esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
The data analysis for both the UEQ+ and SUS questionnaires was performed using available templates. For the UEQ+, this includes the calculation of the KPI of each individual component as well as the overall system KPI. The KPI for the UEQ+ are calculated similarly to the KPI for the UEQ questionnaire, from which it is derived. This calculation is described in [45]. The individual \(KPI_{j}\) for each participant \(j\) can be calculated as follows:  
\[KPI_{j}=(\sum_{i=1}^{3}M_{i,j}*I_{\frac{t\alpha}{i},j})-4. \tag{1}\]  
Here, \(i\) corresponds to the UEQ+ parameter of the specific component, namely the robot-assisted assessment, the health devices and the app. \(M\) is the mean of each UX scale per participant, i.e., the perspicuity scale in this study, while \(I_{\frac{t\alpha}{i},j}\) refers to the relative importance of the specific characteristic of this scale. This number is then subtracted by 4 to normalize the 7-point Likert scale around zero, resulting in values between \(-3\) and 3. The overall system KPI can then be calculated as the mean of all individual participant KPI using the following equation, with \(n\) being the number of all participants:  
\[KPI=\frac{1}{n}\sum_{j=1}^{n}KPI_{j}. \tag{2}\]  
For the scoring of the SUS, we use the following equation, as described in [30], where \(X\) represents the sum of the points for all odd-numbered questions subtracted by 5, while \(Y\) is calculated by substracting the sum of the points for all even-numbered questions from 25:  
\[SUS\;Score=(X+Y)*2.5. \tag{3}\]  
In addition to the UEQ+ and SUS, we evaluated the success and completion rates of the health assessments. We define the success rate as the rate at which both [PERSON]'s speech recognition and the Rasa dialogue manager correctly identify the user's intent. The completion rate is the rate at which the entire health assessment, including all questions, is completed. To determine both the success and completion rates, we compared the Rasa conversation logs with the transcripts we created from the audio recordings of the robot interactions.
# Networked Roadside Perception Units for Autonomous Driving
## 6 Conclusion and Future Work  
In this paper, we discussed AutoC2X, an open-source software we developed to enable both vehicle-based infrastructure-based cooperative perception. We also described the field experiments conducted on the AutoC2X-based RSPU system proposed in the study. Experimental results showed that, even in the worst case, the delay in receipt of cooperative perception message was only 100 ms with Wi-Fi. The proposed system is independent of the access layer technologies, and we believe that the latency will be better using C-V2X or 5G. To realize broader cooperative perception, we proposed and discussed the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network. To balance the trade-off between wider message dissemination, delivery delay, and PDR, we proposed an algorithm that provides higher priority to cooperative perception messages corresponding to areas closer to the receivers. The evaluation results demonstrated that our proposed scheme successfully widen the perception range and delivers messages with a high delivery ratio and low latency, especially under massive road traffic.  
In the future, we plan to work on the following: First, further development of AutoC2X is necessary to feedback the V2X information to autonomous driving. For example, prediction and planning must take advantage of cooperative perception. Second, the proposed networked RSPUs can benefit from a CPM generation rule [11] that mitigates latency and packet loss. Further research is necessary to verify the applicability of the technique to networked RSPUs. Third, scenarios with multiple sensors and multiple transmitters require further investigation with regard to both the RSPU system and networked RSPUs.  
Conceptualization, M.T., T.O., and M.K.; methodology, M.T., T.O., and M.K.; software, T.O. and M.K.; validation, M.T., T.O., and M.K.; formal analysis, M.K.; investigation, M.T., T.O., and M.K.; data curation, T.O. and M.K.; writing-original draft preparation, M.T.; writing-review and editing, M.T.; visualization, M.T., T.O., and M.K.; supervision, M.T. and H.E.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Evaluation of delays. (**a**) Tokyo scenarios, (**b**) Paris scenario.  
**Funding:** This work was partly supported by JSPS KAKENHI (grant number: JP17H04678).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Local Energy Markets
These data can be used for billing, monitoring energy usage trends, and optimizing energy systems. The account database records the information of accounts. An account database is a structured collection of data that stores information related to user accounts. Auctioneers are the team players in the LEM who enter competitive bids simultaneously for selling and buying the energy. Aggregators are intermediary entities that provide an opportunity for prosumers to access the market and facilities of the intermediary's entities. The intermediary's entities collect the energy from multiple producers to facilitate energy transactions within the LEC, which provide services such as metering, billing, and settlement. In the LEM, energy market regulators ensure the integrity and stability of the energy market. They may include government agencies, utilities, or independent regulatory bodies, and develop policies and regulations that promote fair competition, protect consumer rights, and encourage the adoption of DERs. Table 1 summarizes the market players' classifications and relevant references in LEM. Figure 5: Interactions among the LEM entities. Although LEMs are emerging as a promising solution for managing decentralized energy resources and promoting renewable energy adoption, they face several challenges. These include planning and regulatory barriers, technical complexities, scalability issues, market fragmentation, and economic constraints. Planning for an LEM involves addressing participant preferences, goals, procedures, policies, and implementation strategies. Regulatory challenges may include complex licensing and permitting requirements, which can hinder small-scale producers from participating. Technical barriers cover aspects such as design, installation, local resource availability, infrastructure, interoperability, cybersecurity, data privacy, and the skills needed for operation and maintenance. Additionally, the small-scale nature of LEMs can lead to market fragmentation, making it difficult to achieve economies of scale and liquidity.
## 3 Results and Discussion
### Removal of Colour  
AL waste has a dark brown colour, so its decolourization is important to avoid negative environmental and visual effects. Highly coloured wastewater reduces the passage of light through the water, causing a reduction in photosynthetic activity and, therefore, altering the flora and fauna of the water [38]. The colour index profile is shown in Figure 5. Greater removals were obtained when the fungus was added.  
In the non-inoculate experiments (C1 and C2), the colour index was also reduced by endogenous microflora, especially during the first 24 h. Afterwards, the C1 slightly changed up and down, reaching final percentages of removal around 20%. When the fungus was inoculated (E1 and E2) the colour index decreased more abruptly especially during the first 24 h. Removals around 70% were obtained in both cases after 4 days. Then, the colour removal slightly increased and remained approximately stable, finally reaching a 60% reduction. The obtained results were in accordance with those found in the literature regarding the colour removals in recalcitrant wastewaters using white-rot fungi. [PERSON] and [PERSON] [39] reported colour degradations of 64% after continuous fungal treatment with _P. chrysosporium_ of industrial textile wastewaters. [PERSON] et al. [25] reported colour removals around 60-65% in OMW using basidi  
Figure 4: Changes in total phenolic content for non-inoculated tests C1 () and C2 (), and inoculated tests E1 () and E2 (). The standard deviation (SD) of the experimental data were in all cases less than 4% of mean value.  
Figure 5: Changes in colour index during biological treatment. The dashed lines shown the non-inoculated tests C1 () and C2 (), used as controls. The solid lines shown the inoculated tests E1 () and E2 (). The standard deviation (SD) of the experimental data were in all cases less than 11.5% of mean value.  
spp. Similar reductions were reported for olive mill effluent treated by an adapted strain of _Trametes versicolor_[40].  
Taking into account data reported by the literature and results obtained in this work, the use of white-rot fungi to treat AL could be considered to be a promising treatment technology. Although the removal efficiencies of sCOD, colour and phenolic compounds after treatment with fungi were slightly lower than those reported for OMW treated with physical-chemical treatments, it should be considered that these processes usually present serious drawbacks such as their high cost, bad odour, addition of chemicals, or fouling of the membrane [4,18]. Additionally, chemical oxidation treatments can produce more recalcitrant or toxic intermediate compounds, reducing the effectiveness of the treatment. In contrast, the use of white-rot fungus allows the degradation of a wide range of recalcitrant contaminants due to its ability to release extracellular enzymes, as well as lead to the detoxification of wastewater [25].
## 4 Discussion and Conclusions
### Limitations  
The motivation to perform invasive FFR was clinical, which resulted in a large proportion of anatomically borderline lesions in a population with extensive atherosclerotic disease. No cases were excluded, and the results should be interpreted with the consideration that this was a retrospective single-center study.  
The anatomical data that was used as input to the DL model may not always accurately reflect the true luminal geometry due to limitations of the OCT acquisition itself (heart motion during automatic pullback, sub-optimal calibration), and small errors that are introduced by the linear interpolation of radius values for the rejected contours. Furthermore, by using the effective radius information as input, we neglect the actual three-dimensional shape of the coronary lumen. The literature suggests that this has a small impact [78], but in certain samples, with non-circular lumen geometry, e.g., concave shape, the impact may not be negligible.  
Moreover, the manual editing steps limit the real-time capabilities of the algorithm and introduce intra- and inter-observer variability.  
While the subgroup analyses indicate that the length of the considered segment does not influence the results, the maximal length of 7.5 cm may represent a limitation in the case of serial stenoses. For example, if lesions are present in the proximal and distal segment of a vessel, a processed vessel length that is larger than the limit of 7.5 cm would be required to accurately predict FFR.  
Finally, to validate our findings and to provide more representative results, the proposed method requires further validation in larger, prospective studies, that are conducted at multiple clinical sites.
## 4 Applications of ML-Based Routing Algorithms in WSNs
### Intelligent Transportation Systems  
The Internet of Vehicle (IoV) is a typical application in the field of transportation systems, which can effectively improve traffic efficiency and improve traffic safety. The application of wireless multimedia sensor network (WMSN) brings rich sensing and driving capabilities to the sensing layer of IoV. The complex task processing and frequent data communication in IoV need to ensure the effective distribution of energy while ensuring QoS, which is a challenge for heterogeneous WMSN with uneven energy distribution.  
Based on RL, an energy-saving distributed adaptive cooperative routing (EDACR) was proposed [78]. Compared to other ML algorithms, the RL algorithm is lighter, has low requirements on computing resources, and can provide the high flexibility and adaptability for the dynamic topology of heterogeneous WMSNs. In EDACR, RL is used to maintain the routing table, and some suitable relay nodes instead of all nodes are selected to maintain the routing table to achieve energy-efficient distribution. The nodes adaptively choose whether to obtain the knowledge of expected performances of reliability and delay that could be provided by the candidate relay nodes through by lightweight RL according to their remaining energy. EDACR combines collaborative routing and RL to reduce system energy consumption while ensuring QoS.  
To guarantee the real-time transmission in IoV, a routing algorithm based on collaborative learning automata was proposed [79]. The learning automata (LA) located at the nearest access point (AP) learns from past experience and quickly makes routing decisions. The vehicle density as well as the distance and delay between the AP and the nearest service unit are input as parameters to cooperate with LA to select an optimized path. Vehicles communicate with each other in a collaborative manner to share information about these variables and intelligently choose the best route. Experimental results show that the routing algorithm can quickly plan the optimal path and effectively reduce the delay and energy consumption.  
Considering the energy transfer problem in the vehicle energy network (VEN) organized by renewable energy, charging stations, and electric vehicles, the literature [80] takes the joint optimization of renewable energy routing and dynamic storage allocation as the goal and studies a VEN with time-varying point-to-point traffic flow and adjustable energy storage capacity at stations. Based on linear programming, the joint energy storage capacity and routing planning method are explored. The long and short-term memory model is used to predict traffic patterns, and RL is used to iteratively improve the prediction accuracy. Experiments verify the effectiveness of the proposed method in optimizing routing and reducing energy consumption.
# Seismic Design and Ductility Evaluation of Thin-Walled Stiffened Steel Square Box Columns
## 3 Finite Element Analysis
### Loading Description  
Strength and ductility analysis of bridge piers typically involves evaluating their performance under various loading conditions, including axial, monotonic, and cyclic loads. Axial loading applies a force along the axis of the bridge pier, simulating dead loads, live loads, and other service loads within the bridge [54]. Monotonic lateral loading involves the gradual application of force or displacement in a single lateral direction without reversal.  
Cyclic lateral loading, which represents the horizontal component of seismic excitation, can be either force-controlled or displacement-controlled. This loading involves increasing the lateral load or displacement by integer multiples of the initial yield load or displacement in alternating directions. Experimentally, cyclic loading is commonly applied using a push-pull hydraulic jack [39]. In this study, a unidirectional displacement-controlled cyclic loading protocol is used, as illustrated in Figure 2d. The solid line represents one-cycle loading (\(N=1\)), while the dashed line denotes three-cycle loading (\(N=3\)). During the loading history, a quasi-static cyclic lateral displacement (\(\delta\)) is applied to the top of the column, accompanied by a constant axial force (\(P\)), see Figure 2a. The amplitude of the cyclic displacement is incrementally increased in multiples of the yield displacement (\(\delta_{y}\)), which is defined by Equation (7).  
\[\delta_{y}=\frac{H_{y}h^{3}}{3 EI} \tag{7}\]  
\[H_{y}=\left(\sigma_{y}-\frac{P}{A}\right)\frac{s}{h} \tag{8}\]  
Equation (8) is used to calculate the lateral yield load (\(H_{y}\)), where \(A\), \(h\), \(E\), \(I\), and \(S\) represent the cross-sectional area, height, Young's modulus, the moment of inertia of the cross-section, and the elastic section modulus of the column, respectively [6; 44].
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 2 Magnetorheological Damper
### Mathematical Model of the MR Damper  
According to the theory used for the gap flow between two parallel plates, the calculation formula of the damping force can be expressed as:  
\[F=F_{\eta}+F_{\tau}\text{sgn}(x)+F_{f} \tag{1}\]  
where \(F\) is the output damping force of MR damper; \(F_{\eta}\) is the viscous damping force without magnetic field; \(F_{\tau}\) is the coulomb damping force; \(F_{f}\) is the frictional damping force. Since there is sliding friction between the piston and the cylinder in the damper, the friction coefficient is very small, and the friction force between the two is much smaller than the viscous damping force and the coulomb damping force. Thus, the influence of this friction force is ignored in theoretical calculation and simulation analysis.  
Therein, \(F_{\eta}\) and \(F_{\tau}\) can be expressed by:  
\[F_{\eta}=\frac{6\eta A_{p}^{2}v}{\pi g^{3}}\cdot\left(\frac{L_{1}}{r_{i}}+\frac {L_{1}}{r_{m}}+\frac{L_{1}+L_{2}+t_{a}/2+t_{b}}{r_{e}}\right) \tag{2}\]  
\[F_{\tau}=\frac{cA_{p}}{g}\cdot\left(L_{1}\left(\tau_{i}+\tau_{m}+\tau_{f} \right)+L_{2}\tau_{s}\right) \tag{3}\]  
where \(\eta\) is the viscosity of the MR fluid without magnetic field; \(v\) is the velocity of the piston; \(g\) is thickness of the flow gap; \(c\) is the correction factor, with a value of 2-3; \(r_{i}\), \(r_{m}\), \(r_{e}\) are the radius of inner, middle and outer gap, respectively; \(L_{1}\), \(L_{2}\) is the effective length  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value/mm** \\ \hline Inner radius of piston rod & \(r_{o}\) & 7 \\ External radius of piston rod & \(r_{e}\) & 10 \\ External radius of core & \(r_{e}\) & 13 \\ Radius of piston & \(r_{p}\) & 31 \\ Inner radius of inner gap & \(r_{i}\) & 19 \\ Length of first outer gap & \(L_{1}\) & 8 \\ Length of second outer gap & \(L_{2}\) & 7 \\ Thickness of gap & \(g\) & 1 \\ Length of nonmagnetic ring & \(t_{a}\) & 13 \\ Thickness of nonmagnetic disk & \(t_{b}\) & 3 \\ Thickness of ring & \(t_{c}\) & 3 \\ Thickness of sleeve & \(t_{d}\) & 3 \\ Height of exciting coil & \(H_{c}\) & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main structural dimensions of the proposed initial MR damper.  
Figure 2: Piston assembly diagram of the MR damper.  
of the flow gap; \(\tau_{i}\), \(\tau_{m}\), \(\tau_{f}\), \(\tau_{s}\) are the shear yield strength of the MR fluid in different flow gaps, respectively, which are related to the magnetic flux density; \(A_{p}\) is the effective area of the piston, which can be expressed as:  
\[A_{p}=\pi r_{p}^{2}-\pi\Big{[}(r_{i}+g)^{2}-r_{i}^{2}\Big{]}-\pi r_{e}^{2} \tag{4}\]  
The dynamic range of the MR damper can be expressed by the ratio (\(D\)) of the output damping force to the viscosity damping force when the friction force is not considered:  
\[D=\frac{F_{\eta}+F_{\tau}}{F_{\eta}} \tag{5}\]
## 2 Materials and Methods
### Selection of Sampling Sites  
The selection of sampling sites is a critical stage of any pollution impact assessment. For this purpose, two sites belonging to the Gulf of Gabes were selected, with different degrees of contamination (Figure 1):  
* Ghannouch (respective geographical coordinates, 33\({}^{\circ}\)55\({}^{\prime}\) N and 10\({}^{\circ}\)5\({}^{\prime}\) E) is the area considered the most polluted due to the establishment of the chemical group, suggesting that the marine ecosystem and therefore the fauna and flora are threatened by liquid, solid, and gaseous industrial discharges [20].
* Sidi Salem (Djerba), the island of Djerba, is characterized by seasonal mass tourism, and the main sources of pollution are still anthropogenic waste.  
Twenty adult specimens (n = 20) were collected from each area in November 2021. They were captured with the aid of neighborhood fishers around ten kilometers south of Gabes and around 20 km north of Djerba at a depth of roughly 1 m, which is the typical depth of a crab burrow. The live crabs were transported to the laboratory in buckets filled with aerated seawater as quickly as possible. Once the cephalothorax was opened on ice, the digestive glands and gills were quickly removed. The organs were dried and powdered for metal analysis, frozen at \(-\)20 \({}^{\circ}\)C, ground in a mortar with liquid N2, and stored at \(-\)80 \({}^{\circ}\)C until biochemical testing. Three pools were prepared from each sampling station for each collected tissue.
## 2 Materials and Methods
### Sequence Data Processing  
Paired-end read quality was assessed using FastQC (version 0.11.7) [36]. Poor-quality bases were trimmed and adapters were removed using Trimmomatic (version 0.36) [37]. Retained reads were aligned to a human reference genome (Ensembl/Genome Reference Consortium Human Build 38, GRCh38) using Hisat2 (version 2.1.0) [38]. FeatureCounts (version 1.5.0) was used to count the fragments mapped to an exon/gene [39]. Differentially expressed genes (DEGs; adjusted \(p<0.05\)) between different groups were determined using an R-package DESeq2 (version 1.30.0) [40]. One sample of the LA-treated group was excluded from the study as its differential gene expression results did not correlate with the other samples.