esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Fastener Failure on Dynamic Performance of Subway Vehicle
## 2 Model of Vehicle-Structure System
### System Model
#### 2.1.3 Model of Coupled System  
The wheel-rail contact relationship is used to couple the dynamic equations of the vehicle and the track after they have been obtained. At present, two popular kinds of model deal with the wheel-rail contact. The first is the \"close fitting method\" [27; 28; 29]. This method connects the vehicle and track system into a large system, which can ignore the degree of freedom of the wheel set, and the vehicle and track are connected directly through the primary suspension spring, and the gap between the wheel and rail can also be considered. The stiffness matrix and mass matrix of system need to be updated at each sub step of the calculation. Its advantage is that it does not need to consider the Hertz spring, so as to realize the large step calculation, and its disadvantage is that the wheel-rail separation cannot be considered. The other model is the \"wheel-rail contact state method\" [30; 31]. This coupling method considers the real wheel-rail contact: the vehicle and track are coupled together through wheel-rail force. There is no need to update the matrix in the calculation process. The advantage is that it can accurately reflect the wheel-rail contact state. The disadvantage is that it needs to search the wheel-rail contact points, resulting in low computational efficiency, and the Hertz spring needs to be considered, resulting in a small integration step. To accurately reflect the wheel-rail contact state, the \"weak coupling\" model will be applied. The normal force of the wheel-rail is calculated by Hertz spring, and the creep force of the wheel-rail is calculated by the Kalker model. More details of modeling can be found in Refs. [14; 32].  
The dynamic equation of the vehicle-subway structure coupled system model (VSS-CSM) can be written as:  
\[\left\{\begin{array}{l}\mathbf{M}_{\mathrm{p}}\ddot{\mathbf{X}}_{\mathrm{p} }+\mathbf{C}_{\mathrm{p}}\dot{\mathbf{X}}_{\mathrm{p}}+\mathbf{K}_{\mathrm{p} }\mathbf{X}_{\mathrm{p}}=\mathbf{F}_{\mathrm{p}}\\ \mathbf{M}_{\mathrm{i}}\dot{\mathbf{X}}_{\mathrm{t}}+\mathbf{C}_{\mathrm{i}} \dot{\mathbf{X}}_{\mathrm{t}}+\mathbf{K}_{\mathrm{i}}\mathbf{X}_{\mathrm{t}}= \mathbf{F}_{t}\end{array}\right. \tag{19}\]  
Then, Equation (19) is solved by moving the position of the wheel set on the element and combined with the step-by-step integration method.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 2 State of the Art  
The most widespread use of PLC technologies is in smart metering [6; 7]. In practice this means, e.g., downloading data from electrometers, communication between distribution transformer stations [8], etc. BPLC technology is today considered, e.g., in automotive application areas [9]. Another case is home use where BPLC is included as a primary or secondary communication channel. In most cases it is the BPLC plug-in adapter which can replace high-speed connection, i.e., Wi-Fi routers in domestic use. The ever-evolving field of smart metering, smart-grid and smart city areas requires an even more advanced system with high precision monitoring, if possible. Restrictions in BPLC technology do not allow such options compared with other technologies. It can be expensive to perform a test implementation of the system without guaranteeing the required functionality because of BPLC's disadvantages, such as the high channel variability in different conditions. Therefore, it is necessary to develop advanced simulation systems to determine the performance of the communication technology.
## 4 Global Error Model Verification by Numerical Simulations
### Summary  
The results presented in Sections 4.1 and 4.2 demonstrate that when the absolute value of the measurement deviation was less than \(5^{\circ}\) ( \(\mid\Delta A_{\rm s}\mid\,\leq 5^{\circ}\), \(\mid\Delta h_{\rm s}\mid\,\leq 5^{\circ}\)), the proposed error model's accuracy was validated. This also confirmed the correctness of the conditions under which the proposed error model was valid.  
Conversely, when the absolute value of the measurement deviation exceeded \(5^{\circ}\) ( \(\mid\Delta A_{\rm s}\mid\,>5^{\circ}\), \(\mid\Delta h_{\rm s}\mid\,>5^{\circ}\)), the proposed error model's accuracy decreased.
## 4 Discussion
### Daily Correlations between Solar Induced Fluorescence  
In this study we identified strong linear relationships between time resolved measurements of leaf PAR, SIF and photosynthesis in both plant species. Similar measurements performed by [PERSON] et al. [8] identified correlations between SIF\({}_{\text{FR}}\) and PAR and \(\phi\)PSII, but found no unique relationship between the PAM measurements and PAR. We suggest that this may result from using reflectance and global PAR measurements, which were not able to accurately approximate the leaf-level PAR. We identified significant correlations between SIF and global PAR for avocado and orange jasmine leaves. Nonetheless, this relationship was of poor quality (R\({}^{2}<\) 0.15; Table 1), which suggests that global PAR may be a poor proxy of true leaf PAR, at least in our situation, where leaves were oriented vertically in an east facing direction. Additionally, multiple regression consistently identified leaf PAR as a main predictor of SIF for both plant species, suggesting that collinearity between leaf PAR, photosynthesis and SIF is the main driver of correlations between SIF and photosynthesis within daily measurements.  
To the best of our knowledge, no study has examined the relationship between APAR partitioning parameters (YPSII, YNO and YNPQ) and SIF and YSIF. We identified statistically significant correlations between SIF and YSIF with measurements of YPSII, YNPQ and YNO, and also global and leaf PARs (see supplementary material; Table S5). Correlations between SIF and YSIF with PAM measured F\({}^{\prime}\) and \(\phi\)PSII or YPSII have been reported in both herbaceous plants and conifers [7; 21; 43]. However, the poor quality of relationships identified between YSIF and APAR partitioning parameters in this study suggests that further investigation may be required. Between the two species examined, correlations were of a lower significance for orange jasmine leaves. A strong waxy layer on the top adaxial side of orange jasmine leaves likely contributed to this by causing specular reflections and reducing the amount of absorbed excitation energy from LIFT flashes thereby increasing measurement errors. The direction of correlations between SIF and photosynthetic measurements were consistent with published correlations between SIF and \(\phi\)PSII and F\({}^{\prime}\) (e.g., [7; 8]) and were congruent with expected changes in YPSII, YNPQ, YNO and ETR under increasing leaf PAR.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 3 Classification of Phishing Detection Strategies
### ML Techniques  
ML techniques, particularly supervised learning models, have significantly advanced phishing detection. These models learn from labeled datasets to classify emails or URLs as malicious or benign. Common algorithms include the following:  
* Decision Trees: Construct a model based on splitting data by key features;
* SVMs: Identify optimal hyperplanes that separate phishing from legitimate samples [32];
* Neural Networks: Model complex feature interactions, particularly effective in detecting sophisticated phishing attacks;An example of a mathematical framework is shown below.  
For supervised learning, the objective is to find a mapping function:  
\[:X\to Y\text{ where }:\ Y=f(X)+\epsilon\]  
where the following variables are used:  
\(X\) represents the input features (e.g., URL length and sender info).  
\(Y\) is the output label (phishing = 1; benign = 0).  
\(\epsilon\) is the error term.  
The model is trained to minimize the loss function, commonly the binary cross-entropy loss for classification tasks:  
\[L(y,\hat{y})=-[y*\log{(\hat{y})}+(1-y)*\log(1-\hat{y})]\]  
where the following variables are used:  
\(y\) is the true label.  
\(\hat{y}\) is the predicted probability.  
Drawbacks include the following:  
Machine learning ML ng models using this approach typically require large, labeled datasets for training. They are prone to concept drift and have difficulty adapting to emerging phishing techniques without frequent retraining.
## 2 Materials and Methods
### Quantification of Total Phenolic Content  
The total phenolic content of the extracts was estimated with the FolinCiocalteuPhenol Reagent (Sigma-Aldrich, Hamburg, Germany). Crude extracts were dissolved in methanol and water (70% \(v/v\)). The experiment took place in the dark, and the procedure followed the description in our previous study [24]. Extracts were incubated for \(1^{1/2}\) h and their absorbance was read at 765 nm. Quantification of phenolic compounds was performed using a standard curve of gallic acid monohydrate (Riedel-de Haen AG, Seelze, Germany) at concentrations ranging from 100-800 ug/mL. Results were expressed as mg of gallic acid equivalents (GAE) per gram of dry plant material using the equation derived from the plot: y = 0.0011x + 0.0148 (r = 0.999, \(n\)=3).
# Fragility Analysis Based on Damaged Bridges during the 2021 Flood in Germany
###### Abstract  
Floods trigger the majority of expenses caused by natural disasters and are also responsible for more than half of bridge collapses. In this study, empirical fragility curves were generated by referring to actual failures that occurred in the 2021 flood in Germany. To achieve this, a calibrated hydraulic model of the event was used. Data were collected through surveys, damage reports and condition ratings from bridge owners. The database comprises 250 bridges. The analysis revealed recurrent failure mechanisms belonging to two main categories: those induced by scour and those caused by hydraulic forcing. The severity of the damage was primarily dependent on the bridge typology and, subsequently, on the deck's weight. The analysis allowed us to draw conclusions regarding the robustness of certain bridge typologies compared to others for a given failure mechanism. The likelihood of occurrence of the triggering mechanism was also highlighted as a factor to consider alongside the damage probability. This study sheds light on existing vulnerabilities of bridges to river floods, discussing specific areas in which literature data are contradictory. The paper also strengthens the call for a shift towards a probabilistic approach for estimating hydraulic force in bridge design and assessment.  
fragility curve; flood; scour; hydraulic force; bridge
The main simulation optimization methods include the 6-DOF dynamic mesh simulation method (where the impeller speed (\(n\)) is not an input parameter) [25; 26] and the fixed n-value mesh motion simulation method (where \(n\) is an input parameter) [27; 28]. The latter method has lower simulation difficulty and higher simulation accuracy. For low-head hydropower stations, [PERSON] et al. [29] used the mesh motion simulation method to analyze the output power and pressure variation on the blade surface of an AFT for five different blade angles (10.1\({}^{\circ}\)-18.1\({}^{\circ}\)). The results show that the optimal flow guiding effect occurred at a 10.12\({}^{\circ}\) blade angle, increasing the AFT output power by 4.4%. Researchers have used CFD simulations combined with RBF neural networks to establish a mapping relationship between turbine efficiency and structural parameters and then applied the NSGA-II genetic algorithm for multi-objective optimization. After optimization, the pressure, flow velocity, and turbulent kinetic energy flow fields inside the turbine significantly improved, and the energy conversion efficiency increased by 5.7% [30; 31]. Moreover, AFTs also serve as the main components of hydraulic turbines in petrochemical industries and urban high-rise water supply systems [28]. By optimizing the geometry of the blades, the energy conversion efficiency of the AFT can be improved by 1.2% [32]. In this study, we develop a hydrodynamic fertilizer injector (HFI) based on an axial-flow turbine (AFT), which uses an AFT to convert the kinetic energy of water to mechanical energy and then generates negative pressure through a diaphragm pump to achieve fertilizer injection. Drawing from optimization methods for AFTs used in hydraulic engineering and hydraulic turbine devices, we combine hydraulic performance testing with CFD numerical simulations. The focus is on structural parameters such as the number of impellers, the number of blades on each impeller, and the arrangement of impellers. We explore the response patterns of the output power and cavitation resistance to these impeller structural parameters. Additionally, the internal flow field distribution characteristics of the AFT were analyzed, and the structure of the AFT was further optimized from the perspective of negative pressure cavitation. The optimized design will undergo prototype fabrication and fertilization performance testing. These results provide a basis for the development of hydraulic fertilization injectors.
## 4 Conclusions  
The primary aim of this study was to investigate the application of coagulation-flocculation as a treatment method for real BWW containing a high amount of organic substances and nutrients. In order to assess the effectiveness of coagulation and flocculation as a pre-treatment, the concentration of COD, BOD\({}_{5}\), N\({}_{\text{tot}}\), P\({}_{\text{tot}}\), and P-PO\({}_{4}\)\({}^{3-}\) have been monitored. The results indicated that the most effective coagulant and flocculant were PAC (dosage: 675 mg L\({}^{-1}\)) and AAF (dosage: 40 mg L\({}^{-1}\)), respectively. The mean removal efficiencies for BOD\({}_{5}\), COD, TSS, N\({}_{\text{tot}}\), P\({}_{\text{tot}}\), and P-PO\({}_{4}\)\({}^{3-}\) were 34.3%, 26.5%, 86.8%, 51.8%, 95.5%, and 95.5%, respectively. The elevated temperature of the BWW may have hindered the assimilation of sufficient oxygen into the water, reducing the sedimentation efficiency. The separated sludge had a DM content ranging from 4.5% to 5%. Following the chemical/flocculation treatment of the BWW, approximately 1.01 kg\({}_{\text{DM}}\) of chemical sludge was released per kilogram of chemical oxygen demand removed. This data will be helpful for both the scientific community and technical operators, providing information about (i) the effectiveness of the pre-treatment of BWW before discharge into the sewer system, and (ii) the amount of chemical sludge produced during the treatment of this type of industrial WW. These results can be useful for evaluating the applicability of this treatment to other specific contexts, sizing the plant, and considering the most advantageous solution from an economic and environmental point of view for the disposal or valorization of chemical sludge.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15062999/sl](https://www.mdpi.com/article/10.3390/app15062999/sl)]([https://www.mdpi.com/article/10.3390/app15062999/sl](https://www.mdpi.com/article/10.3390/app15062999/sl)), Figure S1: The treatment plant used in industrial-scale tests; Figure S2: Details of the flocculation unit in the industrial-scale plant; Figure S3: A sample of raw WW before chemical pre-treatment (left) and a sample after coagulation/flocculation process (right); Table S1: Temperature, pH, and conductivity in the inlet and outlet and consumption of chemicals of the industrial-scale plant.  
Conceptualization, M.R., P.H. and J.T.; methodology, M.R. and P.H.; validation, M.R. and P.H.; formal analysis, M.R., P.H. and J.T.; investigation, J.T.; resources, M.R. and P.H.; data curation, M.C.M. and J.T.; writing--original draft preparation, M.R., P.H. and J.T.; writing--review and editing, M.C.M. and V.T.; visualization, M.C.M. and V.T.; supervision, M.R. and P.H.; project administration, M.R. and P.H.; funding acquisition, M.R. and P.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
The authors declare no conflicts of interest.
## 3 Performance Evaluation Methods
### System Coverage Rate  
The system coverage rate, which represents the sensor networks' monitoring performance to the detecting region, is the most significant criterion for evaluating sensor networks in practical applications. Higher system coverage indicates that, if the sensor network in the target region is more comprehensive, the less vital data will be lost, which allows scientists to obtain a better network for the target area.  
In order to evaluate the coverage effect of the final topology deployed by our 3D algorithm, we use the Monte Carlo algorithm to calculate the system coverage rate following by below steps, as shown in Figure 1.  
(1) Set node \(A\) to be the sensor node farthest from the center of the sphere region when the network approaches the equilibrium state, and define the distance between the two nodes as \(R\). If the sensing radius of a single node is \(R_{S}\), the radius of the overall coverage area is \(R_{a}=R+R_{S}\) and the corresponding entire coverage area should be \(V_{a}=(4/3)\pi R_{a}^{3}\).  
(2) Use the Monte Carlo algorithm to randomly select one sensor node and make the circumscribed cube of \(V_{a}\) region. We take three random values \(X,Y,Z\) from the interval \([-R_{a},R_{a}]\) to obtain a three dimensional point \((X,Y,Z)\). It is necessary to determine whether this point is within the coverage area by checking the condition \(X^{2}+Y^{2}+Z^{2}<=R_{a}^{2}\). If this condition is satisfied, this test point will be chosen. Otherwise, we do the selection process again according to the above rules.  
(3) Depending on the current position \((x_{i},y_{i},z_{i})\) of node \(i\), we mark the point within the range of a sensor if \((x_{i}-X)^{2}+(y_{i}-Y)^{2}+(z_{i}-Z)^{2}\leq r^{2}\).  
(4) According to the number of test points, \(p_{t}\), we can repeat procedures (2) and (3) for \(p_{t}\) times and finally obtain the number of all marker points, which can be recorded as \(p_{m}\). Therefore, the system coverage rate will be evaluated by \(C=p_{m}/p_{t}\).  
It should be noted that the Monte Carlo algorithm is only used to evaluate the system coverage rate. It is not a part of VFA-DP-3D algorithm to deploy the sensor node (with the GPS and UAVs). The Monte Carlo algorithm will help the users to check if the 3D network coverage rate is good enough or not at the beginning or any time step of the network deployment process, which can ensure that the final network can satisfy the user's requirements.
# Radiation Damage of Polydimethylsiloxane and Polyimide by X-ray Free-Electron Laser
## 1 Introduction
The fundamental [-OSi(CH\({}_{3}\))\({}_{2}\)-]n structure in PDMS has intrinsic hydrophobic properties [39]. The plasma immersion ion implantation technique can enhance the hydrophilicity and biocompatibility of PDMS [40]. PDMS is an inexpensive rubbery elastomer with excellent optical clarity [41]; it can be structured to any desired shape, thickness, and size using a soft lithography technique based on replication molding of micromachined molds [41]. Because of these advantages, PDMS has been widely used as a sample delivery material in X-ray research [42; 43]. PI is a high-performance polymer with excellent temperature stability, solvent resistance, radiation resistance, and mechanical strength [44; 45]. Because PI produces considerably low background scattering when exposed to X-rays [25], which does not affect data processing, it has been widely used as a sample delivery material in serial crystallography (SX) studies [34; 46; 47] and for crystal mounting loops in macromolecular crystallography [48]. In this study, we investigate the radiation damage to PDMS and PI caused by XFEL. We analyze the radiation damage to films and microfluidic devices exposed to XFEL. Our results expand our knowledge of XFEL-induced radiation damage and provide insight into the direction of the development of sample delivery systems for SFX experiments and XFEL science.
## 3 Technology, Hardware and Software of Edge Computing
### The Technology of Edge Computing
#### 3.1.2 Container Technology  
Container technology is a virtualization method used to increase the portability and efficiency of applications. This technology enables applications to run in an isolated environment with all their dependencies, accelerating software development and deployment processes and optimizing resource usage. Containers are widely preferred in modern infrastructures such as microservice architectures, CC, and EC.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 2 The Proposed Method  
### The Framework of the Proposed Method  
The detection flow is shown in Figure 1. Firstly, the pole mask obtained by the pole image segmentation is fused with the depth map to generate a frustum, and then it is sent to the point cloud segmentation model to obtain the pole point cloud. Finally, piecewise fitting is used to obtain the central axis of poles and calculate the inclination angle of the poles. During segmenting poles, in order to avoid background interference such as cross arms, insulators, and wire on the poles, only the pole skeletons are marked when labeling, but the accuracy of pole recognition is low. Therefore, the mask bounding box of Mask R-CNN is modified to contain more feature information, and the convolutional block attention module (CBAM) attention mechanism is added to the Mask R-CNN head network, which together improve the accuracy of pole segmentation. Aiming at the incomplete cylinder-like [28] point cloud of the outer contour of poles, this paper proposes a segmented processing method. In the height direction, each small segment of the intercepted point cloud is approximately processed into a cylinder and fitted using the Random Sample Consensus (RANSAC) algorithm. The detection method makes full use of the feature that the image can efficiently locate the pole object in complex scenes and the point cloud contains depth information. It can not only detect the inclination angle of poles in any direction at one time, but also reduces the difficulty of point cloud processing, improves the detection accuracy of the pole inclination angle, and provides the possibility for automatic patrol inspection.