esa-sceva/satcom-chunk-collection huggingface dataset:
# High Rate Stormwater Treatment for Water Reuse and Conservation--Review
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & & **Short-Term Impacts:** & & **Long-Term Impacts:** \\ \hline \multirow{4}{*}{Ecological health:} & - & Improved water quality by reducing pollutants entering water bodies. & - & Restoration and enhancement of natural ecosystems through the promotion of native vegetation and creation of interconnected green spaces. \\  & - & Creation of habitats for wildlife through features like vegetated swales and rain gardens. & - & Increased biodiversity and resilience to climate change. \\  & - & Reduction of erosion, thus preserving soil integrity. & - & Improved soil health over time. \\ \hline \multirow{4}{*}{Human health:} & - & Decreased risk of flooding and erosion, enhancing public safety and reducing property damage. & - & Reduction of waterborne diseases and improvement of air quality. \\  & - & Provision of recreational opportunities and enhancement of mental well-being through green spaces. & - & Mitigation of the urban heat island effect, leading to healthier and more livable communities. \\  & - & Mitigation of the urban heat island effect, improving overall urban livability. & - & Potential economic benefits through reduced stress, depression, anxiety, and property damage. \\ \hline \multirow{4}{*}{Environmental sustainability:} & \multirow{4}{*}{-} & \multirow{4}{*}{Prevention of pollutants such as sediment, heavy metals, and nutrients from contaminating water bodies. } & - & Long-term protection and restoration of aquatic ecosystems. \\  & - & Prevention of pollutants such as sediment, heavy metals, and nutrients from contaminating water bodies. & - & Cascading effects on entire watersheds, including improved habitat for aquatic organisms. \\ \cline{1-1}  & - & Protection of aquatic ecosystems by maintaining overall water quality. & - & Minimization of pollution and preservation of water quality through effective stormwater management practices. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Short- and long-term impacts of GSI systems. 1. Technological Gaps: Identifying shortcomings in current GSI practices and exploring new technologies with the potential to meet non-potable reuse requirements (Section 2). 2. Detailed review of different new filter media to enhance the removal of specific pollutants (Sections 3 and 4). 3. Pilot-scale Investigation on High-rate Stormwater Technologies: Assessing the feasibility of high-rate filtration and adsorption systems to achieve cost-effective stormwater reuse. This provides insights for future research for the development and validation of stormwater treatment trains in urban areas (Section 5). Overall, the transition to more sustainable stormwater management practices is not only environmentally necessary but also holds economic and public health benefits, especially in the context of global water scarcity.
## 3 Results  
### Power Plant Types and Sensor Technologies Covered  
Before delving into the methodological features of the reviewed articles, it is essential to understand the technological and application contexts in which the fault detection methods were applied. To this end, the dataset was analyzed based on two practical dimensions: the type of power plant under investigation and the sensors or monitoring technologies used for data acquisition and fault detection.  
Across the three data sources--MDPI, Elsevier, and Google Scholar--a diverse range of power generation systems was represented, as shown in Tables 1-3. The most commonly addressed category was thermal power plants, which appeared consistently across all sources. This prevalence reflects the widespread use and operational complexity of these systems, particularly those involving steam turbines and boilers.  
MDPI showed strong representation in thermal and hybrid plant studies, with occasional references to waste-to-energy plants and distributed heating systems.  
* Elsevier included the broadest variety of plant types, notably covering nuclear and combined cycle power plants in greater detail. Articles sourced from this database also discussed integration with renewable components, such as PV panels in hybrid systems.  
\begin{table}
\begin{tabular}{c c} \hline
**Power Plant Type** & **References** \\ \hline Thermal & [68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85;  
* Google Scholar, while also dominated by thermal systems, included more exploratory studies involving wind turbines and microgrid configurations, possibly due to its indexing of academic theses and preprints.  
This distribution indicates a balanced but not entirely uniform coverage, where traditional systems (coal, gas, nuclear) dominate, while renewable and hybrid systems are gaining attention, especially in more recent publications.  
Sensor types and data sources used for fault detection were also extracted and categorized. A large proportion of the reviewed studies employed temperature, pressure, and vibration sensors, in line with common practice in industrial monitoring.  
* Thermal imaging and infrared sensors were especially notable in studies focusing on PV systems or power electronics (e.g., inverters and converters), particularly in MDPI and Google Scholar articles.
* Acoustic sensors and current signature analysis were featured in condition monitoring of rotating machinery (e.g., pumps and generators), with application to hydroelectric and nuclear systems.
* Advanced techniques such as multisensor fusion, drone-assisted monitoring, and unsupervised anomaly detection from signal data were more frequently seen in recent Elsevier papers.  
These findings demonstrate the tight coupling between sensor selection and system architecture and highlight the need for fault detection frameworks to be adapted not only to the type of algorithm used but also to the nature of the monitored system and its associated data streams.
## 3 Results and Discussion
### The Reusability and Batch Difference of 3D-rGOPFH  
The reusability of the sorbent in the MSPE procedure can reduce the cost of analysis. To investigate the mechanical stability and reusability of the 3D-rGOPFH, the recovered 3D-rGOPFH sorbent was rinsed with 2 mL of acetone for three times. After such washing, no sample residue was detected with GC-MS/MS, and the sorbent was reused for the next extraction cycle. The 3D-rGOPFH sorbent was used at least ten times without a significant loss of the adsorption ability, which demonstrated that the fabricated 3D-rGOPFH via hydrothermal reduction possessed high stability. The robustness for the preparation of different batches of the 3D-rGOPFH has also been examined. The adsorption ability of the 3D-rGOPFH, synthesized in five batches, exhibits no obvious changes with the RSDs below 8.7% from batch to batch.
## 4 Problem Solution
### Offline Part
Through iterations, Q\({}^{\prime}\) learns to converge to real Q-values. The best offloading policies in each state are extracted from the behavior that results in the greatest efficiency, depending on Q\({}^{\prime}\). At timeslot \(t\), the selected action could now be described as \(a^{*}=\text{argmax}\ Q(S_{t},\ a_{t},\text{w})\). The experience replay method is being used in the learning process to maximize learning performance, and the learning experience at each time is preserved in a replay memory. The experience is made up of all observed state transitions and acquired utilities as a result of behavior. \((S_{t},\ a_{t};\ Ef_{t};\ S_{t+1})\) is the experience gained during time slot \(t\). A batch of recorded experiences taken at an arbitrary from the replay memory is being utilized as samples in training the parameters of the Q-network throughout Q-learning updates. The training aims to get the gap between Q \((S_{t},\ a_{t})\) and Q\({}^{\prime}(S_{t},\ a_{t},\text{w})\) to be as small as possible. To represent the difference, we use a loss function Equation (30) [23]. \[\text{Loss}\ (\text{w}_{t})=\mathbb{E}\ a[\frac{1}{2}a(\text{Q}^{\prime} \ _{\text{tar}}\ -\text{Q}^{\prime}(S_{t},\ a_{t},\ \text{w}))^{2}] \tag{30}\]Here \(\mathrm{w}_{t}\) is the Q-network parameters at time \(t\). \(\mathrm{Q^{\prime}}_{\mathrm{tar}}\) is a learning objective that represents the function's optimal value in frame \(t\) and could be described as [55]:  
\[\mathrm{Q^{\prime}}_{\mathrm{tar}}\ =Ef_{t}+\gamma Q\ (S_{t},\,\mathrm{argmax}\, \mathrm{Q^{\prime}}(S_{t+1},\ a_{t+1},\ \mathrm{w}_{t})). \tag{31}\]  
To change \(\mathrm{w}_{t}\), we use a gradient descent method. The gradient obtained by differentiating \(Loss\ (l)\) is as follows:  
\[\Delta\mathrm{w}\ Loss\ (\mathrm{w}_{t})=\mathbb{E}\ a\left[\Delta\mathrm{w}_{t} \ \mathrm{Q^{\prime}}(S_{t},\ a_{t},\ \mathrm{w}_{t})\left(\ \mathrm{Q^{\prime}}(S_{t+1},\ a_{t+1},\ \mathrm{w}_{t})-\mathrm{Q^{\prime}}_{\mathrm{tar}} \right)\right] \tag{32}\]  
Then, \(\mathrm{w}_{t}\) is updated based on Equation (32). Additionally, \(\chi\) denotes the scalar stage scale [55]. \[\mathrm{w}_{t}\leftarrow\mathrm{w}_{t}-\chi\Delta\ \mathrm{w}\ \mathrm{Loss}\ ( \mathrm{w}_{t}) \tag{33}\]  
We use the \"greedy policy\" to prevent local maximum when balancing exploration and exploitation in the learning experience. To gain better offloading strategies, randomly selected actions are taken with probability \(\varepsilon\); otherwise, optimal actions are chosen with probability \(1-\varepsilon\), where \(0<\varepsilon<1\). The DO\({}^{2}\)QIEO scheme is depicted in Algorithm 1. The entire procedure can be performed only when the system is turned off. If the system is switched to online mode, the PDS approach will be used to determine the online requirements. The section that follows explains how the system works when it is in online mode. Figure 4 depicts a schematic representation of the DO\({}^{2}\)QIEO system for IoT offloading computation. Figure 4: The DO\({}^{2}\)QIEO method is being used to establish a decision-making mechanism.
# Recent Advances in Tunable External Cavity Diode Lasers
## 3 Research Progress of ECDL
### External Cavity Quantum Well Laser
#### 3.1.3 Other External Cavity Configuration  
In addition to employing gratings as optical feedback elements, researchers have developed special external cavity structures to enhance laser performance. While planar mirrors are commonly utilized in external cavities, their broad spectral bandwidth often limits their  
Figure 4: Littman/Metcalf ECDL with adjustable dynamic spectral broadness [39]. ©OSA. Copyright 2023 Optics Express.  
effectiveness in longitudinal mode selection. In general, the external cavity structure will be a planar mirror combined with other optical elements to achieve better mode selection. For example, planar mirrors can be used as feedback elements and combined with etalon to fabricate external cavity semiconductor laser [44; 45].  
[PERSON] et al. proposed an external cavity structure comprising two identical lenses, an etalon, and an end mirror [46]. The design incorporated a fixed etalon and a tunable filter to form a double mode selection mechanism. Two lenses were employed to collimate the beam emitted from the gain chip and to focus the light onto the mirror, respectively. Such an external cavity system has high tolerance to the translation and angular misalignment of the end mirror and the lenses. The laser system demonstrated a linewidth of 7 kHz, an output power of 14 dBm, and a tuning range of more than 40 nm.  
High power lasers play a crucial role in a wide range of applications. However, the output power of a single laser diode is inherently limited. The researchers propose to use laser diode array combined with a suitable external cavity structure to achieve higher output power while maintaining a narrow line width and tunability. An external-cavity oscillator was employed as the seed source, with its output coupled into a tapered semiconductor optical amplifier by aspheric lenses [47]. A cylindrical lens was used to compress the slow-axis far-field divergence angle of the amplified light. Finally, an output power of 4.5 W at 670.8 nm was achieved.  
The ECDL can be tuned by adjusting optical elements such as the diffraction grating and mirror. During the tuning process, a wavelength shift exceeding a quarter of the laser wavelength often leads to mode hopping, which is difficult to avoid. A novel method of realizing mode-hop-free tuning by synchronous adjustment of optical elements has been proposed [48]. This approach has the potential to significantly enhance tuning speed. The external cavity is shown in Figure 5, including a periscope, etalon, and narrow band pass filter all mounted on a rotating stage. The etalon and narrow bandpass filter are used for mode selection and mode locking, while wavelength tuning was achieved by rotating the periscope. Experimental results demonstrated a mode-hop-free continuous tuning range of 1.7 THz at a central wavelength of 652 nm.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 2 System Design  
The proposed system architecture, depicted in Figure 1, leverages a CF-mMIMO-driven cloud-edge-terminal collaborative framework for UAV swarms. This hierarchical architecture contains three tiers:  
* **Cloud tier** integrates cloud servers and core networks for centralized computation and global resource management;
* **Edge tier** deploys distributed CF-mMIMO transceivers with edge servers to enable spatial multiplexing and low-latency processing;
* **Terminal tier** comprises a swarm of \(K\) single-antenna UAVs for visual perception tasks.  
In the proposed system, inter-UAV communication is indirectly achieved via cloud-edge relaying. Each UAV periodically uploads its status to the cloud server via UDP, which aggregates and broadcasts swarm-wide state updates. This design eliminates direct D2D  
Figure 1: System design.  
links, simplifying synchronization and enhancing centralized coordination. To mitigate interference, the CF-mMIMO system monitors channel conditions and dynamically real-locates resources to interference-free sub-bands, ensuring link reliability during swarm operations. The hierarchical structure and functionalities of each tier are detailed below.
## 4 Conclusions  
This paper investigated the sensitivity characteristics of rotors subjected to rub-impact problems to parameters of rubbing interfaces. The harmonic balance method and pseudo arc-length continuation method are utilized to obtain the steady-state response of the system. Furthermore, the analytical expressions for the sensitivity coefficients are derived for the purpose of accuracy and performance. Two classical models, namely the Duffing oscillator and the gap model, are utilized to verify the accuracies and capabilities of the adopted methods. Then numerical investigations of the sensitivities of steady-state response to parameters of rubbing interfaces are performed and conclusions are drawn as follows:  
1. The response of rotors subjected to rub-impact is sensitive to the parameters of rub interfaces only when the amplitudes are large enough to reach or exceed the  
Figure 23: The sensitivity curves of the response of the Jeffcott rotor with respect to normal stiffness at \(g=0.1\) mm and \(\mu=0.5\): (**a**) First-order sensitivities; (**b**) Second-order sensitivities.  
Figure 22: The sensitivity curves of the response of the Jeffcott rotor with respect to friction coefficients at \(g=0.1\) mm and \(k_{y}=1\times 10^{7}\) N/m: (**a**) First-order sensitivities; (**b**) Second-order sensitivities.  
prescribed initial clearance. If that is not the case, the whole system is equal to a linear system under a periodical excitation force.
2. The response of the rotor is much more sensitive to the initial gap with the two other factors being relatively insignificant. The sensitivities to the initial gap \(g\) are not contiguous at rotating speeds where a separation/contact status transformation happens since it determines the existence of rub-impact.
3. With the increase of initial gap, the rotors become more sensitive. In contrast, the friction coefficient has negative impact on the sensitivities and the multi-solution region grows larger with its decrease. Moreover, the variation of normal stiffness has relatively little influence on the sensitivities but it does influence the characteristics of the multi-solution region, i.e., the region expands as it increases.  
Conceptualization, M.J., Z.Z., Y.X. and D.Z.; methodology, M.J., Z.Z., Y.X. and D.Z.; validation, M.J.; resources, Z.Z.; writing--original draft preparation, M.J.; writing--review and editing, Z.Z., Y.X. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Research Program supported by the 111 project (B16038), China.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 3 Design of a Digital Satellite-Borne Base Station for Quick Functional Reconfiguration
### Network Model
#### 4.2.4 Selection Algorithm for the VNF to Be Migrated  
In this paper, we introduce a resource-aware RAIL-based algorithm [21] for decision-making regarding the VNFs to be migrated.  
When a node is overloaded and a VNF has to be migrated, the VNF to be migrated must be chosen based on the type of overloaded resource in order to reduce the resource occupancy of the overloaded node to a reasonable range as quickly as possible. The occupancy rate of each VNF on the node for each resource can be calculated as described in the previous section. We also defined the threshold value for each resource and defined a triplet \((\alpha,\beta,\gamma)\) to represent the overload status of the three resources of the node. When the computing resource is overloaded, \(\alpha\) is assigned a value of 1. When it is not overloaded, \(\alpha\) is assigned a value of 0, and similarly for the storage and network resources; the occupancy rate of each VNF on the node for each resource can be calculated as \(\frac{n_{i}^{core}}{N_{i_{j}}^{core}}\), \(\frac{n_{i}^{mem}}{N_{i_{j}}^{mem}}\), \(\frac{c_{i}}{\sum_{k\
eq j\}k\in N^{Node}C_{jk}}\).  
We define the migration index \(\theta_{i}\), add the perception of a multidimensional environment, and use the RAIL dynamic weight setting to ensure that the weight of overloaded resources is greater than 1, the weight of lightly loaded resources is less than 1 according to the usage of corresponding resources, and the migration index of virtual functions with more usage of overloaded resources is greater.  
\[\theta_{i}=\sum_{k}\biggl{\{}\alpha_{k}\frac{1}{1-x_{k}}+(1-\alpha_{k})(1-x_{ k})\biggr{\}}\chi_{ik},K=(Core,Mem,Com) \tag{12}\]  
where \(\alpha_{k}\) indicates whether the resource type \(k\) is overloaded, \(x_{k}\) indicates the node's resource type \(k\)'s occupancy, and \(\chi_{ik}\) denotes the occupancy of \(v_{i}\) on the resource type \(k\) of the node.  
All VNFs on the overloaded nodes are ranked according to the migration index, and the VNF with the highest migration index is chosen to enter the VNF migration sequence.
## 3 Analysis of the Lane Control Strategy Based on Truck Platooning Simulation
### Analysis of the Lane Control Strategy
#### 3.2.2 Analysis of the Optimal Speed Control Strategy  
Based on the conclusion of Scenario I, real-time road traffic information was employed in the CVIS environment to optimize the speed control of truck platooning using a genetic algorithm. The algorithm's objectives encompass maximizing traffic flow in a predicted time domain for the bottleneck cell, minimizing travel time for all vehicles on the road, and minimizing fuel consumption from the perspective of truck platooning vehicles. The optimal speed control strategy first analyzes the difference between optimal speed control with and without truck platooning when setting up dedicated lanes, and further examines the difference between optimal speed control with truck platooning in the queue and separation phases under a 4F32T strategy by vehicle type and different lanes.  
Figure 6 presents a comparison of the delay and conflict rates with and without optimal speed control of truck platooning when the second lane is opened for HDT and truck platooning in the separation phase are set to pass the weaving section under the exclusive lane policy. \"-N\" and \"-Y\" mean simulations without and with optimal speed control strategy respectively. Each column in Figure 6 represents the proportion of trucks in the total traffic flow. ATP indicates automated truck platooning, and the corresponding percentage represents the proportion of platooning trucks among overall truck traffic. Furthermore, the labels -N and -Y denote the absence and presence of optimal speed control, respectively.  
Overall, there is a decrease in delay when there is optimal control of truck platooning under the same conditions. With an increase in truck percentage and traffic, the delay and conflict rate are significantly decreased when there is optimal truck platooning control. In terms of delay, when the volume of traffic is 7000 veh/h, when the proportion of trucks is 5%, 10%, 15%, 20%, and truck platooning separation form, the delay decreases by approximately 1-2, 2-5, 5-10, and 3-16 s. With an increase in the proportion of truck platooning, there is an increase in the value of truck platooning optimal control on delay. In  
Figure 6: Separation phase with 4F32T strategy through the weaving section in Scenario II.  
terms of conflict, the same conditions have truck platooning optimal control of conflict rate in each truck proportion and truck platooning proportion; in the flow \(\leq\)6000 veh/h, there is no significant difference between the conflict rate discount; in the volume 7000 veh/h, the conflict rate is about 0.8, with truck platooning optimal control being better than no truck platooning optimal control.
## 5 Post-Evaluation Analysis
### Spoken Term Detection
#### 5.1.1 System Analysis for In-Language and Out-of-Language Queries  
An additional analysis for INL and OOL queries was carried out for the STD task, and the results are presented in Tables 18-20 for MAVIR, RTVE and SPARL20 data, respectively. The results show in general better system performance for INL than for OOL queries (regardless of threshold calibration issues), since the target language matches that used for the system construction. The systems that do not employ data augmentation generally perform better than their counterparts with data augmentation as happened in the overall evaluation results.
## 2 Test Setup and Model Preparation
### Layout of Components  
For the model foundation, the layout of components were the same for the test group and the conventional group. Figure 4 shows the plan and schematic cross-section of the model foundation along with the layout of components. Strain gauges were symmetrically attached to model piles and the sheet-pile wall to measure the bending moment. To investigate the seismic response of the sand at different depths, both accelerometers and pore water pressure transducers were arranged in three layers. In addition, displacement transducers were fixed on the shaking table via an iron bracket.