esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Calculation of Fault Dislocation
### Model Assumptions and Calculation Parameters  
The calculation assumed the subgrade and its lower foundation had an isotropic homogeneous body. The initial displacement and stress fields caused by the gravity of the subgrade and foundation had to be considered. The linear elastic model was adopted for the bedrock. The strain softening Mohr-Coulomb model was adopted for the subgrade and foundation. The specific calculation parameters are shown in Table 2.  
In the strike-slip fault dislocation calculation stage, the velocity excitation was applied to the \"yellow\" unit in Figure 17. The fracture was an important index for evaluating the sustainability of the subgrade. By monitoring the change in the combined damage factor, the subgrade and its lower foundation fracture induced by a fault dislocation were analyzed. The combined damage factor comprises shear and tensile damage factors. The calculation formulas for the three damage factors are as follows:  
\[p=1-\frac{c_{n}}{c_{0}} \tag{1}\]  
\[q=1-\frac{\sigma_{n}}{\sigma_{0}} \tag{2}\]  
\[r=1-(1-p)\times(1-q) \tag{3}\]  
where \(p\) is the shear damage factor, \(q\) is the tensile damage factor, \(r\) is the combined damage factor, \(c_{0}\) is the initial cohesion of the element, \(c_{n}\) is the current cohesion of the element, \(\sigma_{0}\) is the initial tensile strength of the element, \(\sigma_{n}\) is the current tensile strength of the element. When the combined damage factor of the element is 1, it means that the element has been destroyed and the crack has occurred.
## 3 Identification of QBIE Temperature Events
### Defining QBIE Temperature Events  
A QBIE event is defined when the 10-60-day bandpass filtered anomaly exceeds two times its standard deviation (\(\sigma\)), as carried out in most research on climate and weather extremes [14]. It is important to note that the \(\sigma\) of the 10-60-day filtered anomaly varies across seasons (Figure 3). Our results indicate that, across all stations, the QBWO and ISO signals exhibit the largest variability during February and March, with \(\sigma\) values approximately three times larger than those in August and September. The large seasonal differences in \(\sigma\) suggest that a fixed \(\sigma\) should not be used to define QBIE temperature events.  
To address this, we calculate a 31-point smoothed daily \(\sigma\) based on the period 2006-2022 Equation (3) and take it as the threshold for defining QBIE temperature events (Figure 3). If the filtered anomaly data are greater than +2\(\sigma\), then this is classified as a positive QBIE temperature event, or a QBIE heat wave. Conversely, if the filtered anomaly is smaller than \(-2\sigma\), then it is considered as a negative QBIE temperature event, or a QBIE cold surge. Finally, the starting and ending dates of a QBIE event are determined by the nearest points where the filtered anomalies cross the zero line. Below is the explicit procedure for identifying QBIE events:  
\[\sigma(d)=\sum_{y=2006}^{2022}\sum_{d=-15}^{15}\frac{filtered(y,d)}{N} \tag{3}\]  
Figure 3: Seasonality of standard deviation (\(\sigma\), unit: \({}^{\circ}\)C) of 10–60-day bandpass filtered daily minimum (blue), mean (green), and maximum (red) temperature anomalies of (**a**) HKO, (**b**) PEN, (**c**) SLW, and (**d**) WLP stations during the period of 2006–2022.  
Based on the above procedures, we identified all QBIE heat wave and QBIE cold surge events based on the 10-60-day bandpass filtered maximum, minimum, and mean temperature anomalies from all 14 stations. The longest record of the constructed QBIE events is derived from the HKO station observations, covering the period from 1885 to 2022. Figure 4 gives an example of six QBIE heat waves and four QBIE cold surges observed by the HKO station in 2010. For example, a QBIE heat wave started on 9 October, peaked on 21 October, then ended on 24 October (Event #9 in Figure 4).
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 6 Results  
The proposed solution demonstrated a high degree of reliability in detecting and mitigating various phishing attacks, including the Zero-Font technique. Extensive evaluations across multiple test scenarios consistently yielded high detection accuracy, underscoring the effectiveness of the tool in protecting individuals and organizations against phishing-related threats such as data breaches, credential theft, and financial loss.  
This performance is largely attributed to the multi-layered architecture of the system, which integrates heuristic-based techniques with machine learning algorithms. By analyz  
Figure 10: Linear SVM confusion matrix.  
Figure 9: Random forest confusion matrix.  
ing subtle and often obfuscated indicators, such as metadata, headers, embedded URLs, and textual features, the system effectively identified and flagged phishing attempts. Once detected, these emails were automatically redirected to the user's junk folder, eliminating the need for manual filtering. Furthermore, a real-time alert mechanism promptly notified users of potential threats, reducing the likelihood of engagement with malicious content and minimizing the risk of data exposure.  
Importantly, the system maintained a low false positive rate, ensuring that legitimate emails were not incorrectly flagged. This balance between high detection accuracy and practical usability reinforces the suitability of the proposed solution for real-world deployment. In general, the results demonstrate that the approach provides robust and proactive defense against a wide range of phishing techniques, positioning it as a promising enhancement to existing cybersecurity systems.  
Table 9 presents a comparison between the proposed method and other supervised learning algorithms. Although our solution incorporates additional layers beyond supervised learning, the classification performance of the integrated machine learning models can still be benchmarked against these established algorithms.
# Nanosponges for Water Treatment: Progress and Challenges
###### Abstract  
Nanosponges have shown promising capabilities for efficient removal of organic/inorganic pollutants from water based on absorption/adsorption and disinfection processes. The application of nanosponges (especially cyclodextrin-based nanosponges) can be considered a cost-effective strategy with minimal energy and time requirements in comparison to other routinely deployed water treatment modalities. These polymers with unique physicochemical properties, architectures, and highly cross-linked three-dimensional networks need to be further explored for removing pollutants with simultaneous eliminations of microbial contaminants from wastewater. Additionally, the surface functionalization of these nanosponges utilizing magnetic, titanium dioxide, and silver nanomaterials can significantly improve their properties for water remediation purposes, although nanosponges altered with carbon nanotubes and metallic nanomaterials/nanocatalysts for water treatment appliances are barely explored. Notably, crucial factors such as adsorbent type/dosage, contact time, competing ions, adsorption isotherm models, kinetics, thermodynamics, and reaction/experimental conditions (e.g., molar ratios, temperature, and pH) are important aspects affecting the adsorption and removal of pollutants using nanosponges. Furthermore, the nanotoxicity and biosafety of these nanosponge-based systems utilized for water treatment should be comprehensively evaluated. Herein, recent advancements in the design and deployment of nanosponge-based systems for removing organic/inorganic pollutants from water and wastewater are deliberated with an emphasis on challenges and perspectives.  
nanosponges; water treatment; pollutants; nanosponge-based systems; cyclodextrin-based nanosponges 2022
## 2 Materials and Methods
### Resin Disc Specim Production
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Commercial Name** & **Composition** & **Lot No.** & **Manufacturer** \\ \hline Meliodent pink & Powder (polymethylmethacrylate) & & \\ (Heat-polymerized & Liquid (methylmethacrylate, dimetha & KO10062 & Kulzer Gmbh, \\ Denture Resin) & crylate) PMMA & & \\ \hline Denta Base pink (Hard & PMMA resin & & \\ 3D-printed resin for & 7,7,9(or 7,9,9)-trimethyl-4,13-diox-3,14-diox- & & \\ 3D-printed resin for & 5,12-diazahexadecane-1,16-diyl bismethacrylate; & Not available & ASIGA Pty Ltd., \\ denture base) & Diphenyl[2,4,6-trimethylbenzoyl] phosphine oxide; & & \\ Tetrahydrofurfuryl methacrylate & & \\ \hline Flexo Denture Base V2 & Urethane dimethacrylate UDMA & & \\ pink (Soft & 2-hydroxyethylmethacrylate and 2,4, & \\
3D-printed resin) & 4-trimethyl-hexamethylenediisocyanate-UDMA & SNR202300021 & Senertec 3D Yazici, \\  & (according to the manufacturer’s information, sent & \\  & by mail) & & \\ \hline Lipton Black Tea; & & L4 076 1 J3 01 & Ekaterra global Operations \\ Origin, Kenya & Black tea 100\% & L4 076 2 J3 01 & BV, Rotterdam, \\  & & L4 076 3 J3 01 & The Netherlands \\ \hline Jacobs Kronung & & & Jacobs Douwe Egberts, DK \\ (filtered coffee) & Ground coffee grains & LK00942063 01 & Amsterdam, The \\  & & & Netherlands \\ \hline Akres 2023 (dry red wine) & Alcohol 14\% vol. & L 240221 & Produced and bottled by \\  & & & Skouras Winery, \\  & & & Argos, Greece \\ \hline Coke® Original Taste & Water, Sugar, Carbon dioxide, Caffe, & & \\ (Coca-Cola) & phosphoric acid, Other Aromatic substances & 20B43E29B & Coca-Cola HBC Cyprus, \\  & & & Nicosia, Cyprus \\ \hline Mallinckrodt Silica gel & Silica gel & 4000834 & Mallinckrodt Chemical \\  & & & Works, St. Louis, MO, USA \\ \hline KMG POLISH LIQUID & Alcohols, C-16-18, ethoxylated Aquatic Acute 1, & & \\ H400; Aquatic Chronic 3, H412 2.5 & Z02 SNB & CANDULOR AG, Clattpark \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical details of the materials used. All specimens' dimensions, especially thickness, were verified with a micrometer (measurement accuracy: 0.1 mm) and were stored in distilled water in room temperature (23 \({}^{\circ}\)C). The ninety (90) specimens were randomly separated into five different groups per denture base polymer material. Fifteen subgroups were thus produced, with six specimens in each one (Table 2).
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 2 Related Work
The modular design provides flexibility, scalability, and ease of maintenance, as each module can be updated or replaced without affecting the rest of the system. Scalable cloud solutions are required to manage the enormous and heterogeneous amounts of data generated by road traffic systems, including standard ETSI C-ITS messages, such as CAM, Decentralized Environmental Notification Message (DENM), Infrastructure to Vehicle Information Message (IVIM), Signal Phase and Timing Extended Message (SPATEM), Collective Perception Message (CPM), and more, as well as other types of information, such as Local Dynamic Maps (LDMs), High Definition Maps (HD-Maps), raw sensor data, video streams, among many others. Geocasting techniques, such as the ones implemented in the C-MobILE project [17], simplify the process of linking all road users, including vehicles, service providers, and traffic management centers (central C-ITS stations). These techniques are based on the same MQTT geographical tiling scheme mentioned earlier. Connected and Automated Vehicles (CAVs) can rely on additional sources of data for extending their fields of view and having more detailed information about their surrounding environment. This is made possible by the C-ITS infrastructure, incorporating devices ranging from roadside sensors and communication units to cloud C-ITS components. As a result, CAVs can make more informed decisions or even delegate some of those decisions to the roadside or cloud infrastructure, optimizing traffic speed and safety features. The AUTOPILOT (automated driving progressed by Internet Of Things) project [20] is an example of such a deployment, as it uses the IoT ecosystem to integrate connected cars and transform them into automated moving objects. It also uses interoperable M2M platforms to connect all parts of the traffic system. Recently, other communication technologies have been tested to deliver C-ITS services even in challenging environments, such as cross-border regions [21]. These Cooperative, Connected and Automated Mobility (CCAM) applications are expected to benefit from the next generation of mobile networks (5G), providing high reliability and availability, high bandwidth, and low end-to-end latency. Therefore, it is critical to plan and create a roadside and cloud architecture that is independent of the underlying communication systems used to exchange data among the many traffic agents.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 3 The Proposed System Methodology
### Feature Extraction
#### 3.3.3 Fused Intra-Inter Trajectories  
We propose \"fused intra-inter depth silhouette localized point trajectories\" for the first time for human head tracking and counting. This type of trajectory method contains a subset \(X\), containing a set of human joints which gives localized points to form trajectories. First, a subset \(X\) containing five localized points is plotted on human the depth silhouette s, i.e., \(X=\{\)_head_, _neck_, _left-shoulder_, _right -shoulder_ and _chest_\(\}\). The number of localized points varies according to the number of human depth silhouettes \(N\) in each frame i.e., \(N=\{n_{1},\ n_{2},\ \ldots,\ n_{\infty}\}\). These localized points are then joined in the form of trajectories. This results in four trajectories for each human silhouette. These four trajectories are  
Figure 5: Results of fused joint HOG-LBP over the PCDS dataset where **(a)** shows the original depth image, **(b)** shows the HOG results and **(c)** shows the LBP results.  
Figure 6: Energy based point cloud results obtained over frames of the PCDS dataset.  
represented as \(T=\{HN,NRS,\ NLS,\ NC\}\) where \(HN\) is the trajectory between head and neck, \(NRS\) is the trajectory between neck and right_shoulder, \(NLS\) represents the trajectory between neck and left_shoulder and at last \(NC\) represents the trajectory between neck and chest. Figure 7 shows the fused into-inter silhouettes trajectories over the MICC dataset.  
After the formation of all the trajectories, shape descriptors are extracted which can calculate the displacement of the changes in length \(L\) in each silhouette, frame by frame, during the time \(t\) along \(x\) and \(y\) coordinates. This change during time t can be measured using Equation (12) and the normalized displacement vector can be measured using Equation (13) [33];  
\[\Delta p_{t}=(x_{t+1},-x_{t},y_{t+1}-y_{t}) \tag{12}\]  
\[d_{x,y}=\frac{(\Delta p_{1},\ \Delta p_{2},\dots,\Delta p_{T-1})}{\sum_{i-1}^{T-1 }||\Delta p_{i}||} \tag{13}\]
# Cyclodextrin Polymers as a Promising Drug Carriers for Stabilization of Meropenem Solutions
## 3 Results and Discussion
### The Influence of HPCD Polymers on MP's Stability in Aqueous Media  
Due to the presence of a \(\beta\)-lactam ring in the drug's structure (Figure 1), MP degrades in aqueous solution. The chemical changes in MP's pharmacophore causes the loss of its antibacterial activity [20; 36; 37]. MP's instability in water is even visually noticeable, since the solution changes its color from transparent to brown during storage.  
MP's state can be successfully monitored using UV-spectroscopy. Freshly prepared MP solution (fMP) demonstrates the peak at 297 nm (Figure 6), whereas after 2 months of storage (sMP), we observed two low-intensity peaks at 269 and 333 nm. Consequently, MP's degradation rate can be tracked by the intensity and the position of the initial peak.  
Figure 5: Experimental PXRD patterns for MP, HPCD-CA-MP, HPCD-SA-MP, and HPCD-HMD-MP. No more signals were detected from 55\({}^{\circ}\) to 80\({}^{\circ}\).  
The UV's peak of fMP decreases after 5 h of storage at room temperature and the decline occurs by 35% in 9 days (Figure 7). MP-HPCD polymer complexes were also studied to uncover how particles affect MP's stability. In the case of HPCD-CA-MP, we determined a significant acceleration of the drug's degradation; MP's peak intensity decreased dramatically after a few hours (3 times in 5 h and 4 times in 3 days).  
In contrast, for HPCD-SA-MP and HPCD-HMD-MP a positive effect on the drug's stability was observed during the entire time interval. MP's stability increases for 1.2 and 1.4 times in 9 days for HPCD-SA-MP and HPCD-HMD-MP, respectively, compared to the free MP. Unlike HPCD-CA, these polymers have positive and neutral charges. Consequently, the negative charge of the carrier accelerates MP's degradation, possibly due to environmental acidification.  
Unexpectedly, HPCD-HMD-MP\({}_{\text{im}}\) with imprinted drug molecules demonstrated a similar effect on MP's stability that was found for HPCD-SA-MP and HPCD-HMD-MP. The probable reason is the hydrophilic structure of the MP due to which it is involved mainly in electrostatic interactions. Since HMD forms hydrophobic pores in the polymer, electrostatic interplay is the same for imprinted and encapsulated drug-carriers.  
Throughout the experiment of MP's stability, during the storage the samples were also studied by FTIR spectroscopy. As discussed above, the 1613 cm\({}^{-1}\) peak might be  
Figure 6: UV-spectra of freshly prepared MP solution (fMP) and the solution, stored for 2 months (sMP), C\({}_{\text{MP}}\) = 0.1 mM.  
Figure 7: The diagram of MP’s spectrum intensity at 297 nm for MP, HPCD-CA-MP, HPCD-HMD-MP, and HPCD-HMD-MP\({}_{\text{im}}\), T = 22 \({}^{\circ}\)C.  
sensitive to MP's degradation. To prove this statement, we obtained FTIR spectra of MP during 9 days (Figure 8). Indeed, the band's intensity decreased dramatically during MP's storage. Thus, the analysis of this effect for MP-HPCD polymers is informative for uncovering MP's degradation extent. The FTIR data proved all trends that were obtained by UV-spectroscopy.
## 3 Statistical Properties
### Reliability Measures  
The reliability and hazard functions of any lifetime equipment are the fundamental tools for analyzing aging and associated features. The conditional probability of failure, assuming it has lasted up to time \(x\geq 0\), is represented by the hazard function. The hazard, reversed hazard rate, and odds functions of the PZD are given, respectively, by  
\[H_{PZD}(x;\theta,\beta)=\frac{f_{PZD}(x;\theta,\beta)}{S_{PZD}(x;\theta,\beta)} =\frac{\beta\theta^{3}x^{2\beta-1}\big{(}x^{\beta}+1\big{)}}{\theta+\theta x^ {\beta}\big{(}\theta+\theta x^{\beta}+2\big{)}+2}, \tag{11}\]  
\[RH_{PZD}(x;\theta,\beta)=\frac{f_{PZD}(x;\theta,\beta)}{F_{PZD}(x;\theta,\beta )}=\frac{\beta\theta^{3}x^{2\beta-1}\big{(}x^{\beta}+1\big{)}}{(\theta+2)e^{ \theta x^{\beta}}-\big{(}\theta+2+\theta x^{\beta}\big{(}\theta+\theta x^{ \beta}+2\big{)}\big{)}}, \tag{12}\]  
\[O_{PZD}(x;\theta,\beta)=\frac{F_{PZD}(x;\theta,\beta)}{S_{PZD}(x;\theta,\beta )}=\frac{e^{x^{\theta\theta}}(2+\theta)}{2+\theta+x^{\beta}\theta\big{(}2+ \theta+x^{\beta}\theta\big{)}}-1. \tag{13}\]  
Plots of the hazard, reversed hazard rate, and odds functions of the PZD are illustrated in Figure 5, Figure 6, and Figure 7, respectively.  
Mean residual life (MRL) is crucial in demography, reliability, and survival analysis. The mean residual life function of PZD is defined as  
\[MRL_{PZD}(x;\theta,\beta) =E(X-x|X>x)=\frac{1}{1-F_{\text{PZD}}(x;\theta,\beta)}\underset{x}{ \overset{\infty}{\int}}(1-F_{\text{PZD}}(t;\theta,\beta))dt \tag{14}\] \[=\frac{\rho^{\theta\theta}\left[\left((\theta+2)\Gamma\left(1+ \frac{1}{2},\theta\theta\right)+\Gamma\left(2+\frac{1}{2},\pi^{\theta}\theta \right)+(\theta+2)\Gamma\left(\frac{1}{2},\pi^{\theta}\theta\right)\right) \right]}{\beta\theta^{1/\beta}\left[\theta+\theta\pi^{\theta}\left(\theta+ \theta\pi^{\theta}+2\right)+2\right]}.\]  
MRL plots of the PZD for some particular parameters are offered in Figure 8. This figure reveals that the MRL for PZD is decreasing for these values with different behavior decreasing-constant \((\theta=8,\ \beta=0.6)\) and decreasing \((\theta=2,\ \beta=3)\).  
**Remark 1**.: _It is to be noted that \(MRL_{PZD}(0;\theta,\beta)=\mu=\frac{\beta(\theta+2)+1}{\beta(\theta+2)\theta^{1 /\beta}}\Gamma\left(2+\frac{1}{\beta}\right)\)._
## 1 Introduction
3. Studies related to the \(AST\) control system. There are few studies that analyse the control of Archimedes screws. [PERSON] et al. [25] developed several simulation models for small hydropower plant system using constant speed and variable speed generator operation modes, as well as a combination of equipment models based on manufacturer data, in situ measurements, and published data. The control system used is not mentioned in this work. Notwithstanding, there are no studies dedicated to the analysis of control systems in actual operating scenarios. To the best of the authors' knowledge, no research has been performed concerning the energy analysis of \(AST\)s operating under different control modes. The aim of the work presented in this paper is the experimental study of three control systems for an Archimedes screw turbine: (i) a water level control (\(WLC\)) system, (ii) the maximum power point tracking (\(MPPT\)) method, and (iii) an optimal water level control (\(OWLC\)) system. With a \(WLC\) system, a classic \(PID\) controller is used to set the height  
Figure 1: Diagram of a typical Archimedes screw turbine system. of the water level chosen at random. The maximum power point search method is used with an \(MPPT\) system, and the \(OWLC\) system is based on the successive application of the two previous systems. The three control systems have been evaluated at the same micro-hydropower plant. The main contributions of this work are as follows:  
1. An analysis to determine the control system that maximises electricity production;
2. An analysis to determine the control system that maximises the efficiency of the micro-hydropower plant;
3. An analysis to determine the control system that reduces mechanical stress on the gearbox;
4. The actual micro-hydropower plant measurements. The measurements collected in this study are derived from a micro-hydropower plant. Unlike previous studies that primarily focused on examining test models, this research investigates actual operational scenarios. While studying test models offers valuable insights into system performance, certain phenomena specific to real-world cases may not be captured through such research. This paper is structured as follows: Section 2 provides the overall structure of the control systems. Section 3 defines the specifications for the case study. The data acquisition system, the measurement devices and the accuracy of measurements are provided in Section 4. The assessment parameters are presented in Section 5. The results are presented in Section 6. Finally, Section 7 summarises the main contributions and conclusions of the paper.