esa-sceva/satcom-chunk-collection huggingface dataset:
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 3 System Model and Problem Formulation
### Data Amount Model  
Inspired by previous work [22], we find the data amount in bidding information is one significant factor for model accuracy, assuming that each data owner reports their data amount truthfully. In particular, when the total amount \(D\) of training data in the federated system is larger, the global model accuracy \(GA\) is higher. In addition, more training data \(d_{i}\) provided by each individual data owner \(i\) would produce a higher aggregated global model accuracy. Thus, to obtain a higher global model accuracy, the server tries to select the data owner set with both larger local data amount of data owners and a larger total data amount with a limited budget, \(B\). However, these two goals may not be achieved  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(d_{i}^{t}\) & The amount of data bid by data owner \(i\) at round \(t\) \\ \(x_{i}\) & Whether data owner \(i\) is selected at round \(t\) \\ \(b_{i}^{t}\) & Data owner \(i\)’s bid price at round \(t\) \\ \(B^{t}\) & Budget at round \(t\) \\ \(D^{t}\) & The maximum data amount that the server can select within the budget at round \(t\) \\ \(f_{i}^{t}\) & CPU frequency of data owner \(i\) at round \(t\) \\ \(C_{i}^{t}\) & The computing capability of data owner \(i\) at round \(t\) \\ \(GA^{t}\) & Global model accuracy at round \(t\) \\ \(LA_{i}^{t}\) & Local model accuracy for data owner \(i\) at round \(t\) \\ \(GA_{-i}^{t}\) & Global model accuracy after removing data owner \(i\) \\ \(S^{t}\) & The set of selected data owners at round \(t\) \\ \(Q^{t}\) & The maximum resource quality that the server can select within the budget at round \(t\) \\ \(rep_{i}^{t}\) & The direct reputation of data owner \(i\) at round \(t\) \\ \(Rep_{i}^{t}\) & The overall reputation of data owner \(i\) in previous rounds \(t\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key Notations.  
Figure 1: Framework of the proposed incentive mechanism QuoTa.  
simultaneously. The main idea in this paper is to choose data owners with as a large local data amount as possible while the total amount of training data reaches a given threshold. More details will be shown in Section 3.4.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)
#### 3.1.4 Feedback Focused  
This category includes studies that explicitly use audio-derived information to deliver feedback to educational stakeholders. Unlike works that remain at the descriptive or observational level, these studies aim to close the loop by translating insights into instructional guidance for teachers or learning support for students.  
A prominent line of work centers on teacher feedback. Refs. [38; 39] quantify teachers' uptake of student ideas, highlighting how conversational patterns can either reinforce or hinder student contributions. Similarly, [PERSON] et al. [40] investigates how competitive versus non-competitive response systems influence teacher behavior, providing reflective feedback that helps educators adjust their instructional strategies.  
Other studies focus more narrowly on improving questioning techniques. For example, [PERSON] et al. [41] analyzes teachers' focus questions and provides targeted feedback to enhance student engagement. In the same vein, [PERSON] et al. [42] examines how specific teacher interventions affect students' motivation, sense of identity, and classroom belonging. In line with this, [PERSON] et al. [43] examine teacher speech to enable self-reflection and support instructional improvement.  
On the student side, feedback mechanisms are used to guide learning progress. [PERSON] et al. [44] introduces an automated support system that helps students answer science questions, while [PERSON] et al. [45] proposes a predictive model to assess fluency and accuracy in language learning. These tools aim to replicate or augment teacher evaluations, providing learners with specific areas for improvement.  
What Are the Most Common Audio Features Used in Educational Studies, and How Are They Extracted? (RQ2)  
The studies examined employ a diverse array of audio features, which can be systematically grouped into three distinct groups: acoustic features, diarization-based features, and NLP-based features. These categories not only reflect the level of abstraction of the extracted information but also correspond to different analytical strategies employed across studies. To support interpretability, we provided a structured summary of representative features within each category, offering readers a reference point for the more technical descriptions that follow. To our knowledge, this synthesis constitutes the first structured mapping of audio features and their extraction methods within educational research contexts.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 6 Future Trends on Smart Urban Mobility  
Smart mobility solutions are catalyzing a paradigm shift in the area of mobility management in smart cities. Several innovative approaches are being devised to improve the state-of-the-art of smart mobility. A few important factors to be considered include:  
* Mobility in degraded vision
* Electric vehicles
* Alternate fuels
* Mobility solutions in natural calamities and disasters
* Mobility for differently abled citizens
* Inclusive, environment friendly, sustainable and efficient transportation
* IoT based dynamic traffic management
* Transparent and distributed traffic management
* Security of citizens, devices and vehicles.  
Artificial Intelligence is one of the pioneering technologies being exploited in recent years to provide state-of-the-art mobility solutions. Deep learning-based object detection and identification can be used to provide improved visibility in conditions of degraded vision (such as low light areas, foggy, windy or rainy environments) [150; 151; 152]. IoT-based road condition monitoring, environment monitoring, and surrounding monitoring can provide unprecedented information which can be used to predict future conditions to aid in the desired course of actions. Blockchain technology can provide a distributed,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Autonomous Vehicles** & **Optimization of Logistics** & **Mobility-as-a-Service** & **Traffic Flow Optimization** \\ \hline Blockchain & [139; 140; 141] & [137; 138] & [125; 126; 140] & [131] \\ \hline Geospatial technologies & [142; 143] & [133; 134] & [127; 128] & [133; 134] \\ \hline Big Data & [144; 145] & [134] & [127; 129] & [134] \\ \hline Clean Energy & [146] & [132] & [130] & [132] \\ \hline Artificial Intelligence & [147; 148; 149] & [133; 134] & [126] & [133; 134] \\ \hline IoT & [142; 145] & [135; 136] & [128] & [135; 136] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of studies that apply enabling technologies to main services and application of smart mobility.  
transparent, and immutable Vehicle-to-Everything (V2X) network for effective, secured and privacy preserved management of the smart mobility ecosystem [153; 154; 155; 156]. Technology for low carbon emissions, alternative fuels, electric vehicles and improved batteries can also aid in the protection of the environment and attaining sustainable development goals for better environment for a better quality of life (QoL). Extensive research was conducted in the field of communications to provide connectivity in times of natural calamity and disasters [157; 158]. Affordable satellite-based communication technologies are being explored to provide connectivity in extreme and unavoidable conditions.
## 3 Finite Element Simulation  
The finite element model is established in accordance with the actual condition of the bridge structure, which is the basis for analyzing the dynamic characteristics in the maximum cantilever state of this bridge. When establishing the finite element model, the elements are selected according to the following principles: (1) the selected element types must be able to mostly simulate the force characteristics of the structure; (2) it must be able to ensure the calculation results are accurate; (3) the finite element model should be simple and efficient. The calculation effort should be as simple as possible, and analysis of the results should be more convenient.  
Based on the finite element analysis software, ANSYS, a three-dimensional finite element model of the actual bridge is established, in which the BEAM4 element is used to simulate the bridge tower, the temporary piers, and the steel truss girder. The BEAM188 element is used to simulate the steel truss girder. Stayed cables are simulated by the spatial bar element, link10, and the elastic modulus of stayed cable is modified using the Ernst formula to satisfy the nonlinear feature of cables. The bridge deck is simulated by the SHELL63 element. The average densities of the cables, steel truss girder, and concrete pylon are calculated by the actual amount of steel and concrete used, and the quality of the bridge deck and other structural members are taken into consideration. The fixed hinge support is installed at the P2 pier, and the longitudinal movable hinge supports are arranged at other piers. The model is established in the order of main tower, main beam, cable, and auxiliary piers. In the construction stage, due to the cantilever assembly method, only the deadweight and temporary construction load are considered. After the whole bridge is built, giving the link10 attribute to the cable, the cable force amplitude modulation is carried out through deadweight. P2 piers are all fixed, and the remaining piers and abutments release UX. After the model is established, the model analysis is carried out. The material parameters used in the finite element model are listed in Table 1, the ansys bridge model is shown in Figure 4, and the cable details are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component Names**} & \multirow{2}{*}{**Poisson Ratio**} & **Elastic Modulus** & **Mass Density** & **Linear Expansion** \\  & & **(GPa)** & **(kg/m3)** & **Coefficient (10\({}^{-5}\)\({}^{\circ}\)C)** \\ \hline Pylon (C50 concrete) & 0.2 & 34.5 & 2.549 & 1.0 \\ Stayed cables & 0.3 & 195 & 8.005 & 1.2 \\ Q370 Steel & 0.3 & 206 & 7.85 & 1.2 \\ Rigid arms & 0.3 & 206,000 & 0.00 & 1.2 \\ Construction brackets & 0.3 & 206 & 7.85 & 1.2 \\ Supports & 0.3 & 100,000 & 0.00 & 1.2 \\ Longitudinal connection of beams & 0.3 & 54 & 7.85 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters.  
Figure 3: The overall drawing.
## 2 Data and Methods
### 3D Geological Modeling Scheme  
The primary approach of constructing the 3D geological structure model of Wuhan's MDA is to use the large-scale stratigraphic structure as the constraint framework to progressively constrain the sub-scale stratigraphic structure. Initially, an in-depth analysis of the geological evolution history of the study area is conducted to establish the fundamental geological framework, encompassing stratigraphic units and stratigraphic sequences. This framework serves as the initial constraint, upon which the variations within the stratigraphy of the foundational geology are analyzed to determine the stratigraphic units and sequences of secondary geological structures. This process culminates in the formulation of a standardized stratigraphy for 3D geological modeling in Wuhan's MDA. Using this as the basis of the modeling, through the integration of the drilling data, the profile data, and the stratigraphic maps, the modeling constraint data are obtained for each stratum. The specific modeling scheme is shown in Figure 3.  
Analyze the geological and tectonic evolution history of Wuhan, determine the basic geological framework of the study area, and use it as a constraint framework to determine the standard stratigraphy for 3D geological structure modeling of Wuhan's MDA;  
Figure 3: Scheme for 3D geological structure modeling of Wuhan’s MDA.  
Organize and analyze geological data of different specialties, types, and data structures (including drilling data, basic geological profile, hydrogeological profile, and geophysical exploration data, etc.), and obtain computer-recognizable modeling constraint data for each stratum in a uniform data format;  
Utilize standardized stratigraphic constraint data as modeling data, adopt GOCAD as the modeling platform, and employ the SGrid modeling scheme to conduct 3D geological structure modeling for Wuhan's MDA;  
According to the distribution density and unevenness of the boreholes, 10% of the boreholes are proportionally selected from the entire research area as model validation boreholes to participate in the model quality assessment, so as to check and optimize the 3D geological structure model.
## 3 Results and Discussion  
### Mechanical Strength  
Figure 2 shows the two and 28 days compressive strengths for the MA157 pastes prepared with 4, 6 and 8 M NaOH solutions. In the two days materials, the highest strength, at \(>\)25 MPa, was recorded for the pastes prepared with 6 M NaOH. The 28 days pastes exhibited strength of 32 MPa at 4 M NaOH, 33 MPa at 6 M NaOH and 36 MPa at 8 M NaOH. The increase in mechanical strength with time was attributed to the greater degree of reaction, although strength was also observed to depend on reaction product chemical, mineralogical and microstructural composition, as discussed in a later section.  
The compressive strength values recorded here were higher than reported by other authors, who worked with thermally treated alkali-activated illite/smectite clay with CaO contents of \(<\)5%. [PERSON] et al. [25] found compressive strength values of around 13 MPa with 6 M NaOH, (solution/solid ratio = 0.41, curing 20 h at 20 \({}^{\circ}\)C in sealed moulds avoiding drying out, after demouling; seven days at 60 \({}^{\circ}\)C in 100% relative humidity (RH), 3 h at 80 \({}^{\circ}\)C in 100% RH and drying over 24 h at 40 \({}^{\circ}\)C). [PERSON] et al. [26], using a potassium silicate solution, observed strength of 25 MPa (initial curing in an opened polystyrene sealed mould, 48 h at 70 \({}^{\circ}\)C, subsequently, the mould was closed into oven at 70 \({}^{\circ}\)C for 2 h, after demouling ambient air (25 \({}^{\circ}\)C, 40% RH) during 21 days). [PERSON] et al. [19] obtained values of \(<\)10 MPa with sodium silicate molar ratio of SiO\({}_{2}\)/Na\({}_{2}\)O = 0.31 (initial curing in wet conditions, seven days, 25 \({}^{\circ}\)C and 85% RH, followed by curing in dry conditions: Three days, 40 \({}^{\circ}\)C, 55% RH).  
The higher strength values found in this study were attributed to the high degree of clay reaction, the outcome of the initial mechanical-thermal treatment. As the benefits of that treatment, which induces phyllosilicate amorphism and carbonate decomposition, were described in detail in an earlier paper [21], they are not addressed hereunder. Another factor deemed to have contributed significantly to the good initial mechanical performance of this alkali-activated, ambient temperature-cured clay, was the presence of a fairly high (\(\sim\)30%) proportion of calcium. Like silicon and aluminium, calcium solubility is heavily dependent upon system alkalinity. Given that solution alkalinity played a substantial role in these complex systems (\(\sim\)43% SiO\({}_{2}\), 13% Al\({}_{2}\)O\({}_{3}\) and 30% CaO, see Table 1), it was defined as the primary object of the present study.  
Figure 2: Two and 28 days compressive strength in 4, 6 and 8 M NaOH-activated pastes (six specimens per test age and material were tested).
## 3 Results and Discussion  
### FTIR Spectra Evaluation  
Figure 3 shows an example of the FTIR spectra of the photoresist EPR-S1_b, while the Figure 4 present the main organic groups characterized. All of the other samples were analyzed separately and afterward plotted together (see Figure 5).  
There are eight specific important regions in the FTIR spectra (Figure 3). The region in 3475 cm\({}^{-1}\) is a vibration typically assigned to hydroxyl groups (OH\({}^{-}\)), and the region at 2930 cm\({}^{-1}\) confirms the presence of methyl, methylene, and aromatic hydrogens in the structure of the sample. The regions in 1798 and 1735 cm\({}^{-1}\) are assigned for carbonyl of ketones, acids, esters, and lactones groups. The regions of 1600 and 829 cm\({}^{-1}\) are assigned to alkenes, and the region in 1247 cm\({}^{-1}\) is assigned to the organic group ether. Figure 4 resumes the organic fractions present in the sample.  
The same profile in the FTIR spectra was observed in the other samples (Figure 5). There were only significant differences between the EPR-S1 and EPR-S2 photoresists, where the band for carbonyl (range of 1700-1800 cm\({}^{-1}\)) was less intense and a second band was observed in the case of the EPR-S1 series. Also, this second band was delocalized for the left side (smaller wavelength).  
The organic groups that characterize the polymer are present in all the samples of EPR-S1 or EPR-S2. However, other organic groups were also identified, indicating the presence of other compounds (e.g., solvents, catalysts, etc.). The assignment in 3475 cm\({}^{-1}\) (hydroxyl groups) can indicate the non-total reaction in the epoxidation step of the synthesis (see Section 3.3. MALDI-TOF Analysis).  
Figure 4: Organic groups identified in the FTIR spectra analysis.  
Figure 3: Fourier-transform infrared (FTIR) spectra of sample EPR-S1_b.
## 6 Conclusions  
In UWSNs, a long propagation delay and high energy consumption are important issues. To solve these problems, we propose a novel routing protocol for UWSNs called RRSS. In RRSS, the best next-hop is determined mainly according to receive signal strength. Sink nodes send beacon signal periodically, and a RSS vector is constructed from each underwater node to a sink node according to RSSB. The sender selects the node nearest to the RSS vector as the best next-hop combining the RSSB and RSSH of the neighbor node. To avoid data packets being delivered to the node in void area or with insufficient energy, a void-avoiding algorithm is designed and the energy threshold is preset.  
Figure 13: Performance comparison. (**a**) Delivery ratio vs. the number of nodes; (**b**) delay vs. the number of nodes; (**c**) energy consumption vs. the number of nodes.  
Simulation results show the RRSS can decrease delay and save energy consumption compared with DBR and VBF. However, there is still no fundamental solution to the problem of energy balance in UWSNs. In future studies, we need to work hard in this area.  
This work is supported by the CERNET Innovation Project (NGII20160307), National Natural Science Foundation Projects of China (61162003), Qinghai Office of Science and Technology (2015-ZJ-904), Key lab of IoT of Qinghai (2017-ZJ-Y21), and Hebei Engineering Technology Research Center for IOT Data acquisition and Processing.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Future Perspectives
### Wireless Sensor Networks  
Wireless sensor networks make it possible to reliably collect sensor data from a large number of point measurements, and are therefore a potential solution for extending the spatial range of point-based measurement techniques. For example, a multi-year field campaign in Val Ferret (VS) in the Swiss Alps uses such a network (Sensorscope) currently consisting of 25 stations distributed over the catchment, measuring relevant meteorological parameters such as rainfall; wind speed and direction; temperature; incoming shortwave radiation and surface temperature of the soil [120]. Soil moisture, soil matric potential, and soil temperatures are also measured at 21 of the stations, using decagon 5 TM and ECTM (soil moisture based on dielectric constant) sensors, and MPS1 (matric potential) sensors at 20 and 40 cm below the surface. Nevertheless, Because of the above-mentioned spatial variability of the soil moisture, these point measurements, although numerous and well distributed, can only give a limited picture of basin-wide soil moisture and its distribution. This is also an area where GNSS-R could provide an interesting solution when combined with an existing infrastructure that would provide the necessary data to calibrate the GNSS-R instrument, which in turn would provide measures in-between the _in situ_ infrastructure.
## 3 Post-Processing Methodologies
### Frequency-Domain Analysis
#### 3.2.2 Integrated-Bin Evaluation: Adaptive Spectral Band Integration (ASBI)  
Adaptive spectral band integration (ASBI) is a frequency-domain analysis technique, in which each pixel's negative phase contrast \(\Delta\varphi(i,j,f)\) is integrated over its optimal frequency range, i.e., \(\left[0\;\mathrm{Hz}\ldots f_{blind,2}^{(i,j)}\right]\)[19]:  
\[DI(i,j)=\sum_{f=0\;Hz}^{f_{blind,2}^{(i,j)}}\Delta\varphi(i,j,f)|_{\Delta \varphi(i,j,f)<0} \tag{4}\]  
ASBI's integrated area is schematically illustrated in Figure 4c on which the second blind (i.e., zero-crossing) frequency \(f_{blind,2}\) is indicated. With this integration procedure, the phase response over a wide range of frequencies is fused in order to get a balance between defect detectability and reduced effects of lateral heat diffusion. This is also related to the typically skewed distribution of the phase contrast curve, in which the higher-frequency range \(\left[f_{\Delta\varphi,max}^{(i,j)}\ldots f_{blind,2}^{(i,j)}\right]\) (i.e., less effects of lateral heat diffusion) contributes more to the ASI's damage index than the lower-frequency range \(\left[0\;\mathrm{Hz}\ldots f_{\Delta\varphi,max}\right]\)[19]. Due to the high dynamic range, the obtained ASI surface maps are represented in a logarithmic scale (after inverting the sign of the damage index values) for improved defect visibility. The specific choice of the integration limits, i.e., \(0\;\mathrm{Hz}\) and \(f_{blind,2}\), also has the advantage that the procedure can be fully automated without the need of user input or parameter selection, in contrast to TSA where the user must manually select the integration limits \(t_{1}\) and \(t_{2}\). ASBI is an integrated-bin evaluation technique in the frequency domain and yields a unique damage index map. Coupled with simulations, ASBI can also be used for quantitative depth inversion [19].