esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Industrial Application Experiments  
After testing and comparison with the actual industrial data, the proposed approach was applied to the dressing mill to verify the effectiveness. The average grade of the processed iron ore is 30%. The algorithm is programmed by MATLAB, and the human-computer surface (as shown in Figure 9) is designed by Visual Basic. The surface is a simple version, and the actual application version's language is Chinese with some additional features, such as querying historical data curves, setting parameters, and so on. Figure 10 is a typical flowsheet of the optimal-setting system running manually. The auto-run indicates that the soft-sensor system carries out estimation operations in accordance with a specified time interval.  
When a new set of ore characteristics or new overflow particle size target are obtained, the operator firstly judges whether to update the feature weights or not, then presses \"calculate\" to run the system. When they get the feedback of the actual overflow particle size, they need to input the data to the system, and the entire group of data saves in the host in the format of EXCEL. After two months of successful operation, a part of the data is shown in Table 5. \(f_{1}\) to \(f_{6}\) in the table represent the ore properties retrieval conditions and \(f_{7}\) represents the target particle size requirement. \(J_{1}\) and \(J_{2}\) are the optimized feeding values and \(O\) represents the actual PPS achieved with the optimized feeding values.  
Figure 9: Human–computer interaction interface.  
(1) The cases in the case base are not enough, especially beyond 72-80. The cases cannot contain all the characteristics of the ore. (2) The target value beyond 72-80 means that the ore is much better or worse than the normal state. The other operating parameters of the grinding process are not adjusted according to the ore properties. In industrial applications, some error, such as human error and equipment error, makes the algorithm have some deviation, and some special cases also affect the accuracy of the system. As the optimization system is used for a long time, a large number of cases covering various situations are stored in the case base and the algorithm will be more accurate.
# A DFB-SOA Based Optical Vector Network Analyzer for Characterization of Bandpass Optical Devices
## 5 Conclusions  
A DFB-SOA-based OVNA was introduced and validated experimentally. The proposed OVNA has the capability to conduct transmission response measurements in the electrical domain, thereby obviating the need for optical domain analysis. Compared with the OSSB modulation based OVNAs, the DFB-SOA-associated optical carrier recovery enables the measurement of the ODUT with bandpass response using our proposed OVNA. The experimental findings indicate the successful measurement of an optical filter possessing a 9-GHz passband, achieving a high level of resolution at 25 MHz. The dynamic range of our proposed OVNA can be broadened by adjusting the driven current within the DFB-SOA. Furthermore, the integration of broadband optoelectronic devices into our system allows for an expanded measurement range. Furthermore, the OVNA we have proposed exhibits high potential for integration into a microchip.  
Conceptualization, Y.D. and H.G.; methodology, J.Z.; validation, Z.S. and B.L.; writing, Y.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key Laboratories of Defense Science and Technology Program Project (No. 6142402200401).  
Figure 10: Measured gain spectrum of the DFB-SOA under different driven current.  
Figure 9: Calculated gain spectrum of the DFB-SOA with different gain coefficient.
## 3 Results
### Comparison of C5 and C6 CI Products
#### 3.1.3 Temporal Evaluation
Compared with 8-day and daily CIs, the monthly CIs could better characterize the overall seasonal patterns of surface CIs, especially for C6 daily data, which present more fluctuations due to a higher temporal resolution and uncertainty, most likely using the daily BRDF data. Through quality screening and averaging, monthly CIs smooth the fluctuations caused by poor quality data and background reflectance changes (e.g., ephemeral rainfall) [47] to provide relatively reasonable CIs of surface vegetation for ecological and meteorological modeling at global and regional scales. Compared with monthly data, the 8-day and daily CIs in the NH fluctuate more at the beginning and end of the year, and the SH CIs fluctuate more in the middle of the year. These small waves are largely related to the decline in the main algorithm retrievals in winter for increased snow/cloud cover. Temporal variations in the CIs in the Trop are relatively stable. The 8-day and daily CIs of shrublands and the CI in the Trop exhibit relatively larger fluctuations than the monthly CIs, especially during May to August, when the main algorithm retrievals decline for increased precipitation. The daily CIs of ENF and MF, mainly found in southeastern North America and southeastern Asia, present short-term increases during August and September. These short-term variations may be attributed to tropical storms at that time, which may affect the main algorithm retrievals by destroying tree canopies and influencing the background reflectance [102; 103]. Although the C6 daily CI shows larger fluctuations than the C5 8-day CI, it provides more information to capture rapid surface changes, and the corresponding monthly CI exhibits better continuity than the C5 data. The temporal patterns of the C6 monthly CIs of the EBF in the NH, the MF/ENF in the Trop and the MF/DBF in the SH are more stable and reasonable than the C5 monthly CIs.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 3 The Glider-Assisted Link-Disruption Restoration Mechanism
### The Glider-Assisted Link Disruption Restoration Process  
In view of the above analysis, it is necessary to design a mechanism to restore the link disruption. We first propose the link disruption recognition and glider-scheduling (LDR-GS) mechanism for identifying the disrupted links and collecting the link disruption information in preparation for scheduling glider for restoring the disrupted link. The glider reaches the location of a disrupted link to complete the restoration of the link disruption to restore the network connectivity. In the next section we design GALO to find out the best location to repair the link more accurately due to the unique trajectory of the glider, but in this part we just introduce the LDR-GS mechanism.  
The cluster head node \(hi\) in the sub-volume \(i\) is responsible for collecting and forwarding the data packets sent by all the cluster members \(S_{i}\) in the cluster and the glider \(g_{i}\), and finally sends the data to the surface sink node. Supposing that the routing table of the cluster head holds all the routing information in the volume, and the route arrived by each sensor node is routed according to the shortest path, that is, the path reached by each node is unique. In this network, underwater nodes automatically go to sleep from the active state when no data needs to be received or sent. While the  
Figure 1: Network model.  
node is sleeping, it no longer sends data packets. When the state changes, the node sends the data packet to update the status information. Glider \(g_{i}\) in this volume periodically approaches the head nodes \(h_{i}\) and the collected environmental data is forwarded to \(h_{i}\). At the same time \(h_{i}\) forwards the link disruption information of cluster members to \(g_{i}\) so that the glider \(g_{i}\) can repair the link disruption is a timely way as the relay node. A schematic diagram of this is shown in Figure 2.  
Therefore, in order to get the state information of cluster members for cluster head nodes in time, cluster members need to periodically report their current location and current status to cluster heads through the LDR-GS mechanism and it is necessary to accurately schedule the glider for link healing when the cluster head node finds any link disruption through the GALO algorithm. The process is shown in Figure 3.  
Figure 3: The overall process of glider-assisted link disruption restoration mechanism.  
Figure 2: The process of glider-assisted link-disruption restoration.
###### Abstract  
This work presents the immobilisation of titanium dioxide (TiO\({}_{2}\)) nanoparticles (NPs) and reduced graphene oxide (rGO)-TiO\({}_{2}\) nanocomposite on glass sheets for photocatalytic degradation of methylene blue (MB) under different radiation sources such as ultraviolet and simulated solar radiation. The TiO\({}_{2}\) NPs and rGO-TiO\({}_{2}\) nanocomposite were synthesised through a simple hydrothermal method of titanium isopropoxide precursor followed by calcination treatment. Deposition of prepared photocatalysts was performed by spin-coating method. Additionally, ethylene glycol was mixed with the prepared TiO\({}_{2}\) NPs and rGO-TiO\({}_{2}\) nanocomposite to enhance film adhesion on the glass surface. The photocatalytic activity under ultraviolet and simulated solar irradiation was examined. Further, the influence of different water matrices (milli-Q, river, lake, and seawater) and reactive species (h\({}^{+}\), \"OH, and e\({}^{-}\)) on the photocatalytic efficiency of the immobilised rGO/TiO\({}_{2}\) nanocomposite was careful assessed. MB dye photocatalytic degradation was found to increase with increasing irradiation time for both irradiation sources. The immobilisation of prepared photocatalysts is very convenient for environment applications, due to easy separation and reusability, and the investigated rGO/TiO\({}_{2}\)-coated glass sheets demonstrated high efficiency in removing MB dye from an aqueous medium during five consecutive cycles.  
reduced graphene oxide; nanocomposite; photocatalysis; methylene blue; water matrix +
Footnote †: journal: Applied
## 2 Algorithm Principle
### Error and Noise Model
#### 3.3.3 Error model of Inertial Navigation System  
The main sources of inertial navigation errors are gyroscopes and accelerometers, which have corresponding error models. Due to space limitations, we do not introduce them here, and give the corresponding error models directly. For details, see reference [19].  
(1) Gyro error model  
\[\varepsilon_{Gj}(t)=\varepsilon_{cj}(t)+e_{rl}(t)+\varepsilon_{irj}(t),\ \ (j=x,y,z) \tag{36}\]  
where \(\varepsilon_{cj}(t)\) is the random constant drift, \(\varepsilon_{rj}(t)\) is the correlated drift, and \(\varepsilon_{irj}(t)\) is the uncorrelated drift.  
(2) Accelerometer error model  
\[\varepsilon_{aj}=\varepsilon_{bj}+\sigma_{wj}\ \ (j=x,y,z) \tag{37}\]  
where \(\varepsilon_{bj}\) is the offset of the accelerometer, and \(\sigma_{wj}\) is the white noise error.
## 3 Factors That Affect the Use of Crowd Management at the PTI  
The use of crowd management has been affected by different factors which can be studied through the concept of pedestrian LOS. The LOS has been studied for over five decades, evolving from macroscopic approaches where pedestrians were likened to fluids characterized by speed, density, and flow [15, 49]. [PERSON]'s foundational work set thresholds for free-flow speeds and jam densities, establishing the basis for LOS classifications used in transit facilities [15]. Subsequent studies in Hong Kong and London refined these observations in bidirectional corridors [50]. Recent work has criticized purely physical models for neglecting behavioral nuances [51], suggesting the need for more individualized analyses [52]. Several researchers emphasized the influence of train headways, ticketing locations, and internal train layouts on LOS and crowding [53]. Touch behavior and spatial distribution inside carriages have also emerged as variables impacting user experience and capacity [54]. Despite this robust body of work, few studies have applied multidimensional frameworks in developing cities, highlighting the need for tailored LOS evaluations that consider local passenger characteristics [55]. Based on different studies (see Table 1), the study by [PERSON] et al. [18] introduced a multidimensional LOS model encompassing five dimensions: passenger behavior, train design, passenger characteristics, train operation, and platform built environment (see Figure 1). By deploying YOLO-based video tracking at Francia Station in Valparaiso, Chile, the researchers quantified real-time passenger metrics such as speed, density, dispersion, and height. The descriptive results revealed that most passengers preferred seating (83% seat occupancy), with limited use of corridors and a concentration near train doors. On the platform, passenger speed varied throughout the peak hour--from an average of 0.47 m/s at 7:00 a.m. to 1.12 m/s closer to 8:00 a.m.--indicating behavioral shifts likely tied to urgency and crowd density. Importantly, taller (adult) passengers walked faster than shorter ones (children), and dispersion among passengers correlated with higher walking speeds under certain crowding conditions. The regression analysis showed that variables such as platform furniture (e.g., bins and benches), the type of passenger (based on height), and number of passengers significantly influenced walking speed, a proxy for LOS. For example, platform features generally reduced passenger speed, while dispersion positively or negatively affected it depending on the time period. The detection of consistent patterns suggests that infrastructure elements and human characteristics jointly shape pedestrian flow dynamics. This study expands traditional LOS models by integrating empirical data from video analysis and offering a framework that can adapt to local contexts and user heterogeneity. Its findings underscore the necessity for transport authorities to adopt human-centered and data-driven planning approaches when designing and retrofitting metro systems in urban environments.
## 3 Methodology  
The procedure adopted in the present work is presented in the flowchart, as shown in Figure 2. Firstly, we collected the CFD data from our previous work for the ML step. The goal of using MLAs with CFD simulations was to decrease the computational time and, thus, the computational cost. The four MLAs used in the present analysis are \(k\)-nearest neighbours (\(k\)-NN) [16], Gaussian process regression (GPR) [17], random forest (RF) [18] and multi-layer perceptron (MLP). The best MLA among all was then used for optimisation. MLAs application requires hyper-parameters tuning to obtain the optimum result. The GridsearchCV tool was used for obtaining the optimised values of the important parameters. It is available in the Scikit-learn library [19].  
\begin{table}
\begin{tabular}{c} **Right** \\ **Lung** \\ \end{tabular} \\ \hline
15 lpm & 25.81081 & 45.27027 & 16.48649 & 27.63514 & 12.90541 & 24.18919 \\ \hline
30 lpm & 32.97297 & 59.45946 & 20.60811 & 35.06757 & 15.27027 & 27.97297 \\ \hline
60 lpm & 32.02703 & 67.36486 & 26.89189 & 47.09459 & 16.68919 & 31.75676 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical data of DE for various size particles at different flow rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Right Upper** & **Right Middle** & **Right Lower** & **Left Upper** & **Left Lower** \\ \hline  & & & 10-micron & & \\ \hline
15 lpm & 13.12 & 11.31 & 39.16 & 7.51 & 28.9 \\ \hline
30 lpm & 11.11 & 9.8 & 43.35 & 3.8 & 31.94 \\ \hline
60 lpm & 6.66 & 4.07 & 56.97 & 1.34 & 30.93 \\ \hline  & & & 5-micron & & \\ \hline
15 lpm & 16.54 & 13.78 & 32.16 & 10.87 & 26.65 \\ \hline
30 lpm & 13.32 & 14.72 & 33.12 & 9.5 & 29.3 \\ \hline
60 lpm & 10.96 & 13.61 & 39 & 7.49 & 28.95 \\ \hline  & & & 1-micron & & \\ \hline
15 lpm & 17.49 & 14.39 & 33.15 & 11.11 & 23.86 \\ \hline
30 lpm & 17.19 & 15 & 33.34 & 12.5 & 22.97 \\ \hline
60 lpm & 16.83 & 12.87 & 35.22 & 12.78 & 27.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical DE data for different lung sections for various size particles at different flow rates.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 3 Solving Hierarchical Localization By Means of a Classification CNN and Regression CNNs
### Coarse Localization Stage Using a Classification CNN  
As mentioned in Section 3.1, first we performed a coarse estimation of the location of the robot by detecting only the area where the robot is. This was carried out using a CNN trained for classification. Given a base CNN, we used the transfer learning technique consisting of retraining a pre-trained network to address a different problem with a new set of images, that is, reusing the architecture, weights and parameters of a CNN which already works properly as starting point to build a new CNN with a different purpose (classification of omnidirectional images corresponding to an indoor environment). The main advantage of using this technique is that we can benefit from the intermediate layers, since their parameters have been tuned using a large number of images. Thus, the problem is reduced to adapting the initial and/or final layers and retraining with the new set of images so that the new CNN is able to solve the new problem. This technique considerably reduces the amount of time for training and even leads to better results than creating a new network from scratch. In this case, we re-trained the AlexNet network. We transformed the initial layers to adapt them to an input of a 640 \(\times\) 480 pixel omnidirectional image and the labeling of the output has been transformed to a one-hot vector that identifies the location area. This will be detailed in Section 4, considering the characteristics of the dataset captured in the indoor environment. During the training process, a set of hyperparameters are tuned:  
* Epochs: these define the number of times that the learning algorithm will run through the entire training dataset;
* Initial Learn Rate: this controls how much the model has to change in response to the estimated error each time the weights are updated;
* Optimization algorithm: this changes the attributes of the CNN in order to reduce the losses;
* Loss function: this is an error function that can be used to determine the loss of the model and, as a consequence, update the weights to reduce the loss in the sucesive iterations;
* Batch size: this determines the number of samples that will be passed through to the network at one time.  
The details of the tuning of these hyperparameters and sensitive tests will be shown in Section 5.
## 3 Results
### Absorbed Pollution  
The intermediate parameters for absorbed pollution calculations are shown in subfigures of Figure 8. Figure 8a-d depict R, the rainfall erosion factor (mm\(\cdot\left(\text{hm}^{2}\cdot\text{h}\right)^{-1}\)); K, the soil erodibility factor; Land S, terrain factors (the slope length factor and slope degree factor); and, C, the crop factor, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**TN**} & \multicolumn{2}{c}{**TP**} & \multicolumn{2}{c}{**NH\({}_{4}^{+}\)-N**} & \multicolumn{2}{c}{**COD**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Load**} & \multicolumn{1}{c}{**Total (t)**} & \multicolumn{1}{c}{**Load**} & \multicolumn{1}{c}{**Load**} & \multicolumn{1}{c}{**Total (t)**} & \multicolumn{1}{c}{**Load**} & \multicolumn{1}{c}{**Load**} \\  & \multicolumn{1}{c}{**(kg\(\cdot\)km\({}^{-2}\))**} & \multicolumn{1}{c}{**Total (t)**} & \multicolumn{1}{c}{**(kg\(\cdot\)km\({}^{-2}\))**} & \multicolumn{1}{c}{**Total (t)**} & \multicolumn{1}{c}{**(kg\(\cdot\)km\({}^{-2}\))**} & \multicolumn{1}{c}{**Total (t)**} \\ \hline Urban runoff & 0.7114 & 5.3894 & 0.0745 & 0.5646 & 29.6421 & 224.5591 & 233.7493 & 1770.8087 \\ Agricultural runoff & 1623.4974 & 9388.4000 & 180.8363 & 1054.1692 & & & & \\ Rural resident & 2.1314 & 17.3983 & 0.1199 & 0.9787 & 203.1639 & 1658.3959 & 129.9348 & 1060.6378 \\ pollution & 16.2173 & 135.2771 & 0.7291 & 6.1815 & 497.3304 & 4148.5027 & 90.6505 & 756.1647 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pollution load and the total discharge of the four types of dissolved pollution.  
Figure 8: Intermediate parameters of absorbed pollution.  
Finally, TN and TP, calculated for the absorbed pollution, are given in Figure 9 below. From Figure 9, it can be seen that absorbed TN was in the range of 0.2937-1164.3431 kg/km\({}^{2}\), and the absorbed TP was in the range of 0.0353-114.4276 kg/km\({}^{2}\). We can also see that the highest value was in the west part of Henan Province. The calculation of the absorbed pollution considered multiple factors, including DEM and precipitation, thus appearing as continua of variations.
## 5 Long-Term Stability and Lining Safety of Surrounding Rocks in Tunnels
### Timing of Lining
#### 5.2.2 Earliest Timing of Lining  
The earliest timing of lining is mainly to control the deformation load imposed on the lining by the creep deformation of the surrounding rocks, thus avoiding excessive stress and damage to the lining in the later stage. Based on the calculation of lining stress after the pouring of lining, the earliest timing of lining for XZ43+090 is determined to be 90 days after the support installation and that for XZ67+070 is 80 days after the support installation. The forces of these two linings are shown in Figures 13 and 14. The maximum compressive stress in the concrete and steel of XZ43+090 is 15.78 and 157.80 MPa, respectively. The maximum compressive stress in the concrete and steel of XZ67+070 is 16.07 and 161.30 MPa, respectively. Figure 17 shows the deformation of the surrounding rocks after the lining is poured at this time.  
We can observe that the settlement deformation of the arch 90 days after the support is installed in XZ43+090 accounts for 74% of the overall deformation (deformation rate = 0.11 mm d\({}^{-1}\)), and the convergence deformation of the sidewall is 67% of the overall deformation (deformation rate = 0.13 mm d\({}^{-1}\)). The settlement deformation of the arch 80 days after the support is installed in XZ67+070 accounts for 75% of the overall deformation (deformation rate = 0.149 mm d\({}^{-1}\)), and the convergence deformation of the sidewall  
Figure 16: Stress of the lining structure of XZ67+070: (a) stress in concrete, (b) stress in steel.  
Figure 15: Stress of the lining structure of XZ43+090: (a) stress in concrete, (b) stress in steel.  
is 67% of the overall deformation (deformation rate = 0.159 mm d\({}^{-1}\)). The deformation rates of the arch and sidewall are within the limit.
## 3 Preliminaries
### Client Selection in FL  
Client selection is crucial in FL as it directly impacts the efficiency, convergence speed, and accuracy of the training process [20]. The clients selected in each training round contribute to model updates, so it is essential to choose clients that balance computational capacity, resource availability, and privacy concerns.  
Several client selection strategies have been proposed, each focusing on different criteria, such as the following:  
* Random Selection: clients are chosen randomly, often used as a baseline.
* Battery Optimization: prioritizing clients with sufficient battery levels to prevent premature device shutdowns during training.
* Privacy Preservation: choosing clients based on their privacy needs, ensuring that the most privacy-sensitive data is processed by devices with adequate security.
* Accuracy-based Selection: selecting clients with high local model accuracy to speed up convergence.