esa-sceva/satcom-chunk-collection huggingface dataset:
# Catalytic Advanced Oxidation Processes for Sulfamethoxazole Degradation
## 3 Results
### Replicated and Cyclic Experiments  
Duplicate tests and cyclic experiments were performed for the photocatalytic ozonation process, and the results are shown in Figures 6 and 7, respectively.  
Three repetitions of photocatalytic ozonation with H\({}_{2}\)O\({}_{2}\) in the presence of CNT were performed following the procedure described in the methodology. The replicated tests demonstrated that the process is reproducible in terms of SMX removal and the small differences observed in the early stages are negligible.  
CNT, Fe/CNT, and TiO\({}_{2}\)/CNT catalysts were selected for cyclic experiments of photocatalytic ozonation. Three cycles were performed, and after each cycle, the final solution was filtered, and the retained catalyst was dried for 24 h at 100 \({}^{\circ}\)C. No significant differences were observed in terms of SMX removal as can be seen in Figure 7a,c,e, whereas some differences were observed in terms of TOC removal as shown in Figure 7b,d,f. In the case of CNT, a significant decrease was observed between the first and second cycle, and this decline may be due to the introduction of oxygenated groups on the CNT surface promoted by O\({}_{3}\) and/or H\({}_{2}\)O\({}_{2}\)[2; 4; 7]. Oxygenated groups reduce the electron density on the CNT surface, decreasing O\({}_{3}\) decomposition in oxidizing radicals and the degradation of organic pollutants becomes less efficient [12; 14]. For Fe/CNT and TiO\({}_{2}\)/CNT, the differences in TOC removal are less significant over the three cycles which can be explained by the great stability of these materials. In the case of Fe/CNT, the stability of CNT is increased by the formation of Fe-O-C bonds during calcination of the catalyst [39]. The TiO\({}_{2}\)/CNT composite does not suffer significant surface changes because it has a small content of CNT (15%) and TiO\({}_{2}\) is highly stable.  
Figure 6: Dimensionless SMX concentration during reproducibility runs of photocatalytic ozonation with H\({}_{2}\)O\({}_{2}\) (L/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\)) in the presence of CNT.  
AOPs involve very complex reactions. Although photocatalytic ozonation with CNT presents 93% of mineralization, its final solution had high toxicity (approximately 40% of inhibition effect). On the other hand, catalytic ozonation with H\({}_{2}\)O\({}_{2}\) and Fe/CNT achieved just 76% of TOC removal, a low level if compared with other reactions, even though, it also achieved low toxicity (8% of inhibition effect). This can be explained by different reaction mechanisms that may occur, leading to the formation of other non-detected products. These intermediates may have a small amount of carbon having a low contribution in TOC quantification; however, they can be very toxic.  
Figure 8: Light emission inhibition of _Vibrio fischeri_ bacteria when submitted to samples resulting from the treatment (after 180 min) of SMX by photocatalytic ozonation (**a**), catalytic ozonation with H\({}_{2}\)O\({}_{2}\) (**b**), and photocatalytic ozonation with H\({}_{2}\)O\({}_{2}\) (**c**).
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 3 Test Results and Discussion
### Design Recommendations  
In this section, based on the analysis of the experimental results, the following recommendations on practical seismic design pertaining to the precast concrete members connected by the grouted splice sleeve are proposed:  
1. Stirrups should be configured in the range of the sleeve height to improve the compression capacity of concrete. Furthermore, the sleeve shall be further optimized in terms of configuration, in order to prevent the grouting material from clogging and to ensure that the installation of the sleeve has no effect on the fixing of reinforcement.
2. The interface between the column body and pedestal, including the side, top, and bottom surfaces of the prefabricated members can be roughened, and the minimum exposed height of the coarse aggregate should be specified. Moreover, the preset slot with the thickness of 20 mm between the column body and pedestal should be sealed by high-strength grouting material, which can effectively prevent the grout from leaking out as well as improve the load-carrying capability.
3. Grouting material should possess high strength, high liquidity, and long initial setting time. However, the grouting material with high strength may lead to low plasticity. It is recommended to produce the modified grouting material with more significant performance according to the requirements of the corresponding regulations, on the premise that the compressive strength can attain 100 MPa within 7 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Yield Displacement** & **Ultimate Displacement** & **Displacement Ductility** \\  & \(\Delta_{y}\) **(mm)** & \(\Delta_{u}\) **(mm)** & **Coefficient \(\mu_{\Delta}\)** \\ \hline
1\# (cast in situ) & 6.97 & 21.48 & 3.08 \\
2\# & 2.59 & 8.95 & 3.46 \\
3\# & 2.87 & 16.53 & 5.76 \\
5\# & 2.67 & 8.65 & 3.24 \\
6\# (cast in situ) & 3.35 & 11.79 & 3.52 \\
7\# & 2.09 & 10.26 & 4.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Displacement ductility coefficient of each specimen.
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 4 Study Case
### Actual Working Conditions on Site  
According to the actual feedback from the site, the Maoping mine has experienced several smaller ground pressure (or rock burst) events at the top of the mining working face roadway. When subjected to high stresses at depth, whether inherent or caused by mining activity, rock fractures can occur within the support structure. This can result in a reduced bearing capacity of the rock body and detachment of supporting structures, such as pipe slit anchors, from the fractured rock mass. The prolonged action leads to the damage of the 433 m quasi-slope path of the I-8# orebody belonging to this type, as shown in Figure 9. With an increase in mining depth and the superposition of deep high-pressure water, this characteristic of deep fractured rock bodies becomes more prominent.  
Figure 8: Focused analysis area model diagram: (**a**) top view; (**b**) north view. The shading in (**a**) indicates the analysis area, the different colors in (**b**) indicate different analysis areas.  
According to the early warning method, 28 sets of data were counted with 8 h as the step time, and the ratio of the accumulated microseismic events to the accumulated energy and the time period before and after the statistical step time are shown in the table. It can be seen that there are four sets of data simultaneously meeting the early warning index of ground pressure risk, accounting for 14% of all data, especially within 5 days before the rock burst phenomenon. The data meeting the early warning condition are concentrated, comprising 75% of the total early warning data, indicating that this time period has a higher ground pressure risk than other time periods. This is consistent with the predictions of the fractal dimension and spatial distribution pattern analysis.
## 5 Results and Analysis
### Consumption Characteristics of the Household Application  
The investigated household was a two-bedroom domestic house which is a typical UK family home [31]. The activity schedules were recorded over one year as illustrated in Figure 7. Each diagram demonstrates both weekday and weekend scenarios in each season as Figure 7a-d shown.  
Similar activity patterns are discovered according to the four diagrams. These weekday activities can be generally divided into three patterns, including a morning section, a mid-day section and an evening section. On a spring weekday, the three sections start from 7:00, 13:00 and 16:30 respectively as shown in Figure 7a. From 7:00, the occupants carry on different activities. The morning section starts at 5:30 on a summer weekday (Figure 7b) which is earlier than other seasons. The irregular polygon in morning section shows that the occupant number varies obviously in morning section because that family members have different daily schedules on the weekdays. The autumn day (Figure 7c) has a quite similar pattern to the spring day except that each section in autumn starts earlier than that of the spring day. For example, the morning section in autumn starts from 6.30 which is half hour earlier than spring. The midday section in winter (Figure 7d) starts slightly earlier than that in other seasons since occupants might come back home earlier in winter due to cold weather. Winter weekdays have the most occupancy hours (11:00 to 15:00). The weekends can be divided two sections, day and night sections. Weekend activities in four seasons are almost the same except that the spring night section ends earlier than other seasons. These discrepancies give rise to the variations of energy consumption.
###### Abstract  
Green hydrogen, produced via electrolysis using renewable energy, is a zero-emission fuel essential for the global transition to sustainable energy systems. Optimizing hydrogen production requires a detailed understanding of bubble dynamics at the cathode, which involves three key stages: nucleation, growth, and detachment. In this study, hydrogen bubble growth was investigated in a custom-built electrolysis cell with microelectrodes, combining high-speed imaging and electrochemical measurements with a potentiostat. The results reveal distinct growth regimes governed by a potential-dependent time exponent, captured through a power law. Within the evaluated range of potentials, three regions with different bubble departure behaviors were identified: _(i)_ at low potentials (2.0-2.6 V), bubbles depart without coalescing, _(ii)_ in the transitional region (2.6-3.2 V), bubbles coalesce to varying degrees before detachment, and _(iii)_ at high potentials (\(\geq\)3.2 V), large, coalesced bubbles dominate. These findings highlight the significant impact of coalescence on bubble growth and departure behavior, affecting electrode coverage with gas and, consequently, electrolysis efficiency. Understanding these interactions is crucial for improving hydrogen evolution efficiency by mitigating bubble-induced mass transport limitations. The findings contribute to advancing electrolysis performance, offering insights into optimizing operating conditions for enhanced hydrogen production.  
hydrogen; bubbles; nucleation; bubble growth; platinum; microelectrode; electrolysis Article
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 3 Delay-Tolerant Protocols Enhanced via Age of Information
### Age of Information Enhanced Routing  
In this work, a variant of the FRESH protocol is proposed, as explained in detail. More specifically, it is obvious that during the implementation of this routing algorithm, the so-called \"age\" of the packet is not taken into account at all, i.e., the time interval from the moment of its production to the time of observation. Depending on the choice of various system parameters (neighborhood radius, number of nodes in the DTN network, initial ring formation radius when delivering packets, etc.), system packets are likely to circulate indefinitely in the network, wasting available resources, as well as gradually increasing their age. Nevertheless, in the real world of DTNs, it makes sense for packets to have a specific Time-To-Live (TTL) available, which acts indirectly as an expiration date. Therefore, if a packet exceeds the expiration date value without reaching the destination, then it is considered useless and should be dropped from the network. Furthermore, this is necessary in time-sensitive applications.  
This idea is promoted in this work, where we incorporate in the FRESH protocol the concept of AoI, in order to impose a specific upper bound for the age of each packet. Thus, in the proposed modified protocol, the routing algorithm is similar to the one explained above. However, at the end of each time slot, the age of each packet remaining in the network increases by one time unit, and if the age of a packet exceeds the value of a specific threshold for AoI, the packet is dropped from the network. At first glance, with the modified FRESH protocol, one does not expect an increase in throughput, namely the ratio of successfully transmitted packets to the total number of packets generated in the network, as the proposed modified protocol does not differ substantially from the original FRESH protocol's main function. Any differentiation of the throughput could be observed in more complex cases, such as in the case of specific buffer space constraints for each node. Here, for simplicity, it has been assumed that each node has an infinite buffer. The expected difference is in the age of the packets and is reflected in metrics, such as the average age of the packets, the maximum observed age, etc. Eventually, it is expected to find a tradeoff between the throughput and the average age of the packets.
# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 4 Methodology
### Crowdsensing Model
#### 4.1.2 VCG-Based Auction Model for Truthful Reporting  
The VCG-based user-centric approach is defined in Section 5.1 in the context of the proposed MCS platform. In VCG auction, there are multiple items and agents, with each agent placing a bid for each item without knowing the valuations of others. The auction system assigns the items according to their true valuations, while each agent pays only the marginal value when compared to the other agents, encouraging truthful bidding.  
The Vickrey-Clarke-Groves mechanism [112; 113] is defined by the following optimization problem (Equation (14)) that maximizes the total utility, based on the optimal allocation of items to agents:  
\[X(\hat{\sigma})=arg\max_{x\in X}\sum_{i=1}^{n}\hat{\sigma}_{i}(x) \tag{14}\]  
where \(\hat{\sigma}_{i}:X\to R_{+}\) represents the valuation (bid) of agent \(i\) with regards to each possible outcome, i.e., \(x\in X\).  
The payment function for agent \(i\) depends on the valuations of other agents for a given outcome, i.e., \(x\in X(\hat{\sigma})\), with the agent \(i\) and \(x\in X(\hat{\sigma}_{-i})\), without the agent \(i\), as defined in Equation (15):  
\[p_{i}(\hat{\sigma})=\sum_{j\
eq i}\hat{\sigma}_{j}(X(\hat{\sigma}_{-i}))\ -\ \sum_{j\
eq i}\hat{\sigma}_{j}(X(\hat{\sigma})) \tag{15}\]  
The mechanism promotes truthful bidding through incentive compatibility (truthful bidding is a dominant strategy), individual rationality (each truthful agent receives a payment) and efficiency (truthful bidding maximizes social welfare).
## 8 Conclusions  
In conclusion, ultrabroadband coherent MIR spectrum which covers the entire MIR region was generated through two-color filamentation. The spectrum spread from 400 to 5500 cm\({}^{-1}\), which corresponds to more than 3.5 octave with full width at tenth maximum. The duration of the MIR pulses was measured as short as 6.9 fs with XFROG, which corresponds to nearly half cycles of 3.3 \(\mu\)m center wavelength. The generated pulse contained frequency components as low as \(\sim\)250 cm\({}^{-1}\), which was observed by using electro-optic sampling technique.  
The inline configuration scheme allowed us to generate MIR pulses with higher spatial symmetry and higher stability than those from the previous scheme [22]. The instability of the CEP was estimated as 154 mrad rms in 2.5 h.  
We also found that the efficiency of the MIR pulse generation was very sensitive to the delay between the fundamental and SH pulses. It was revealed that the delay dependence of the efficiency comes from the interference between two opposite parametric processes, \(\omega_{1}+\omega_{1}-\omega_{2}\rightarrow\omega_{0}\) and \(\omega_{2}-\omega_{1}-\omega_{1}\rightarrow\omega_{0}\). Significant phase shift at around 2200 cm\({}^{-1}\) could also be explained by the phase difference between the signals from the two parametric processes, where the two signals have opposite chirp with each other.
# Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling
## 2 Methodology
### Data Clustering for Sampling  
When performing performance adaption, it is inevitable to incur considerable computational cost if all data points are included. It is necessary to select few points from the entire available dataset. The quality of data selection will directly affect the accuracy of the prediction model. The random sampling method can reflect the distribution characteristics of the data to some extent, but it may have difficulties when facing operating condition data. For example, some operating conditions may repeatedly occur in a certain sampling time period, increasing their distribution density. This may easily result in these similar data being sampled multiple times, while some operating conditions are not collected. Thus, a data-clustering technique was employed to learn the intrinsic relationship between data and divide them into different clusters. The basic concept is that similar operating condition data are close to each other and have similar outputs. The clustering technique can help to group points that are within close proximity and find the centroids that have the smallest sum of distances to all other points. In this paper, k-means clustering [26] was used to group the data and find the centroids; then, the data points closest to the centroids were chosen and formed the clustering sampling set to calculate the characteristic adaption. K-means is a representative method of unsupervised learning, which divides data into k classes predetermined according to the distance between the samples. The k-means algorithm has the advantages of fast convergence speed and strong interpretability; its drawback is that the number of k clusters needs to be predetermined, but in this study, it was turned into an advantage as the number of sampling points can be chosen according to the computational needs. On the one hand, cluster sampling can make full use of the collected data. On the other hand, it can also collect data with large feature differences as much as possible. Suppose there are m data points and divide them into k clusters. The calculation steps are as follows:  
1. Randomly select \(k\) points, \(\mu_{1}^{0}\), \(\mu_{2}^{0}\),..., \(\mu_{k}^{0}\), as initial centroids.
2. In the \(t^{\text{th}}\) iteration step, calculate the distance from each point to the \(k\) centroids according to Equation (7), and assign them to the nearest cluster. \[distance_{i,j}^{t}=\left\|x_{i}-\mu_{j}^{t}\right\|^{2},\ i=1,\ldots,m,j=1, \ldots,k\] (7)Calculate the mean of the points in each cluster and update the centroids by Equation (8).  
\[\mu_{j}^{t}=\sum\
olimits_{x_{i}\in C}\frac{x_{i}}{n},\ n\ is\ the\ number\ of\ points\ in\ C_{j} \tag{8}\]  
4. Repeat steps 2 and 3, until the difference between two consecutive centroids is smaller than a predefined threshold or the max iteration step is achieved.
## 3 Methodology  
### Hydrological Model: The Block-Wise Use of TOPMODEL (BTOP Model)  
The BTOP model is a distributed watershed hydrological model based on a physical mechanism [25; 26]. It was initially developed at Yamanashi University, Japan [27] for distributed hydrological simulation of large basins [28].  
Compared with the traditional TOPMODEL model, the BTOP model was developed based on its in-depth analysis and improvement. It has made some progress in terms of river network technology, natural sub-basin delineation, the method of giving the spatial distribution of the catchment model parameters as grid cells, and confluence accuracy [29]. The BTOP model has been widely used in the hydrological simulation of various river basins worldwide, such as the Fuji River Basin [29; 30; 31] (3500 km\({}^{2}\)) in Japan, the Mekong River Basin [32; 33] (811,000 km\({}^{2}\)), the Min River Basin in China [34], eight basins in Nepal [35] and a dozen small- and medium-sized watersheds in the United States [36]. Effective testing has been completed in all these river basins.  
(1) Runoff generation process  
The BTOP model uses TOPMODEL as the runoff generation sub-model, and the raster cells are divided into four parts in the vertical direction: vegetation part, root part, unsaturated part and saturated part. The vegetation part includes rainfall and evapotranspiration processes, the unsaturated part is divided into active and inactive zones, and the lowermost layer is the saturated zone. The main flow generation types of grid cells include super permeability, full storage and base flow.  
(2) Flow routing process  
The BTOP model uses the Muskingum-Cunge method as a flow routing sub-model, as a physically-based flow routing method rather than an empirical approach, this algorithm is equivalent to the convection-diffusion model and it is more suitable for large river basins than the kinematic wave model since the riverbed slopes are often mild.  
(3) Model parameters  
The five calibration parameters for saturated transmissivity of soil are the block-average Manning coefficient (\(n_{0c}\)), drying function parameter (\(\alpha\)), discharge decay factor (\(m\)), block-average saturation deficit (\(SD_{bar}\)), and groundwater dischargeability (\(D_{o}\)), which are used as the initial values in the training period of the model. They are automatically optimized by the SCE-UA [37] algorithm to obtain the parameters for the testing period, which are then substituted in the model for runoff simulation. The BTOP model was chosen because of its solid physical basis and few rate-determining parameters. Moreover, the parameters of the model all have physical meaning or interpretation for characterizing soil properties, land use and other features, which can effectively reduce the uncertainty of the model. Moreover, the model has a good application background in the Yangtze River basin, where the study area is located.
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI  
Physicians have established over the years specific protocols to identify and acquire the most relevant ultrasound planes for each organ of interest. The ultrasound probe has to be posed on the available acoustic windows with the right location, orientation, and pressure. Those indications are far from trivial for an inexperienced user, as they depend on the patient's morphology, and the images typically have a poor signal-to-noise ratio. Furthermore, they are one of the first and largest steps in the steep learning curve. Consequently, one of the main goals of computer-assisted ultrasound scanning systems is the real-time recognition of standard planes with clinical value as the first step in assisted guidance. Many studies have used AI to make it easier to recognize the standard planes of the target organs during a scanning session.