esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 5 Discussion
### Predictive Modeling of XSS Vulnerabilities Based on Cookie Attributes  
A key innovation in this study was the incorporation of cookie attributes (e.g., expiration date, date of creation, domain, etc.) as variables for the machine learning models, differentiating this approach from existing methods that focus on script detection [55]. The models were trained using real student navigation data obtained in an academic environment, which allowed the identification of risk patterns specific to educational contexts [56].
## 2 Materials and Methods
### Analytical Methods  
Ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS) was used to quantify E1, E2, E3, EE2 and BPA in the solution simultaneously on a UPLC system (Quattro Premier XE, Waters, Milford, MA, USA). The mobile phase consisted of acetonitrile (A) and ultrapure water (B); the analysis conditions are listed in Table 2. The membrane surface was observed via scanning electron microscopy (SEM) (QUANTA 200, FEI, Hillsboro, OR, USA).  
The scanning ranges of the three-dimensional excitation-emission matrix (EEM) fluorescence spectroscope (F-7000, Hitachi, Japan) were excitation wavelength/emission wavelengths (Ex/Em)  
\begin{table}
\begin{tabular}{c c c} \hline
**Technique** & **Component** & **Conditions** \\ \hline \multirow{6}{*}{UPLC analysis conditions} & Chromatographic column & 90\% A 0 min\(\rightarrow\)64\% A 0.5 min\(\rightarrow\)maintained to 6 \\  & (100 mm \(\times\) 2.1 mm I. D. \(\times\) 1.7 \(\mu\)m) & min\(\rightarrow\)10\% 10 min\(\rightarrow\)returned to the \\ \cline{2-3}  & Temperature of columns & 50 \({}^{\circ}\)C \\  & Injection volume & 10 \(\mu\)L \\  & Flow rate of mobile phase & 0.3 mL-min\({}^{-1}\) \\ \hline \multirow{2}{*}{MS analysis conditions} & Ionization mode & ESI \\  & Temperature of ion source & 120 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 2: Analysis conditions of UPLC/MS/MS.  
Figure 2: Schematic diagram of ozone (**a**) and ultrafiltration unit (**b**).  
of 220-420 nm (step of 5 nm)/240-600 nm (step of 2 nm). After obtaining the fluorescence intensity corresponding to each Ex/Em, Origin8.5 was used to plot the contour map. UV-Vis spectroscopy (UV-2401 PC, Shimadzu, Kyoto, Japan) was used to scan the full wavelength range of 200-400 nm. Estrogenicity was determined based on a yeast estrogenicity screening assay [19; 20] and the result was expressed by the E2 equivalent concentration (EEQC). Acute ecotoxicity was assessed by determining inhibition of the luminescence of the marine Gram-negative bacterium _Vibrio fischeri_ with a rapid screening kit (JQ TOX-kit IV) purchased from Beijing Jinda Qingchuang Environmental Technology Co., Ltd. (Haidian District, Beijing, China) [21]. The bacteria were in freeze-dried form and activated by rehydration with a reconstitution solution. The light emission wavelength at 475 nm of this bacterium in contact with different samples for 30 min was measured, and bioluminescence inhibition was calculated.  
The removal efficiency of EDCs was used to evaluate the effect of O\({}_{3}\) degradation and UF rejection of EDCs; \(R(\%)=\frac{C_{0}-C}{C_{0}}\times 100\%\), where \(R\) is the removal rate of EDCs, \(C_{0}\) is the initial concentration of EDCs, and \(C\) is the residual concentration of EDCs in the solution.
## 1 Introduction  
High-temperature ceramics of yttria (Y\({}_{2}\)O\({}_{3}\))-doped zirconia (ZrO\({}_{2}\)) exhibit effective ion conductivities through substitutional solid solutions of low-valence cations [1]. Oxygen ions can be transferred in the solid phases along the oxygen vacancies formed in the zirconia-ytria composite lattice [2]. The cubic crystals are stabilized to restrict thermal transformations in the tetragonal and monoclinic crystals above a molar concentration of 8.0, as shown in the zirconia-ytria phase diagram [3]. The processed ceramic components of the yttria-doped zirconia linearly expand and contract with repetitive heating and cooling [4]. Solid electrolytes of yttria-stabilized zirconia (YSZ) have been developed for practical applications in solid oxide fuel cells (SOFCs) [5] and high-temperature oxygen analyzers [6].  
Recently, YSZ electrodes have been used in the molten salt electrolysis of aluminum smelting [7]. Metal anodes of mesh tubes coated with fine YSZ layers can attract oxygen ions in molten alumina, reducing the melting point by cryolite addition. The oxygen ions are absorbed at the surfaces and transferred through the electrolytes, and gaseous oxygen is formed at the mesh anodes and drained through the tube. The remaining aluminum ions are gathered at the cathodes, and the deposited metals can be refined using pure metals. Conventionally, carbon anodes are introduced into molten alumina, and oxygen is chemically separated as carbon dioxide. The use of YSZ electrodes for metal refining is a promising method to reduce greenhouse gases.  
In our previous studies, YSZ electrodes with three-dimensional (3D) structures were precisely fabricated by stereolithographic additive manufacturing (STL-AM) for practical applications of SOFCs [8]. Microlattice patterns with ordered pore arrangements were designed and created to increase the specific surface areas for effective gaseous reactionsand permeabilities. Photosensitive resins with ceramic nanoparticles have been used as printing inks in STL-AM [9]. A cross-sectional layer was formed on a spread slurry paste by laser drawing. A solid object was obtained via layer lamination and interlayer bonding in the lump of uncured paste. The formed composite precursor was dewaxed and sintered to create a full ceramic electrode [10].  
Here, YSZ microlattices with ordered porosities were processed as ceramic electrodes for effective aluminum smelting without discharging carbon dioxide. The coordination numbers and aspect ratios of the ceramic microlattices were spatially considered and optimized to systematically increase the molten salt permeabilities and specific surface areas by numerical simulations. The laser processing conditions in STL-AM were systematically optimized and progressively discussed. The dimensional accuracies and microstructural soundness of the ceramic lattices were measured and precisely observed after heat treatments.
## 3 Internal Contact Pressure Model Validation
### Formulation of the Finite Element Model  
As Figure 12 shows, according to different sources of contact pressure, the corresponding finite element models is established to investigate the distribution patterns and magnitudes of contact stress under different conditions.  
The 1 \(\times\) 7 wire model is established by generating the wire axis from a helix and then sweeping to create the wire surface. This approach is advantageous as it skips the process of transitioning from parallel to helical, saving computational time. The wire end is coupled to a reference point, and fixed constraints are applied to this reference point to anchor the wire end. In the analysis steps, a U2 displacement is applied to the reference point to simulate the stretching process after helical twisting, allowing the wires to make  
Figure 11: Superposition of the contact pressure at the contact point due to bending. (**a**) Superposition at the \(\langle 2\rangle\) contact point for (2,1) wire. (**b**) Superposition at the \(\langle 1\rangle\) contact point for (2,1) wire. The red asterisk indicates the contact point.  
Figure 12: Finite element model under three contact types: (**a**) 1 \(\times\) 7 wire twist model; (**b**) 91-wire radial compression model; (**c**) 91-wire bending model.  
full contact and generate contact pressure. C3D8R elements are used for meshing, and contact is modeled as a hard contact using a penalty function algorithm.  
The model for the radial compression of the 91-wire cable involves closely arranging the wires according to the actual situation, creating multiple layers of wire bundles using the array function. Uniform loads are applied to the outermost surface of the wires to simulate the tightening effect caused by the shrinkage of the HDPE sheath due to temperature reduction. This approach provides better control over the magnitude of radial pressure and allows for a more intuitive comparison with theoretical results. Similar to the previous model, the wire ends are coupled to a reference point, and constraints are applied to simulate the boundary conditions of the wires. C3D8R elements are used for meshing, and contact is modeled as a hard contact using a penalty function algorithm.  
The model for the bending of the 91-wire cable involves arranging the wires closely, applying uniform pressure to the top of the outermost wire to ensure full contact and induce bending. This method, simulating the bending contact of the wires, is more prone to convergence and ensures sufficient contact between the wires for analyzing the distribution of contact pressure. The boundary conditions for the cable are the same as in the previous two models.
## 4 Experiments
### Experimental Setup
#### 4.1.3 Performance Metrics  
We evaluate the model performance with HR and NDCG. HR is a straightforward metric used to measure the recall of a recommender system. It evaluates the system's ability to retrieve relevant items from the test set within the top-K recommendations. NDCG considers both the relevance of recommended items and their position in the recommendation list. NDCG aims to assess the quality of the ranking, ensuring that the most relevant items are ranked at the top, thereby enhancing user satisfaction and engagement. HR and NDCG effectively evaluate the system's recall and ranking abilities. Together, these metrics provide a comprehensive view of the system's performance, guiding improvements and ensuring better user experiences. HR and NDCG could be formulated as Equations (6) and (7).  
\[HR=\frac{1}{n}\sum_{u=1}^{n}\text{hits}(u) \tag{6}\]  
\[NDCG=\frac{1}{n}\sum_{u=1}^{n}\frac{\log 2}{\log{(p_{u}+1)}} \tag{7}\]  
where \(n\) is the number of users and \(hits(u)=1\) indicates that the test item of the user \(u\) is in the top-K recommendation list, otherwise \(hits(u)=0\). \(p_{u}\) denotes the position of user \(u\)'s test item in the top-K recommendation list and \(p_{u}\rightarrow\infty\) when the test item is not in the list. Here, we set \(K=10\) for all experiments.
## 3 Methodology
### Microwave Data Downscaling  
#### 3.3.1 Feature Vector Generation for LST Conversion  
To transform MWRI LST data from 25 km to 1 km resolution, we used a statistical downscaling approach that integrates auxiliary predictors at finer resolutions. The key predictors used in this process are the Normalized Difference Vegetation Index (NDVI), the digital elevation model (DEM), and slope data. These predictors are associated with the MWRI LST data, and their relationship is captured using regression models. Below is the procedure followed:  
1. **Data Preparation:** The original LST data at 25 km resolution is complemented by auxiliary features such as NDVI, DEM, and slope, each of which is provided at a 1 km resolution. These predictor datasets are loaded from respective directories and combined to form a feature vector for each LST data point. The NDVI, DEM, and slope are critical in capturing the variations in surface characteristics that influence LST.
2. **Regression Models:** Using the downscaled predictor datasets (NDVI, DEM, and slope), regression models are constructed for each LST band. These models represent the relationship between the downscaled predictors and the LST values. The downscaled LST (DLST) data are obtained by applying these models to the predictor feature vectors.
3. **Data Fusion:** The downscaled LST values are combined with the fine-resolution predictors (NDVI, DEM, and slope), creating a fused dataset at 1 km resolution. This allows the higher-resolution spatial information to be incorporated into the downscaled LST data.  
These features are combined using statistical regression methods to predict the LST at the finer 1 km resolution. The entire process of downscaling includes multiple regression models for each LST band, with models being discarded if they fail to meet a specified R\({}^{2}\) threshold (default set at 0.3) or if more than 40% of their pixels are missing.
Sensors_**2011**, _11_, 7141-7161; doi:10.3390/s110707141  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Long-Range Wireless Mesh Network for Weather Monitoring in Unfriendly Geographic Conditions**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Research and Postgrade Studies, Autonomus University of Quer daro, Cerro de las Campanas S/N, C.P. 76010 Quer daro, M cico; E-Mails: [EMAIL_ADDRESS] (G.H.-R.); [EMAIL_ADDRESS] (G.M.S.-Z.); [EMAIL_ADDRESS] (E.A.R.-A.); [EMAIL_ADDRESS] (R.D.B.T.); [EMAIL_ADDRESS] (R.E.P.-T.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +52-442-144-5820 ext. 6003; Fax: +52-442-192-1200.  
_Received: 11 May 2011; in revised form: 17 June 2011 / Accepted: 19 June 2011 / Published: 12 July 2011_  
**Abstract:** In this paper a long-range wireless mesh network system is presented. It consists of three main parts: Remote Terminal Units (RTUs), Base Terminal Units (BTUs) and a Central Server (CS). The RTUs share a wireless network transmitting in the industrial, scientific and medical applications ISM band, which reaches up to 64 Km in a single point-to-point communication. A BTU controls the traffic within the network and has as its main task interconnecting it to a Ku-band satellite link using an embedded microcontroller-based gateway. Collected data is stored in a CS and presented to the final user in a numerical and a graphical form in a web portal.  
**Keywords:** wireless sensor networks; remote monitoring; embedded systems
## 2 Experimental Study
### Passivity Impedance-Based Stability Criterion
\begin{array}{c}\Re\{\mathrm{Y(j\omega)}\}&=\Re\left\{\frac{\mathrm{Y_{inv} (j\omega)}\big{(}1+\mathrm{\
abla_{inv}(j\omega)Z_{g}(j\omega)}\big{)}}{\big{|} 1+\mathrm{Y_{inv}(j\omega)Z_{g}(j\omega)}\big{|}^{2}}\right\}\\ &=\Re\left\{\frac{\mathrm{Y_{inv}(j\omega)+Y_{inv}(j\omega)\mathrm{Y_{inv}(j \omega)Z_{g}(j\omega)}}}{\big{|}1+\mathrm{Y_{inv}(j\omega)Z_{g}(j\omega)}} \right\}\\ &=\frac{\Re\{\mathrm{Y_{inv}(j\omega)+Y_{inv}(j\omega)|^{2}R\big{\{}Z_{g}(j \omega)\}}}}{\big{|}1+\mathrm{Y_{inv}(j\omega)Z_{g}(j\omega)}\big{|}^{2}} \geq 0,\forall\omega\in(0,\mathrm{f_{L}}]\end{array}  \tag{4}\]  
The current loop control transfer function \(\mathrm{H_{c}(s)}\) of a well-functioning grid-connected inverter is passive because its poles are located in the left half plane. Due to the same principle, the closed-loop transfer function of the inverter-grid system H(s) is also passive if Y(s) is already passive. The power grid is composed of passive components such as an inductor, capacitor, and resistor, so the grid impedance \(Z_{g}(s)\) is always stable and passive [14]. In this way, the stability of the inverter-grid system is determined by the passivity degree of the inverter output admittance Yinv(s). This criterion is expressed in Equation (5). \[\mathrm{Re}\{\mathrm{Y_{inv}(j\omega)}\}\geq 0,\forall\omega\in(0,\mathrm{f_{L}}] \tag{5}\]  
When Equation (5) is met, the inverter-grid system is stable if the resonance frequency of grid impedance is located in the interval (0,fL). In Equation (5), when \(\mathrm{Re}\{\mathrm{Y_{inv}(j\omega)}\}\) is equal to 0, the inverter-grid system will be in a critical stable state. The inverter output admittance can be derived from the modeling of the inverter.
# Synthesis and Electrical Properties of a New Bipolar Material Using Spacer Moiety
## 2 Materials and Methods
### Synthesis of 9-(4-Bromo-2,5-dimethyl-phenyl)-9H-carbazole (1)  
1,4-Dibromo-2,5-dimethyl-benzene (20 g, 76 mmol), 9H-carbazole (19 g, 114 mmol), copper iodide (CuI) (0.72 g, 3.8 mmol), trans-1,2-cyclohexanediamine (0.92 mL, 7.6 mmol), and potassium phosphate (K\({}_{3}\)PO\({}_{4}\)) (51 g, 240 mmol) were added to a 500 mL round-bottom flask, followed by nitrogen substitution and connection to a reflux apparatus. After adding 150 mL of 1,4-dioxane, the mixture was stirred at an elevated temperature for 24 h. Upon completion of the reaction, the solvent was removed, and the residue was extracted with CH\({}_{2}\)Cl\({}_{2}\) and distilled water. After treatment with MgSO\({}_{4}\) and filtration, the product was obtained by column chromatography using hexane, yielding 52%.  
1H NMR (300 MHz, DMSO-d\({}_{6}\)): 8(ppm) 1.80(s, 3H), 2.35(s, 3H), 6.96-6.99(d, J = 8.1, 2H), 7.21-7.26(m, 2H), 7.33-7.38(m, 3H), 7.78(s, 1H), 8.18-8.22(d, J = 9.1, 2H).
## 5 Experimental Setup
### Datasets  
In the experiments, we utilize five different knowledge graphs to evaluate the proposed reasoning method:  
* WN18 RR [38], obtained from the knowledge graph WordNet [39], with the reverse relations removed;
* FB15k [16], a subset of the universal knowledge graph Freebase [40];
* FB15k-237 [41], derived from FB15k, and some reverse relations are deleted.
* YAGO3-10-dr [42], derived from YAGO3-10 [38] with the reciprocal relations removed.
* Wikidata5m [43], derived from Wikidata [44]. The scale is much larger than for the previous four datasets.  
The statistics for the datasets are shown in Table 1, where \"#entity\" and \"#relation\" represent the numbers of entities and relations, respectively. \"#train\", \"#valid\", and \"#test\" denote the number of triples in the training set, validation set, and test set for each dataset, respectively.
## 2 Optical Model  
The optical system contains four optical components, and the relative position of each optical component is shown in Figure 2.  
If the perturbation is small, the deformation of an optical component is negligible, indicating that the optical component can be assumed as a rigid body. The optical model used for jitter analysis can be briefly described as a first-order Taylor expansion of the image motion of a central image point on the focal plane [18; 19]. The linear optical model can be expressed as:  
\[L=L_{0}+\frac{\partial L}{\partial U}\Delta U+(2) \tag{1}\]  
where \(L\) is the image motion of a central image point on the focal plane; \(L_{0}\) is the nominal decentration of a central image point; \(\Delta U\) is the perturbation of each degree of freedom (DOF); (2) represents the second and higher order terms of the expansion, which is neglected in the linear analysis; and \(\frac{\partial L}{\partial U}\) is the optical amplification factor representing the decentration of a central image point on the focal plane associated with each unit transition/tilt of optical component.  
Since \(+X\) is the flight direction of the satellite, decentration on the focal plane is projected to the \(X\) direction (along the flight direction) and \(Y\) direction (normal to the flight direction) to calculate pixel offset of the image. The optical amplification factor of each optical component is calculated with optic design software (Zemax) and shown in Table 1.  
Figure 2: Optical layout of the camera.
## 3 Results and Discussions
### Phase Analysis of FA after Incorporation  
After the standard curing for 3 days and 7 days, the concrete XRD test was conducted based on the FA content of 0% and 20%, respectively, to observe their micromorphology and qualitatively study their hydration products. Then the macroscopic difference is explained from the microscopic mechanism, and the impact of FA on the road performance of concrete is further explored. The XRD test was used to analyze and determine the content of amorphous calcium silicate hydrate (C-S-H) gel, and the test results are shown in Figure 5.  
XRD is mainly used to analyze the crystal changes in the slurry before and after the hydration reaction of pervious concrete, so as to qualitatively analyze the hydration reaction effect of admixture added to pervious concrete. Figure 5 shows the XRD pattern of concrete after 3 days of the standard curing. It can be seen from Figure 5a that the main phases of concrete without FA are SiO\({}_{2}\), CaCO\({}_{3}\), Ca(OH)\({}_{2}\), and Ca\({}_{2}\)SiO\({}_{4}\), whereas the main phases of concrete mixed with 20% FA are SiO\({}_{2}\), CaCO\({}_{3}\), Ca(OH)\({}_{2}\), and C-S-H, which can be seen from Figure 6b. The formation of C-S-H gel indicates that the incorporation of FA promotes the hydration of cement clinker in concrete. With the increase of C-S-H gel content, the porosity of the cementing material decreases, the material becomes dense, the connected porosity of the permeable concrete decreases, and the reaction speed in the system is improved. The content of Ca(OH)\({}_{2}\) and SiO\({}_{2}\) in the system was gradually reduced. The results also present the correspondence in the XRD spectrum phase peak.  
Figure 6 shows the XRD pattern of concrete after 7 days of the standard curing, from which it can be seen that the main phases of concrete without FA are SiO\({}_{2}\), CaCO\({}_{3}\), Ca(OH)\({}_{2}\), and C-S-H. The main phases of concrete mixed with 20% FA are SiO\({}_{2}\), CaCO\({}_{3}\), Ca(OH)\({}_{2}\), C-S-H, and columnar ettrinite (AFt). Compared with the XRD pattern of FA content in 3 days of curing standard, after 7 days of curing standard, the FA added in concrete can replace cement significantly, and exerts an active pozzolanic effect. AFt was further formed by a secondary reaction of calcium hydroxide due to the active pozzolanic effect.
## 2 Related Basic Work
### Cosine Distance  
According to the literature [29], algorithms using cosine distance for high-dimensional datasets tend to exhibit better performance. In order to further verify the impact of cosine distance on high-dimensional datasets, the principle and definition of cosine distance will be shown here.  
**Definition 7** (Cosine distance).: _Cosine similarity measures the cosine of the Angle between two vectors in the space. Let \(x_{ik}\) represent the value of the \(k\)th dimension of the \(i\)th sample point in the dataset \(D\), \(m\) represent the dimension of \(D\), and let \(\theta\) represent the included angle between two sample points, then the mathematical expression of cosine similarity is shown in Equation (8)._  
\[\cos(\theta)=\frac{x_{i}x_{j}}{|x_{i}||x_{j}|}=\frac{\sum\limits_{k=1}^{m}x_{ ik}x_{jk}}{\sqrt{\sum\limits_{k=1}^{m}{x_{ik}}^{2}\sqrt{\sum\limits_{k=1}^{m}{x_{ jk}}^{2}}}} \tag{8}\]  
According to the principle of cosine similarity, as the value of \(\theta\) decreases, the closer the cosine value is to 1, the more similar they are, and the cosine distance \(d_{ij}\) can be expressed as Equation (9):  
\[d_{ij}=1-\cos(\theta) \tag{9}\]