esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Biosensor Application of Electrodeposited Composite Nanostructures
### Composite Electrodes Containing Organic Polymers
The immobilized glucose oxidase showed direct electron transfer and a limit of detection for glucose of 1.0 \(\times\) 10\({}^{-5}\) M.  
The composite nanostructures of a conducting polymer and electrodeposited bimetallic Au-Pt nanoparticles were used as a support for HRP to make a H\({}_{2}\)O\({}_{2}\) sensor [128]. A polypyrrole film was deposited onto a GCE from a solution of 0.05 M of pyrrole in 0.1 M of LiClO\({}_{4}\) by scanning from zero to one V (versus SCE). The bimetallic nanoparticles were electrodeposited at \(-\)0.2 V from an equimolar solution of HAuCl\({}_{4}\) and H\({}_{2}\)PtCl\({}_{6}\) in 0.2 M of Na\({}_{2}\)SO\({}_{4}\). Subsequently, the nanoparticles surfaces were modified by immersion in a solution of L-cysteine for two hours. Calf thymus DNA was immobilized by electrostatic interaction, which was followed by the immobilization of HRP also by electrostatic immobilization. SEM (S-4800, Hitachi, Tokyo, Japan) revealed a high coverage of many globular nanoparticles. The detection of H\({}_{2}\)O\({}_{2}\) at \(-\)0.2 V gave a detection limit of 1.3 uM and a linear range from 4.9 uM to 4.8 mM. Gold nanoparticles were electrodeposited on SPCE and then modified with the enzyme cytochrome P450 2E1 electrostatically immobilized into a polyelectrolyte film of poly (diallyldimethylammonium chloride) (PDDA) and poly(sodium-p-styrenesulfonate) (PSS) [129]. The gold nanoparticles were formed on the SPCE from 1% HAuCl4 solution by applying a potential of \(-\)0.2 V (versus Ag/AgCl) for 30 s. The enzyme-modified SPCE was used to detect 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), which is a carcinogen formed in tobacco smoke. After electrodeposition, there was a fairly uniform distribution of \(\sim\)100-nm gold nanoparticles over the SPCE surface of many irregular graphite particles. Application of the polyelectrolyte layer resulted in a smooth surface. The detection of NNK in urine using chronoamperometry with excellent recovery was demonstrated. SPCE substrates were also used as supports for composite electrodes of gold nanoparticles, increase, and polypyrrole for the detection of polyphenols in propolis, which is the residue produced by honeybees [130]. The parameters for electrode preparation were all optimized in terms of the analytical response. Gold nanoparticles were electrodeposited at \(-\)400 mV (versus Ag/AgCl) from 0.01% HAuCl\({}_{4}\) in 0.1 M of KCl, resulting in particles that were 30 to 50 nm in size. Laccase was adsorbed by drop casting from 10 ug mL\({}^{-1}\) of solution. Pyrrole was electropolymerized from 0.2 M of pyrrole at 700 mV for 600 s, covering the enzyme with an electroactive layer. The enzyme activity was at its maximum at pH 3.5, and with detection at -450 mV in 0.1 M of acetate buffer of this pH, a detection limit of 0.83 mM was determined for polyphenols extracted from propolis using ethanol.
## 3 Results and Discussion
### The Moisture Absorption Rate and Delunidification Rate of Foamed MOC Material  
The thermal conductivity of air in the thermal insulation material was only 0.02 w/(m-K). When water vapor entered the cavity of foamed cement, the thermal conductivity of water was 0.59 w/(m-K), and its thermal insulation performance was greatly reduced. Therefore, hygroscopicity and water content had a significant impact on the thermal conductivity of foamed cement. The test environment was to simulate the temperature and the humidity of the material; the temperature was 30 \({}^{\circ}\)C, the relative humidity was 100%, and the moisture absorption rate tested with different densities of F-MOC and N-MOC is shown in Figure 13. The results show that, under the same density, the hygroscopic rate of F-MOC was less than that of N-MOC. When the bulk density was approximately 1200 Kg\(\cdot\)m\({}^{-3}\), it was difficult for water vapor to enter the sample due to the small pore size. After cleaning the surface, the moisture absorption rate of F-MOC and N-MOC was close, as it only decreased by 1.7%, but when the bulk density decreased to 300 Kg\(\cdot\)m\({}^{-3}\) due to the increase of the specific surface area, the moisture absorption rate of F-MOC was 13.3%, while that of N-MOC was up to 29.5%.  
Figure 11: The influence of different mechanical polishing thickness on the water contact angle of F-MOC.  
Figure 12: The effect of soaking in different solutions on the chemical stability of F-MOC.  
The dehydration rate of F-MOC and N-MOC is as follows in Figure 14. Dehydration experiments of F-MOC and N-MOC with a bulk density of approximately 600 Kg-m\({}^{-3}\) after being blistered for 120 min at 30 \({}^{\circ}\)C showed that the quality of F-MOC could be restored to 98.1% of the original quality, while N-MOC only could be restored to 83.2% of the original quality. The high dehydration rate of F-MOC indicates that the water in the pores of the foamed material was the water that adhered to the surface, which was easy to evaporate and remove, while the low dehydration rate of N-MOC indicates that some of the water in the pores had been immersed in the MOC gelling material. Internally, we know that the mechanical properties of MOC materials decrease significantly in long-term blisters [34], which also indicates that F-MOC materials have better resistance to water vapor and can maintain their initial strength during long-term water vapor.
## 3 Results and Discussion  
### Consistency  
The consistency (PN-EN 1015-3) of all freshly prepared mortars was determined using the flow cone method (Figure 4). The analysis of the consistency results for cement mortars modified with MSWI-FA and CKD waste reveals significant changes in their properties compared to the reference mortar (MS). Mortars with fly ash (MSWI-FA) showed a noticeable decrease in consistency compared to the reference mortar (MS). For MSWI-FA-10, MSWI- MSWI-FA-15, and MSWI-FA-20, the consistency values were 14.9 cm, 14.7 cm, and 14.65 cm, respectively, indicating a deterioration in workability (a key feature of fresh mortar, determining how easy it is to mix, transport, and apply with a minimal risk of segregation of components and loss of homogeneity) with increasing MSWI-FA content. Mortars with cement kiln dust (CKD) exhibited a smaller decrease in consistency compared to MS, with values ranging from 18.2 cm to 19 cm, suggesting a better ability to retain water in the mixture. Combinations of MSWI-FA and CKD (e.g., MSWI-FA-CKD/10-10) affected consistency in varying ways, depending on the proportions of the two components. For example, MSWI-FA-CKD/10-10 had a consistency of 18.5 cm, similar to CKD-10, whereas combinations with higher MSWI-FA content (e.g., MSWI-FA-CKD/20-10) showed lower consistency values. It is notable that the addition of CKD partially compensates for the negative impact of MSWI-FA on consistency. However, this compensatory effect is limited at higher MSWI-FA doses, as shown by MSWI-FA-CKD/17-3, which had a consistency of 13.3 cm.
## 2 Related Work  
The separation of the control plane from the data plane in SDN architecture introduces an extra point of failure which is the control plane failure. Little effort has been devoted by the community to solve the shortcomings of the SDN paradigm under control plane unreliability (including controller failures and control channel losses).  
In order to overcome SDN controller failure, the deployment of multiple SDN controllers [5; 12] may be a solution (controller redundancy); however, it may also lead to inconsistency issues. For instance, HyperFlow [15] proposes physically distributed controllers that are synchronized through a publish/subscribe system. ONOS requires complex procedures for inter-controller synchronization. Another option is to design schemes to handle control plane unreliability.  
For instance, ResilientFlow [16] restores the control channel through alternate paths (path redundancy) in the presence of channel failure. However, these research works focused on solving a single specific type of faults, such as SDN controller failure or control channel failure. Moreover, to tackle SDN controller failures, a solution may be to form a hierarchy or cascade deployment model. In this context, OpenFlow, since version 1.3 [5], supports multiple SDN controllers for managing an equivalent set of forwarding nodes. However, some inconsistent issues, like Event Ordering, Unreliable Event Delivery, and Repletion of Commands, are still matters of concern.  
Ravana [17] ensured event ordering, correct event processing, and execution of commands for exactly once during SDN controller failure. In ref. [18], an integrated SDN concept was proposed where an OLSR-to-OpenFlow (O2O) module was presented to configure control rules that are used to forward OpenFlow packets. After detection of SDN controller failure by the O2O module, the O2O module dumps the OLSR routing tables into the forwarding nodes to recover from SDN controller failure. This approach needs the translation of routing tables into SDN rules.  
Although there have been complex schemes applied in SDN controllers to handle cluster management, device mastership to guarantee consistency in case of controller failure [12], to our knowledge, limited efforts have been devoted to handle degraded control channel performance (e.g., a lossy control channel), which may be the norm when deploying multi-hop wireless networks to serve dense small cell deployments, for instance. Contrarily, this paper focuses precisely on this by proposing a simple (hence, fast to execute/decide) scheme that recovers from SDN controller fault, control channel failure, or even degraded control channel by reacting to Control Packet Loss Ratio (CPLR) variations of the control communication channels as a measure of the unreliability of the control plane from the viewpoint of the nodes.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Theoretical Validation of HSALA
#### 5.3.1 Alignment with Software Engineering Principles  
As shown in Table 9, the HSALA model aligns strongly with modularity, encapsulation, and separation of concerns.  
HSALA aligns closely with core software engineering principles, promoting a well-organized and manageable architecture that facilitates development, maintenance, and scalability.
## 1 Introduction
### Kernel Function  
So far, in support of vector machines, the research on the theory of kernel function has been focusing mainly on the following aspects. The first one is the properties of kernel function. To be more specific, some properties of Mercer kernel are deeply studied and discussed in reference [7]; for example, [PERSON] studied the nature of kernel functions from a geometric point of view [8]. The second one is the construction and improvement of kernel function. For example [9], [PERSON], [PERSON] et al. regard the construction of kernel function as a function interpolation problem, proposing that it is not necessary to construct explicit function satisfying Mercer condition. The key to the performance difference of the kernel function is to determine the inner product value between the test samples and the training samples according to the inner product between the training samples. It is not necessary to construct the analytic expression of the kernel functions when using this method, for which can directly construct the kernel functions from the samples, thus opening up a new channel for the construction of the kernel functions. However, this method is disadvantageous due to too large amount of calculation when solving the actual classification problem. [PERSON] et al. [10], through conducting the Riemann geometric analysis of kernel function, gradually modified the existing kernel function based on experimental data, to make it better matched with the actual problem. The third one is the selection of the kernel function. Through literature review [11], a method of selecting the kernel function based on hybrid genetic algorithm for addressing the upper limit of Loo upper bound is proposed. [PERSON] [12] proposed two methods to automatically determine the kernel function, namely, the parameter determination method based on the matrix similarity measure, and the parameter determination method based on the separable kernel function. These two methods are commonly advantageous due to their small amount of calculation and the convenient calculation, but disadvantageous in that the parameters obtained by using the two methods are usually satisfactory solutions rather than optimal solutions.  
The usual methods of selecting kernel functions to solve practical problems are as follows. Firstly, kernel functions are selected in advance based on the prior knowledge of experts; secondly, Cross-Validation method is adopted, that is, different kernel functions should be tried separately when selecting kernel functions, with the kernel function with the smallest error as the best kernel function. For example, aiming at the Fourier kernel and RBF kernel, combined with the function regression problem in signal processing problems, simulation experiments show that the error of SVM with Fourier kernel is much less than that with RBF kernel under the same data conditions. The third one, also known as the mainstream method for selecting kernel functions at present, is to use the hybrid kernel function method proposed by [PERSON] et al. [13], which is also the pioneering work of constructing kernel functions.
## 2 EKF-Based Parameters Identification
The EKF algorithm is realized using a sequence of equations which generate a time-varying estimate of state error covariance and Kalman gain. The procedure of the EKF algorithm can be computed as:  
State prediction:  
\[\widetilde{\mathbf{X}}_{k+1}=\hat{\mathbf{X}}_{k}+\int_{t_{k}}^{t_{k+1}}\mathbf{f}(\mathbf{X},\mathbf{F})dt\bigg{|}_{X=\widehat{\mathbf{X}}_{k}} \tag{9}\]  
Error covariance prediction:  
\[\mathbf{X}(t_{k+1})=\Phi(t_{k+1},t_{k})\mathbf{X}(t_{k})+\int_{t_{k}}^{t_{k+1}}\Phi(t _{k+1},\tau)\mathbf{B}\mathbf{F}(\tau)d\tau \tag{4}\]0  
Gain matrix:  
\[\mathbf{X}(t_{k+1})=\Phi(t_{k+1},t_{k})\mathbf{X}(t_{k})+\int_{t_{k}}^{t_{k+1}}\Phi(t _{k+1},\tau)\mathbf{B}\mathbf{F}(\tau)d\tau \tag{4}\]1  
State filtering:  
\[\mathbf{X}(t_{k+1})=\Phi(t_{k+1},t_{k})\mathbf{X}(t_{k})+\int_{t_{k}}^{t_{k+1}}\Phi(t _{k+1},\tau)\mathbf{B}\mathbf{F}(\tau)d\tau \tag{4}\]2  
Error covariance update:  
\[\mathbf{X}(t_{k+1})=\Phi(t_{k+1},t_{k})\mathbf{X}(t_{k})+\int_{t_{k}}^{t_{k+1}}\Phi(t _{k+1},\tau)\mathbf{B}\mathbf{F}(\tau)d\tau \tag{4}\]3  
where \(\mathbf{Q}\) is the system noise, \(\mathbf{R}\) is the observed noise, \(\Phi_{k}=e^{\Delta t\Delta t}\approx\mathbf{I}+\mathbf{A}\Delta t\), \(\mathbf{A}=\left.\frac{\partial\mathbf{f}}{\partial\mathbf{X}}\right|_{\mathbf{X}=\widehat\mathbf{{X}}_{k}}\), and \(\mathbf{H}_{k}=\left.\frac{\partial\mathbf{h}}{\partial\mathbf{X}}\right|_{\mathbf{X}=\widehat {\mathbf{X}}_{k+1}}\). Equation (12) combines Euler integration of the system using the time step T with state and parameter adaptation driven by the output error. Due to the Euler integration, T must be very small so as to ensure the filter accuracy.
# The Limited Impact of Running Performance on Football Success in the Turkish Super League
## 5 Conclusions  
Our findings provide valuable insights into the role of running performance and situational factors in football success. Firstly, it was determined that running performance, measured through metrics such as distance covered at various intensities, does not significantly impact match outcomes. This aligns with studies in other leagues that suggest that while physical metrics are a factor in sports performance, they do not overwhelmingly determine the success of football teams. This finding is very important in understanding that football success is multifaceted, involving technical-tactical, psychological, and contextual components that go beyond mere physical exertion. Indeed, our research found that contextual variables, such as home advantage or the ranking of the team and the opponent, are significant predictors of the outcome of a match in contrast to running performance.  
Practically, the findings suggest that coaches and sports scientists should consider a broader array of factors when preparing teams for competition. This could lead to more comprehensive training regimes that address technical-tactical and psychological readiness in addition to physical conditioning. Specifically, given that running performance metrics such as distances covered at various intensities were not significant predictors of match outcomes, emphasis should be placed on optimizing tactical strategies and technical execution rather than on solely improving physical parameters. For example, training sessions could focus on replicating in-game scenarios that require players to adapt to the specific strengths and weaknesses of the opposition. Furthermore, the significant influence of situational factors such as match location highlights the importance of preparing players mentally and tactically for away games. Coaches should implement strategies that mitigate the psychological challenges of playing away, such as stress management techniques and fostering team cohesion.  
Methodology, C.K.; Formal analysis, S.T. and A.E.K.; Data curation, S.T.; Writing--original draft, S.P.; Writing--review & editing, S.P., C.K. and S.M.; Visualization, A.E.K.; Supervision, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The current study received ethical approval from the local university's (the University of Thessaly) ethics committee on 12 October 2022 (code, 1973).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical reasons.  
Author [PERSON] was employed by the company AIDEAS OO. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Duqu
### Duqu Analysis
#### 2.1.7. Keylogger Module  
Unlike Stuxnet, Duqu has a keylogger component that steals information from the infected system. The keylogger not only logs keystrokes but also regularly saves screenshots and packs other types of information. It stores data in the %TEMP% directory of the computer in a compressed format. The executable of the keylogger contains an embedded jpeg file. The jpeg image is not complete, the readable text shows \"Interacting Galaxy System NGC 6745\". This refers to a picture, taken from NASA, showing two colliding galaxies. Within the jpeg file, after the partial image, an encrypted DLL can be found which contains the main keylogger functions.
## 1 Introduction
Utilizing the special properties of VO2 thin films, absorbers constructed with metamaterial capitalize on the distinct temperature phase transition characteristics of VO2, disrupting the impedance matching conditions when triggered thermally [25] and achieving a profound modulation effect. Displaying promising potential, absorbers containing VO2 sub-wavelength structures exhibit remarkable capabilities in achieving high absorption modulation over a wide range of frequencies. In comparison to conventional multilayer absorbers, VO2-based absorbers offer a simpler, ultra-thin, and dynamically adjustable solution, leading to enhanced modulation speed and reduced structural complexity [26; 27; 28; 29]. Switching VO2 from an insulating state to a metallic state (and vice versa) requires a temperature change of about 50 \({}^{\circ}\)C, which takes about tens of picoseconds and depends on various factors such as the thickness of the film and carrier density. Recent studies have shown that by combining multiple VO2 thin films, faster switching speeds and wider absorption bands can be achieved. In addition, a higher initial laser power can be used to accelerate the switching speed. As a result, the exploration and advancement of VO2 broadband absorbers that are broadband, ultra-slim, and can be dynamically adjusted have emerged as a central focus in ongoing research, opening up new perspectives and opportunities for improving the effectiveness of metamaterial absorbers. This work proposes a dynamically tunable, wideband, and efficient absorber based on the phase-change material VO2. When the conductivity of VO2 reaches 200,000 S/m in its metallic phase, it exhibits excellent absorption properties, exceeding 90% absorption across a broad frequency range of 2.4 THz to 11.0 THz. Additionally, near-perfect absorption of close to 100% is achieved at resonance frequencies of 3.0 THz, 7.1 THz, 8.7 THz, and 10.6 THz. By controlling the conductivity of VO2, a transition between the functionalities of a reflector and an absorber can be freely made, enhancing its flexibility and applicability. Equally noteworthy are the features of wide-angle absorption and polarization insensitivity exhibited by the absorber, which effectively broaden its practical applications and adaptability to diverse scenarios. The findings of this study offer valuable guidance for advancing the technology of sophisticated and high-efficiency broadband tunable devices, paving the way for the continuous progress of terahertz technology and its diverse applications.
## 3 Modeling the Relationship between Field AGB and Satellite Data  
Firstly, the pairwise Pearson's product-moment correlation analysis was conducted to determine the correlation of observed above-ground biomass and Sentinel-based predictors, as well as the collinearity between predictors. Predictors that were highly correlated to each other (\(r\geq 0.8\)), and had high variance inflation factors (VIFs \(\geq 10\)) in regression analysis, were excluded from modeling [65; 66]. These analysis steps were performed using SPSS (version 21.0, IBM, Armonk, NY, USA). Then, all of the explanatory variables were transformed to a Z-score to eliminate the effect of index dimension and quantity of data. The formula was defined as \(\mathsf{x}^{*}=(\mathsf{x}-\mu)/\sigma\), where \(\mu\) is the mean value of a specific explanatory variable and \(\sigma\) is its standard deviation [67]. After that, GWR, ANN, SVR, and RF were used in this study to model ABG based on Sentinel-derived predictors (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source Image** & **Relevant Predictors** & **Description** \\ \hline \multirow{8}{*}{Sentinel-1} & \multirow{3}{*}{Polarization} & VV & vertical transmit-vertical channel \\  & & VH & vertical transmit-Horizontal channel \\  & & V/H 1  & VV/VH \\ \cline{2-3}  & & VH\_CON, VV\_CON & Contrast \\  & & VH\_IDS, VV\_DIS & Dissimilarity \\  & & VH\_HOM, VV\_HOM & Homogeneity \\  & & VH\_ASM, VV\_ASM & Angular Second Moment \\  & & VH\_ENE, VV\_ENE & Energy \\  & & VH\_MAX, VV\_MAX & Maximum Probability \\  & & VH\_ELN, VV\_ENT & Entropy \\  & & VH\_MEA, VV\_MEA & GLCM Mean \\  & & VH\_VAR, VV\_VAR & GLCM Variance \\  & & VH\_COR, VV\_COR & GLCM Correlation \\ \hline \multirow{8}{*}{Multispectral bands} & \multirow{8}{*}{Multispectral bands} & B2 & Blue, 490 nm \\  & & B3 & Green, 560 nm \\  & & B4 & Red, 665 nm \\  & & B5 & Red edge, 705 nm \\  & & B6 & Red edge, 749 nm \\  & & B7 & Red edge, 783 nm \\  & & B8 & Near Infrared, 842 nm \\  & & B8a & Near Infrared, 865 nm \\  & & B11 & Short Wave IV, 1610 nm \\  & & B12 & Short Wave IR, 1290 nm \\ \hline \multirow{4}{*}{Vegetation indices} & NVDV 3  & \\ \cline{1-1} \cline{2-3}  & & LAI & Leaf Area Index \\ \cline{1-1} \cline{2-3}  & Vegetation & FVC & Fraction of Vegetation Cover \\ \cline{1-1} \cline{2-3}  & biophysical variables & FPAR & Fraction of Absorbed Photosthetically Active Radiation \\ \cline{1-1} \cline{2-3}  & & Cab & Chlorophyll content in the leaf \\ \hline SRTM DEM & Elevation & H & Elevation, 30 m resolution \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentinel-based imagery data predictors of above-ground biomass.