esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Factors Governing Survival and Occurrence of Opportunistic Pathogens in Premise Plumbing Systems
### Disinfection
#### 4.3.6 UV Disinfection  
UV disinfection is another treatment technology for the inactivation of pathogens and is commonly employed by utilities in Australia, the US, and Europe as a final polishing step in the drinking water treatment train. UV light irradiation does not kill microorganisms; rather, it damages their DNA, thereby disrupting their ability to reproduce and preventing infectivity. Optimum disinfection is achieved at a wavelength of 254 nanometes; however, efficacy of treatment can be significantly impeded by high turbidity [18, 167, 168]. High concentrations of particulate matter and certain dissolved species inhibit the effectiveness of UV disinfection by impairing the transmission of light to the target microorganisms [154]. Some studies indicate that high water temperatures can reduce the longevity of UV reactors and equipment; however other studies insist that temperature fluctuations have little or no impact on disinfection efficacy. UV disinfection does not support the formation of DBPs at doses applied to drinking water, nor does it change pH or treated water quality to increase corrosivity [3].  
In support of UV disinfection for the control of OPPPs, it was showed that relatively low UV doses (mJ/cm\({}^{2}\)) achieved a 2-log (99%) reduction in six different _Legionella_ species [15]. [PERSON] et al. [43] showed that higher UV doses achieved a 5-log (99.999%) reduction in 20 min in a recirculating model premise plumbing under various test conditions. At a higher UV dose again (90 mJ/cm\({}^{2}\)), [PERSON] et al. [169] showed that _Legionella_ species were inactivated within three minutes of exposure. In general, UV light is most effective on protozoa followed by bacteria and least effective against viruses. In regard to the relationship between OPPPs and free-living amoebbea, [PERSON] et al. [170] determined that higher fluence was required for a 4-log (99.99%) reduction in _Legionella_ species when co-cultured with amoeba. UV disinfection has been successfully applied to control _Legionella_ in building water systems [167, 171].  
Commercial in-building UV systems are available across a wide price range and can treat between 3.7 and 1900 L/min with minimum set-up requirements (i.e., basic plumbing skills and an electrical outlet) [168]. The most significant ongoing maintenance costs and efforts typically include the annual replacement of UV bulbs and the quarterly cleaning of the quartz sleeve through which the water flows. Due to the lack of residual provided by UV, downstream growth of microorganisms is a concern with this method. In addition, the application of UV may diminish disinfect residual [3]. As a result, it is often used to supplement other treatment options and/or as near as possible to the POU. UV treatment is not always possible where systems are susceptible to downstream contamination [15].
## 3 Results and Discussion
### Effect of Steel Fibers and Steel Slag on the Particle Loss Resistance of Asphalt Mixture  
It can be seen from Figure 5 that BA has the largest particle loss ratio compared to other three asphalt mixtures, while SF + SS shows the lowest particle loss ratio. Adding steel fibers to asphalt mixture can reduce the particle loss, indicating an improvement of the strength of the mixture. The specific volume characteristics and surface texture structure of steel fibers can improve the absorption and stabilization of asphalt. Furthermore, the spatial network structures formed by steel fibers, which was distributed between the aggregates, has a reinforcing effect on the mixture. The results of SF + SS presented the same conclusion that the addition of steel fiber can enhances the cohesion of asphalt mixture.  
The addition of steel slag can also decrease the particle loss slightly comparing with BA. The reason for this can be explained by the properties of steel slag which has high wear resistance and porous structure. More asphalt was absorbed resulting the increased bonding area between asphalt and steel slag. Basicity property of steel slag also contribute to good adhesion between asphalt and steel slag, making better wrapping with asphalt (S. F. + SS has the highest Particle loss resistance due to the reinforcement of steel fibers and the strong adhesion between asphalt and steel slag.  
Figure 4: Marshall Stability and Marshall Stability Ratio (MSR) of different asphalt mixtures. SF: steel fibers.
## 3 Results and Discussion
### Plate Shape
#### 3.1.2 Salt Spray Test  
Figure 6 shows the results of the salt spray test for bare aluminum and AAO-O (oil impregnation on anodic aluminum oxide) surfaces of aluminum 7075. Corrosion was indicated after 5 h in the case of bare aluminum, whereas the corrosion was not generated in the case of the AAO-O after 720 h. The salt spray test was stopped after 100 h in the bare aluminum case as the corrosion was intensively examined after 100 h. In the case of AAO-O, the test was continuously conducted up to 720 h. The results of the salt spray test showed that the oil impregnation on the AAO surface of aluminum 7075 could enhance the corrosion resistance.  
Figure 5: FE-SEM images of anodic aluminum oxide with widening of aluminum 7075.
###### Abstract  
The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential.  
drone networks; unmanned aerial vehicles; genetic algorithms; reinforcement learning; Q-learning; multi-objective optimization Article
# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 3 Results  
### Data Sources and Threshold Parameters  
The experimental area of this paper is vehicle-mounted LiDAR urban environment data from the competition data of the 7 th National LiDAR Conference Data Processing Competition in 2021, which is the road scene around the College of Surveying and Mapping and the College of Civil Engineering of Henan University of Technology acquired by SSW-2 vehicle-mounted laser scanner laser point cloud, and features include ground, street trees, buildings, green belts, public infrastructure, pedestrians, etc. Among them, there are independent street trees, connected street trees, and there are also street trees and streetlights and other cross phenomena; because of a small number of street trees point cloud due to obscuration and other reasons, there will be some missing.  
Due to the presence of different tree species, this study divided the experimental area into three parts based on the distribution of the three trees in the current experimental area (including small poplar trees, large poplar trees and willow) (Figure 6). Experimental areas \(S_{1}\) and \(S_{2}\) are dominated by small poplar trees with a small number of large poplar trees, while experimental area \(S_{3}\) contains mainly willows and large poplar trees. The geometric characteristics of the three species differ from each other, with the small poplar trees having the same characteristics as the common trees in the road environment, showing a regular shaped trunk with a denser canopy; the large poplar trees differs from the small poplar trees in that the canopy has more branches and leaves but larger gaps between the leaves, and the canopy is much less dense than the small poplar trees in terms of the morphology of the point clouds; in contrast to the growth direction of the other species, the willow has a pendulous canopy and In contrast to the other species, the willow canopy is pendulous and shows a tendency to extend downwards, with the point cloud pattern showing branches going vertically downwards from the top of the tree and obscuring part of the trunk.  
The point cloud data from the three experimental areas and the representation of the different tree species are shown in Figure 6, and statistical information on the data from the experimental areas is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Area Coverage (m\({}^{2}\))** &
\begin{tabular}{c} **Average Point** \\ **Density (Point/m\({}^{2}\))** \\ \end{tabular} & **Scanning Points (Point)** \\ \hline \(S_{1}\) & 120 \(\times\) 40 & \(\sim\)500 & 2,416,962 \\ \(S_{2}\) & 100 \(\times\) 45 & \(\sim\)700 & 3,482,385 \\ \(S_{3}\) & 70 \(\times\) 35 & \(\sim\)900 & 2,252,170 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Point cloud data information statistics result.  
The parameter threshold statistics for the main algorithms in this paper are shown in Table 3, with a linear feature linearity threshold greater than 0.8.
## 2 Materials and Methods
### Performance Variables  
Outcome measurement was grounded in basketball performance variables and included agility, vertical jump, and sprint, performed in that order. The order remained consistently the same for all participants and was deliberately structured to minimize potential fatigue effects. All tests were conducted by two researchers not involved in the execution of the interventions and who were blinded to the participant's allocation.  
To assess agility, the Lane Agility Test (LAT) was employed. Recognized as a basketball-specific test [18], it is included as one of the National Basketball Association (NBA) Draft Combine tests [19]. For the evaluation, four cones were positioned at the corners of a designated area on a basketball court. Players were required to navigate the circuit in the shortest time possible, incorporating sprints, lateral movements, and backward running. The time taken by each athlete to complete the course was recorded. Time was measured with a handheld manual chronometer. Each athlete made two attempts, with a 1 min rest interval between sets.  
Jumping ability was measured through countermovement jump (CMJ). The test began with the subject standing on a platform, in the marked area, with their hands placed on their waist. Subsequently, the subjects were instructed to perform a vertical jump as high as possible by rapidly transitioning from knee and hip flexion to extension. The height reached in the jump was recorded with the Optojump jump platform (Microgate, Bolzano, Italy); this device has been validated [20]. Each athlete performed 3 jumps, leaving 30 s of rest between attempts.  
Finally, the 20 m sprint test (Sprint 20 m) was used to evaluate speed. From a standing position with the dominant foot forward, the athlete had to run 20 m at maximum speed. Three photocells (Microgate(r) Polifemo Radio Light, Bolzano, Italy) were placed to record the athlete's time when passing the 20 m mark [21]. There were 2 attempts per athlete with a 1 min rest between attempts.  
In each performance variable, the average of all attempts was used for subsequent analysis, differentiated between before and after the intervention.
## 1 Introduction
Due to the limitations of traditional image processing methods and the advantages of the powerful decoupling feature extraction ability of deep learning, methods based on deep learning are considered a promising solution to this problem. Recently, various deep learning models, such as Convolutional Neural Network (CNN), Region-CNN (R-CNN), Faster R-CNN and You Only Look Once (YOLO) [26; 27; 28; 29], have received much attention. Effective feature representation and recognition can be obtained through these deep learning methods by automatic learning, and the process of using complex feature extraction algorithms as well as crack identification are eliminated. However, these deep learning methods often have high requirements for the quantity and quality of training data. CNN and R-CNN will generate a large number of candidate regions when selecting candidate regions, which will seriously increase the data. In reality, collecting a large number of labeled coating crack datasets is time-consuming, and to our knowledge, there is no public available dataset on internal cracks in the coating. Transfer learning can transfer the network weight trained from other similar datasets to the target network, which can train the parameters faster and better without having to train from scratch, and can solve the problem of lack of data to a certain extent. Compared with YOLO, the Region Proposal Network (RPN) structure of Faster R-CNN can make the positive and negative samples more balanced, so as to obtain a abetter training effect. YOLO uses a one-stage detector, which mixes the work of positioning and classification. There are no special parameters for candidate regions which would make the training more difficult. The two-stage detector of Faster R-CNN can avoid this problem, thus increasing the training and testing accuracy. For the above reasons, in this paper, an internal crack detection scheme based on Multi Scale Enhanced-Faster R-CNN (MSE-Faster R-CNN) is proposed. Visual Geometry Group Network (VGG-19) was adopted as the baseline network to find the candidate crack Regionof Interest (ROI). Then, the ROI was expanded to obtain the context information. Through a multi-scale Faster R-CNN detector, the state of the candidate region can be determined. This manuscript summarizes the results in the following way. Firstly, the architecture and principle of MSE-Faster R-CNN is presented. Secondly, the dataset preparation and augmentation, as well as the experimental settings are introduced. Thirdly, the performance of MSE-Faster R-CNN in different crack scales is tested and discussed as well as compared to other state-of-art methods. Finally, an information entropy fusion lifetime estimation model is applied using the crack detection experimental results.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 4 System Design
### VLC Integration  
IEEE 802.15.7m is VLC's current standard [5]. This standard defines both PHY and MAC, but is not well suited for IoT since it defines a super-frame structure that assumes bidirectional optical links. In this work, VLC has been introduced as a downlink-only technology in broadcast mode, and a easy custom packet structure has been defined (Figure 8). The potential applications of unidirectional VLC links in IoT are well-studied due to the ubiquity of luminaires and lighting infrastructures. In this work, the communication with an indoor static sensor is demonstrated. Despite this, the proposed hybrid architecture is capable of providing VLC connectivity in other scenarios in which RF is not feasible (or is severely affected by a harsh environment). These scenarios comprise underground (mining, tunnels, e.g.) [28; 29] and underwater environments [30], hospitals [31], and industry. Another  
Figure 6: ZigBee packet structure with the HNF as payload.  
Figure 7: A diagram of an example of data exchange between same-network ZigBee nodes.  
interesting feature derived from the integration of VLC within the proposed architecture is the inherent positioning capability of this technology, which has been extensively studied in the literature [32].  
Start and Stop delimiters have been introduced as a synchronization mechanism. This has been carried out because VLC systems have a dual use. On the one side, VLC access points must act as lighting devices, and this function must remain unaffected by data transmission. On the other hand, these devices are also data sources but information is not being sent continuously. The delimiters help in the time synchronization task. Furthermore, in this case, the HNF subframe (associated to the 0x05 purpose field) comprises only a variable-length data field (up to 48 bytes).
# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
## 3 Methodology
### Metrics  
Since we are looking at node-level clustering in the context of binary anomaly detection (a reading is or is not an anomaly), and since we are considering all of a node's sensors independently to determine the cluster the reading falls into, we will determine the efficacy of the techniques based on whether the NOAA ground-truth visual analysis confirms a reading was an anomaly or not; if so, we ask the following question: does the cluster group in a way that verifies this (and by measuring the Davies-Bouldin Index of the clusters)?  
The first criterion is straightforward: given a readout of NOAA data for the given period and the weather sensor reading for the same period, are readings significantly different than the NOAA data clustered together? The term \"significantly different\" is subjective, so here, we define it as the mean value of the difference between the sensor reading and the NOAA reading in the same time frame. We detail how we find the closest value for a given sensor reading in Section 3.5.  
The Davies-Bouldin Index (DB Index) was introduced in [34]. It is an internal cluster evaluation scheme that uses quantities and features inherent in the dataset. The DB Index is calculated by finding the maximal value based on adding the individual distances within a cluster (intra-cluster distance) \(i\) and every other cluster in the set and dividing by the distance between these clusters (inter-cluster distance), then summing up all the values for all combinations of clusters and dividing by the number of clusters. The lower the value, the better the result. The drawback of this method is that a good value reported by this metric does not necessarily guarantee the best information was retrieved. The DB Index is available in the standard Python sci-kit learn package and in [35]. Please see [34; 36] for an in-depth description. In brief, the DB index acts as a measure of the closeness and density of the clusters.  
\[DBIndex(U)=1/k\sum_{i=1}^{k}\max_{i\
eq j}\left\{\frac{\Delta(X_{i})+\Delta(X_ {j})}{\delta(X_{i},X_{j})}\right\} \tag{2}\]
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 7 Conclusions
The measured azimuthal HPBW between 76\({}^{\circ}\) and 87\({}^{\circ}\) at an elevation of 42\({}^{\circ}\) (approximately the midpoint of medium elevation angles) is adequate for LEO satellite reception. The proposed phased antenna array system can be used to receive LoRa satellites, many of which use the frequency band of 435-438 MHz. Due to the ability to control the main radiation lobe orientation, the proposed system, together with specialized LoRa modules used to decode radio signals received from LoRa LEO satellites, can enhance signal-receiving reliability and expand the coverage capacity of IoT networks. Using the proposed concept, the array can be easily reconfigured for use in the ISM frequency band of 868 MHz. The limits of the signal strength received from the LEO satellites can vary from less than \(-\)100 dBm to \(-\)80 dBm or even more. The simulated gain of the array was high, ranging between 11.55 dBi and 11.97 dBi. Additionally, the power feeding the Yagi-Uda antenna for radiation pattern measurements was approximately \(-\)7 dBm, while the ground station receivers generally demonstrated the capability to detect signals with an MDS level ranging from approximately \(-\)95 dBm to \(-\)138 dBm. These factors collectively led us to conclude that the proposed phased antenna array is well-suited for LEO satellite communications. Conceptualization, C.-L.A., A.L. and A.D.; methodology, M.D. ; software, C.-E.L.; validation, A.L., C.-E.L. and C.-I.A. ; formal analysis, A.Y. ; investigation, M.D. and A.L. ; visualization, C.-I.A. ; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This work was supported by the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2023-F-0579. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest.
## 4 Numerical Results and Discussion
### 2-D Multi-Layer Elastic Wave Propagation Problem in Multilayered Pavement  
As the third example, we model a 2D saturated porous pavement structure to study the propagation of elastic waves. As shown in Figure 6, a square area (0.90 m \(\times\) 0.90 m) with holes (0.020 m \(\times\) 0.20 m) is subjected to the impulsed inclined load at the upper left corner. The domain consists of 46 \(\times\) 46 nodes and is analyzed as a plane strain problem. The thicknesses of layers are 0.25 m (Cement concrete layer), 0.2 m (Cement stabilized subgrade), and 0.45 m (Natural soil). The force impulse \(F(t)\) is expressed as  
\[F(t)=\left\{\begin{matrix}2.0\times 10^{4}N&(0\leq t\leq 40\ \upmu\text{s})\\ 0&(t>40\ \upmu\text{s})\end{matrix}\right. \tag{32}\]  
Figures 7 and 8 shows the cloud diagram of fluid pressure and solid stress distribution in the pores of the multilayer pavement structure at time \(t_{1}=0.15\ \upmu\text{s}\), \(t_{2}=0.3\ \upmu\text{s}\), and \(t_{3}=0.4\ \upmu\text{s}\) obtained using the MDGFEM method and the Newmark method with the time step \(\Delta t=4.0\times 10^{-6}\ \upmu\text{s}\). After an impact load is applied in the upper left corner of this domain, the wave propagates forward as time increases. Pressure and stress nephgrams at different times demonstrate the obvious advantage of MDGFEM in reproducing the silent properties in the area around the hole and at the layer interface compared to the Newmark method, which is helpful for the further development and improvement of nondestructive testing technology of cracks and bottom void diseases in cement concrete pavement.  
Figure 6: Geometric model and boundary conditions of 2D multilayer pavement structure.  
Figure 7: Elastic stress profiles between Newmark method and MDGFEM. **(a)** Newmark 0.15 us; **(b)** MDGFEM 0.15 us; **(c)** Newmark 0.3 us; **(d)** MDGFEM 0.3 us; **(e)** Newmark 0.45 us; and **(f)** MDGFEM 0.45 us.