
3. It is easy to identify fault5 and remove nodes in the WSANCS.
4. It provides very high speed of data transfer.  
In addition, the S-N is connected to Internet through the RPI3+. This connection allows the system to send and received data to the user's mobile application as well as to upload the sensed data to a cloud database.
## 1 Introduction  
### Changes in the Spread of Photovoltaic Technologies in the World  
Nowadays, more and more countries understand the harmful consequences of climate change worldwide. Their common goal should be to keep the global temperature rise below 2 \({}^{\circ}\)C compared to the pre-industrial level and aiming for a maximum increase of only 1.5 \({}^{\circ}\)C [1]. In the transformation needed to achieve these targets and to reduce the greenhouse effect, the use of renewable energy plays a key role. Advances in renewable energy technologies have provided several sustainable alternatives,and more and more attention is also paid to solar energy. Many cities around the world have started to develop their solar energy programs in their urban planning to promote sustainable development and environmental protection. Energy from the Sun, which is available for all mankind, is the basis of most natural processes and it is a sustainable, plentiful, and clean resource [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. The annual solar energy potential arriving on the Earth's surface is approximately 8000 times greater than the current energy demand of the world. Photovoltaic (PV) technologies use PV cells and modules which transform the incoming solar energy into DC energy [12; 13; 14].  
In the PV sector a significant market increase has been observed in the last 10 years mainly due to the state subsidies introduced in many countries, the falling investment costs and the accelerated technological development [6; 15; 16]. In 2018 the share of renewable electricity production was 26.2% of all the electricity produced worldwide, of which PV technology represented 2.4%. In 2018 the global built-in photovoltaic capacity was 505 GW, with the largest proportions being China's 177.5 GW, the European Union's 115 GW, the USA's 62.4 GW and Japan's 56 GW (Figure 1) [6; 17; 18].  
The amount of electric energy produced by PV systems depends primarily on the solar radiation, the temperature, the technology, the current natural factors, the composition of the particular module and the combined effects of the installation and the efficiency rates. PV systems are primarily mounted on fixed mounting systems for economic reasons. However, an advanced mounting technique is to use a solar tracking system to increase the energy production [19; 20; 21; 22].
# Theoretical Modelling of Ion Exchange Processes in Glass: Advances and Challenges
## 4 Future Challenges
### Electro-Diffusion of Multivalent Metals  
Monovalent cations as Ag\({}^{+}\)/Na\({}^{+}\) and K\({}^{+}\)/Na\({}^{+}\) are by far the most used cations in IE processes in glass. However, some successful attempts at doping glass with multivalent cations have also been realized, mainly with transition metals, but also with rare-earths. For example, [PERSON] et al. obtained Co\({}^{2+}\), Au\({}^{3+}\) and Cr\({}^{3+}\) diffusion profiles in silicate glasses [94; 95]; and [PERSON] et al. introduced Er\({}^{3+}\) cations inside soda-lime glasses [96]. These authors used field-assisted configurations with electric fields up to 400 V/mm,as thermal-assisted IE is not very effective with multivalent ion species. This allowed for penetrations of cations of up to \(\sim\)1 um from deposited films. The main utility of glass doping with multivalent transition metals arises from the possibility of converting the glass into an active media. Therefore, optical amplification or waveguide lasers have been demonstrated in erbium-doped glasses [97], and Cr\({}^{4+}\)-doped materials have been proposed as both laser gain materials and saturable absorbers for passive Q-switching in IR lasers [98].  
The ion exchange with multivalent cations gives rise to concentration-dependent structural changes in the glass matrix. This is due to the local coordination rearrangements, which are produced at the ion sites [99]. In addition, the amount of metal that penetrates into the glass matrix, as well as the shape of the concentration profiles depend strongly on the process parameters. Therefore, the model presented in Section 3, which includes the MIE, should be used in the modelling of such processes. Likewise, when the multivalent cations are introduced into the glass, a region depleted of cations, similar to that observed in poled glasses, is formed. Therefore, the approaches used in the modelling of glass poling could be applied to the present processes. However, little work has been done in these directions, and a comprehensive model of electro-diffusion of multivalent metals is still lacking.