esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of Artificial Intelligence in eParticipation: Mapping Current Research
## 4 Results
### Mapping the Domain
#### 4.2.4 eParticipation Effects  
Studies related to the \"Deliberative\" effect: Eleven articles explore various techniques and technologies to enhance participation quality, ranging from chatbots for organizing arguments [77] to applications facilitating ePetition drafting [61] and virtual reality approaches [71]. Additionally, analyses of online consultation platform designs concerning discussion quality principles are included in the literature [41; 79], while there is a study proposing an innovative platform based on game design principles [55]. Challenges encountered in online consultation initiatives aimed at fostering qualitative discussions for policy formulation are also documented [81].  
Studies related to the \"Democratic\" effect: This category is relatively infrequently mentioned, with sporadic references to democracy. Some view public debates as fundamental to democratic societies [81] while technological advancements are sometimes presented as tools to aid democracy [50; 54]. Conversely, concerns are raised about the democratic nature of online consultations due to inadequate technological understanding [79] or the perceived imposition of technologies on citizens [41]. It is also argued that increased citizen participation does not inherently equate to enhanced democracy [44].  
Studies related to the \"Civic Engagement\" effect: The objective of boosting civic engagement is highlighted in most articles discussing eParticipation outcomes, with various AI technologies developed to facilitate or support eParticipation activities. These technologies range from chatbots [77] to text analysis techniques [49; 58; 63; 72; 80] and ePetition assistance systems [86]. Moreover, proposals for eParticipation platforms addressing both governments and citizens are put forth [51; 60; 74; 78]. Recommendations for government adoption of social media platforms based on citizen message analyses are also made [83; 68]. Two studies delve into the scalability of online consultations and the democratic considerations in their design [41; 79]. Finally, other studies include proposals for new policy-making cycles [50] and descriptions of challenges in integrated online consultation initiatives for policymaking [81]. Table 4 summarizes the research articles per eParticipation effect.
### Parent Protocol Analysis  
Z-SEP is a zonal stable election protocol for a wireless heterogeneous sensor network, in which nodes differ in initial power. In this algorithm, the connection of nodes with the sink was carried out in a hybrid way: (1) direct communication and (2) communication through the cluster head. Z-SEP offers two types of nodes: (1) regular normal nodes that are located near the base station, and (2) extended advanced nodes that are located at a distance from the BS. The initial energy of normal nodes is lower than that of advanced nodes. The deployment field area is limited in scope and divided into three zones: (1) zone 1, (2) zone 2 and (3) zone 3. Normal sensor nodes are placed in zone 1. Some of the advanced nodes are located in zone 2 and some in zone 3. Normal nodes are connected to base station directly and communicate, send data in direct mode, whereas the extended advanced nodes form the cluster heads and the cluster heads aggregate records from their member nodes. The cluster heads collect the aggregated data and transmit it to the BS. In the unrestricted area, as the field length changes, the communication of the extended advanced nodes with the BS becomes more difficult. In this case, nodes that are distant from base station, will consume energy a lot. Consequently, the network lifetime will be reduced. This will also affect the selection of the cluster head among the extended advanced nodes. In short, the Z-SEP protocol is discussed briefly below:  
* Direct communication with the base station requires a lot of energy. So far, the sensor node battery capacity and computing capabilities are limited. Clustering and cluster head elections are generated only on extended advanced nodes. CHs aggregate and collect sensed records from their member nodes and transfer it to BS. Consequently, network instability arises from high energy consumption as these nodes are quickly depleted. Consequently, the entire service life of the WSN is reduced.
* It is a heterogeneous routing protocol in which the heads of the cluster are randomly elected from among the advanced nodes, wherefore, the probability that an advanced node with low residual energy, which is quickly depleted and leads to network instability, will be elected as the head of the cluster is quite high. Instability arises when the first head node of the cluster dies. This leads to imbalance in the cluster. Moreover, there is a possibility that there will be no cluster head left for that cluster when a huge amount of nodes die out.
* The instability of the sensor network in the Z-SEP protocol can also be caused by the fact that with an increase in the size of the deployed area of the field, the head nodes of the cluster will start to die out faster, since they would consume more power to deliver data to the sink. Since the distance and energy consumption are directly proportional to each other. In addition, because regular normal nodes and cluster heads in extended advanced nodes communicate directly with the base station.
## 4 Ice
### Ice Sheet  
Altimetry is probably one of the most powerful tools for ice sheet observation. Our vision of the Antarctic ice sheet has been deeply transformed since the launch of the ERS-1 satellite in 1991. With the launch of ERS-2, Envisat and SARAL/AltiKa, the series of altimetric observations now provides more than 20 years of continuous and homogeneous observations that allow monitoring of the shape and volume of ice sheets. The topography deduced from altimetry is one of the relevant parameters revealing the processes acting on ice sheet. Moreover, altimeter also provides other parameters such as backscatter and waveform shape [4] that give information on the surface roughness or snow pack characteristics.  
The most surprising features of Ka-band observations above the Antarctica ice sheet is the very large space and time variability of the backscatter. While the Ku-band variability was about 0.5 dB over few cycles, it can reach 3 or 4 dB in Ka-band. This unexplained observation probably comes from the small wavelength (0.8 cm instead of 2.3 cm) that is very sensitive to snow metamorphism such as surface hoar. These structures are related to snow drift and sublimation and is one of the most unknown of the polar snow. Thus, one of the major limitation of radar altimeter above ice sheet is the penetration of the radar wave within the dry snowpack. For the classical Ku-band used by altimeters, the penetration reaches a few m. The slightest changes of surface echo or of volume echo affect the height retrieval. At the seasonal scale, the impact on height can be of few tens of centimetres. Moreover, this penetration may induce a long term artificial change in height caused by either change in the surface or snowpack properties. On the other hand, volume echo contains few interesting information on snow stratification and on ice grain size, the two volume components of Ku band. [37] show that the penetration depth in Ka-band is indeed between 50 cm and 1 m (see Figure 22). Moreover, they show that the stratification of the snow pack does not play a role in the volume component, the scattering by ice grain being the unique physics. The comparison between Ku and Ka-band may then correct from the penetration error and help in retrieve some snowpack parameter.  
Figure 22: Difference (in m) between Ka and Ku-band height from the Envisat and SARAL/AltiKa altimeter over Antarctica. Due to the difference of penetration, in the central part, the Ku-band elevation is indeed below the Ka one. Near the coast, the smaller antenna aperture of Ka-band yields to a smaller slope error. In the West Antarctica sector, the negative pattern is due to the decrease of surface topography between the time of Envisat and of SARAL/AltiKa.  
The characteristics of SARAL/AltiKa ensures a temporal stability of height retrieval that allows to obtain elevation trends with a good confidence. We well map the impressive loss of outlet glaciers of the West part of the continent. We are able to describe the large digging of Pine Island glacier from ERS-1 to Envisat and now its large enlargement during SARAL/AltiKa period [2].
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 4 Simulation Results
### Convergence Behaviour
For the trajectory to propagate, the increase in the SNR value must raise the transfer characteristic of the demapper high enough to open the tunnel. The system with the log-normal channel model in Figure 17 shows that the trajectory that encountered the tunnel opening closes at \(I_{(A;\ E,\ D_{c})}\)= 0.92 and \(I_{(E,\ D_{m};\ A)}\)= 0.68 for SNR = 8 dB and at SNR = 10 dB. The tunnel closes at \(I_{(A;\ E,\ D_{c})}\)= 0.98 and \(I_{(E,\ D_{m};\ A)}\)= 0.78, implying that the receiver will not converge at 8 and 10 dB, whereas the tunnel opens at SNR = 11 dB, and the trajectory experiences a bottleneck at \(I_{(A;\ E,\ D_{c})}\) = 1 and \(I_{(E,\ D_{m};\ A)}\)= 0.82, implying that a further increase in iterations at this particular value of SNR will not improve the performance of the system. Again, for the system with gamma-gamma in Figure 18, it can be seen that the tunnel opening closes at \(I_{(A;\ E,\ D_{c})}\)= 0.09 and \(I_{(E,\ D_{m};\ A)}\)= 0.34 for SNR = 10 dB, implying that the receiver will not converge. There is a similar situation for SNR = 12 dB with \(I_{(A;\ E,\ D_{c})}\)= 0.75 and \(I_{(E,\ D_{m};\ A)}\)= 0.56, SNR = 14 dB with \(I_{(A;\ E,\ D_{c})}\)= 0.96 and \(I_{(E,\ D_{m};\ A)}\)= 0.7 and, finally, SNR = 15 dB at \(I_{(A;\ E,\ D_{c})}\)= 0.98 and \(I_{(E,\ D_{m};\ A)}\)= 0.75. As the iteration continues, the iterative decoder converges to a final point at the intersection of the ExIT chart curves. Hence, the mutual information improves by following the guide of the tunnel between curves, and the convergence occurs at SNR = 16 dB for the proposed system with the strong channel at \(I_{(A;\ E,\ D_{c})}\)= 1 and \(I_{(E,\ D_{m};\ A)}\)= 0.8. Consequently, the ExIT chart provides an accurate prediction of the convergence behaviour, which is verified and visualized by the simulated decoding trajectory. Figure 11: ExIT chart function for the demapper PNC system employing the TWR-FSO log-normal channel model. Figure 12: ExIT chart function for the demapper PNC system employing the TWR-FSO gamma-gamma channel model. Figure 13: ExIT chart function for the outer decoder. Figure 16: The impact of TWR-FSO with the PNC demapper over the strong turbulence link obtained by the Monte Carlo simulation. Figure 14: BER process for the BICM-ID decoder transfer characteristics function. Figure 15: The impact of TWR-FSO with the PNC demapper over the weak turbulence link obtained by the Monte Carlo simulation.
## 3 Results and Discussion
### Effects of Ultrasonic Pretreatment on Solubilization of Organic Matter
[59], who obtained an increase in the organic matter solubilization between 16% and 100% during the UP of microalgae. The ultrasonic method for microalgal biomass disintegration before AD was also used by [PERSON] et al. [60]. In general, they found that the higher energy applied resulted in a higher COD solubilization. Some studies have shown positive effects of ultrasonication on the disintegration of anaerobic and aerobic granules [61; 62]. Ultrasounds disintegrate both cellular and extracellular matter, therefore, the increase in the concentration of the soluble COD is achieved by solubilizing the solid matter, as well as the extracellular polymeric \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{\begin{tabular}{c} **Sonication Time** \\ **[s]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Soluble COD** \\ **[mgO\({}_{2}\)/L]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **COD** \\ **Solubilization Degree** \\ **[\%]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Soluble TOC** \\ **[mg/L]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **TOC** \\ **Solubilization Degree** \\ **[\%]** \\ \end{tabular} } \\ \hline V1 & 0 & 119 \(\pm\) 11 & - & 89 \(\pm\) 6 & - \\ V2 & 50 & 344 \(\pm\) 39 & 25 \(\pm\) 2.1 & 302 \(\pm\) 27 & 35.0 \(\pm\) 2.4 \\ V3 & 100 & 617 \(\pm\) 43 & 55.3 \(\pm\) 3.9 & 476 \(\pm\) 52 & 63.6 \(\pm\) 7.0 \\ V4 & 150 & 719 \(\pm\) 49 & 66.7 \(\pm\) 3.0 & 533 \(\pm\) 42 & 73.0 \(\pm\) 7.9 \\ V5 & 200 & 771 \(\pm\) 83 & 72.4 \(\pm\) 7.3 & 560 \(\pm\) 65 & 77.5 \(\pm\) 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effects of ultrasonic pretreatment on COD and TOC solubilization, depending on experimental variant. substances (EPS) dissolved in the aqueous phase [48]. Particle size reduction in pretreated biomass, resulting in an increase in the soluble COD concentration, plays a fundamental role in improving the biogas and methane production in AD [63].
# Compact Photonic-Crystals Based Isolator Using Ni-Zn Gyromagnetic Ferrite Posts
## 4 Conclusions  
In conclusion, a new compact PC isolator based on the Faraday rotation effect is demonstrated numerically by inserting Ni-Zn gyromagnetic ferrite posts into a T-typed SLPCs defect structure. As the radius of SLPCs\({}^{\prime}\) rods \(r_{0}\) takes 0.207\(A\), the optimal gap ratio of 32.7% achieves an ultra-wideband level, and the corresponding frequency range of the PBG is from 23.36 to 32.45 GHz. The optimal forward transmission efficiency and reverse isolation of the isolator reach 0.50 dB and 44.20 dB, respectively. With the unique reflecting cavity and built-in fan-shaped coupler, the structure of the designed isolator is more compact for integration. Our designed PCs isolator with high isolation and low insertion loss shows potential applications in future 5G communication systems.  
Conceptualization, D.Z., W.H. and H.I.; methodology, Y.W.; software, Y.W. and B.X.; validation, Y.W. and B.X.; formal analysis, Y.W.; investigation, Y.W., D.Z. and H.I.; resources, D.Z., W.H. and H.I.; data curation, Y.W. and B.X.; writing--original draft preparation, Y.W.; writing--review and editing, D.Z. and H.I.; visualization, Y.W.; supervision, H.I.; project administration, Y.W.; funding acquisition, H.I. All authors have read and agreed to the published version of the manuscript.  
H.I. thanks the support by the FDCT of Macau under Grant 0130/2019/A3 and by University of Macau under Grant MYRG2018-00088-IAPME.  
**Institutional Review Board Statement**: Not applicable.  
Figure 5: (**a**) The curves of isolation and insertion loss of the PC isolator with frequency; (**b**) the curves of the forward and backward reflected powers of the PC isolator with frequency.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge the Institute of Applied Physics and Materials Engineering of University of Macau and the College of Electronics and Information Technology of Shenzhen University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Therefore, it is essential to investigate the influence of different UHPC materials on steel tube UHPC. In this study, steel slag micro-powder UHPC is used as the infill material inside the steel tube, with the addition of a certain amount of steel fibers and coarse aggregates. A total of 27 short-column specimens were designed to investigate the mechanical properties and failure modes of the steel tube-steel slag micro-powder UHPC composite. The load-displacement and load-strain responses were analyzed, and a method for calculating the load-bearing capacity of the short columns was established. The objectives of this research are: (1) to examine the influence of using steel slag micro-powder UHPC on the performance of steel tube UHPC composite members; (2) to analyze the failure mechanisms and load-deformation characteristics of the short column specimens; and (3) to develop a calculation method for predicting the axial compressive load-bearing capacity of steel tube--steel slag micro-powder UHPC short columns. The findings of this study can provide a reference for the design and application of steel tube UHPC composite structures using steel slag-based UHPC as the infill material.
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
## 4 Simulation and Discussion
### Power Spectral Density  
Figure 5 shows the sub-band PSD performances of the PCC-DFT-s-OFDM at \(d=0,\;1,\;2,\;3\) for the \(\pi/2\)-BPSK, QPSK, and 16-QAM signals. The maximum power for each simulation was normalized to 0 dB to ensure a fair comparison. As \(d\) increased, the proposed method concentrated the in-band spectrum and diminished the out-of-band energy. Furthermore, the latter effect would be obvious if PA was considered. Although the PAPR performances of these three kinds of modulations were quite different, their extent of spectrum spreading due to nonlinear distortion was similar. This means that even for low-order/PAPR modulations in DFT-s-OFDM (i.e., \(\pi/2\)-BPSK and QPSK), there is still only limited improvement for PSD when PA nonlinearity is concerned. The advantage of lessening the out-of-band radiation can be underlined from the inter-user interference perspective. As seen in Figure 6a, the side-lobes of two spectrum-adjacent users severely influence the main-lobe of each other when \(d=0\). Figure 6b-d displays the mitigation of ICI for \(d=1,\;2,\;3\); as \(d\) increases, the main-beams of the two users increasingly separate and their PSDs overlap less; that is, lower interference plus noise ratio (SINR) is achieved. Such SINR improvement is especially significant when more users are considered.  
Figure 4: Complementary cumulative density function (CCDF) comparison of the peak-to-average power ratio (PAPR) at \(d=0,\;1,\;2,\;3\) for (**a**) phase shift keying (PSK) and (**b**) quadrature amplitude modulations (QAMs).  
Figure 5: _Cont._
# Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
## 2 Materials and Methods  
### Unclamped Inductive Switching (UIS) Circuit Test  
Figure 1 illustrates the unclamped inductive switching (UIS) test circuit. This circuit was used to assess the avalanche ruggedness of the device. As shown in Figure 2a, when the device under test (DUT) was turned on by a single gate pulse (\(t_{on}\)), the inductor current gradually increased, and the device temperature rose by joule-heating during a \(t_{on}\), as shown in Figure 2b. When the DUT turned off, the power MOSFET entered the avalanche mode in which the \(V_{DS}\) increased to a value close to the breakdown voltage, and the current flowed to the load devices to eliminate the inductor current. However, there was no load device in the UIS circuit and the inductor current was entirely terminated in the DUT. The electrons remaining in the p-base and the holes, which were the majority of carriers in the p-base region, underwent a recombination, and the current was gradually decreased. The blocking voltage increased until the recombination was completed, as shown in Figure 2c [10].
## 3 Interference of Extra Data Transfer Channel on Power Transfer
### Consideration for the Data Transfer Rate  
Since the proposed method transferring data when switch \(S\) is off, the data transmission rate can be determined by:  
\[d_{r}=d_{cr}\frac{t_{off}}{t_{on}+t_{off}} \tag{20}\]  
where \(d_{cr}\) represents the conventional data transfer rate, \(t_{off}\) is the off-state time while \(t_{on}\) is the on-state time in one operation period of the switch \(S\).  
So in order to increase the data transfer rate, we can either increase \(d_{cr}\) or \(t_{off}\). Increasing \(d_{cr}\) can be achieved by increasing the data carrier frequency. As for \(t_{off}\), it can be calculated by:  
\[t_{off}=-R_{L}C_{L}\ln\left(\frac{U_{l-req}-u_{t}}{U_{l-req}+u_{t}}\right). \tag{21}\]  
Equation (21) shows that the time interval of the off state can be increased when we reasonably increase the product of \(R_{L}\) and \(C_{L}\).
## 3 Results  
Table 1 displays descriptive statistics for CA, body composition, strength, flexibility, and balance tests and the results of the one-way between-group ANOVA. Significant statistical differences were observed for CA (\(p\leq 0.01\)). The EG was significantly older than the NEG-A and U23 group. In contrast, the NEG-B was considerably older than the NEG-A and U23 group. No significant statistical differences were observed between groups regarding body composition variables. Regarding the strength indicators, significant statistical differences were found for handgrip (F = 5.235, \(p\leq 0.01\)), CMJ (F = 10.320, \(p\leq 0.01\)), and SJ (F = 7.987, \(p\leq 0.01\)). The EG showed significantly better scores among all groups, followed by the NEG-A. In contrast, the NEG-B presented the lowest scores in the strength tests. Statistically significant differences were also found for the unilateral (right side: F = 4.324, \(p\leq 0.01\); left side: F = 4.324, \(p\leq 0.01\)) and bilateral (F = 4.059, \(p\leq 0.01\)) sit and reach tests, with EG players showing greater flexibility capacity comparing with their peers. Concerning balance, the EG showed a significantly better performance than the other groups in the OSI (F = 6.091, \(p\leq 0.01\)), APSI (F = 5.252, \(p\leq 0.01\)), and LMSI (F = 3.256, \(p\leq 0.05\)). Lower values of the balance platform oscillation indicated greater balance. The results of ANCOVA are shown in Table 2. After adjusting for CA, significant statistical differences were found for handgrip (F = 4.954, \(p\leq 0.01\), partial eta squared = 0.17), CMJ height (F = 9.977, \(p\leq 0.01\), partial eta squared = 0.29), and SJ height (F = 7.777, \(p\leq 0.01\), partial eta squared = 0.25), favoring the EG. Elite players also presented significantly better scores both for flexibility and balance. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{4}{c}{**Mean \(\pm\) Standard Deviation**} & \multicolumn{4}{c}{**ANOVA**} \\ \cline{2-7}  & **EG (\(\mathbf{w=22}\)**)** & **NEG-A (\(\mathbf{w=17}\))** & **NEG-B (\(\mathbf{w=18}\))** & **U23 (\(\mathbf{
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 3 Results and Discussion
### Trained Architecture
Precision is the ratio of true positives to all images classified as true and false positives. The recall factor (or sensitivity) is a proportion of actual positives that are correctly classified. In turn, the F1 score is interpreted as a harmonic mean of precision and recall scores. \[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4a}\] \[precision=\frac{TP}{TP+FP}\] (4b) \[recall=\frac{TP}{TP+FN}\] (4c) Figure 4.0  
The following rules were applied when calculating performance indicators:  
* TP (True Positive) = a sample classified as an anomaly that is truly an anomaly;
* FP (False Positive) = a sample classified as an anomaly, but in fact is from the normal class;
* FN (False Negative) = a sample classified as normal, but in fact is an anomaly;
* TN (True Negative) = a sample classified as normal that truly belongs to the normal class. The performance indicators for the best binary classifier are presented in Figure 4.1