esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, the effect of Galileo augmenting GPS was analyzed from the availability, PDOP, ambiguity dilution of precision (ADOP), and success rate for bridge monitoring. The enhancement effects of Galileo and the baseline length constraint on bridge monitoring were emphatically studied. This paper is arranged as follows: Section 1 is the introduction. The SFSE double-differenced GPS/Galileo mathematical model with baseline length constraint is derived in Section 2. Section 3 introduces the bridge monitoring data collection and the corresponding satellite availability and PDOP are analyzed. Section 4 mainly includes the calculation and analysis of the two sets of bridge baseline data. The performance of integrated GPS/Galileo precision positioning with baseline length constraints for bridge monitoring is evaluated with different indicators, and the frequency analysis of bridge vibration is also performed. Finally, the main conclusions are drawn in Section 5.
Therefore, the different data sets are compared based on common descriptive statistics in Section 5.1 and the introduced metric and visualization in Section 5.2. It is shown that the introduced metric and visualization can detect incomplete and unbalanced data in multi-dimensional data sets while common statistics fail to indicate this. The random forest regression results of the different data sets are given in Section 5.3, showing the impact of data set completeness on model accuracy. Finally, a critical discussion of the metric is performed in Section 6 and a conclusion and outlook are given in Section 7.
# Synergistic Effect of Nonthermal Plasma and ZnO Nanoparticles on Organic Dye Degradation
## 2 Materials and Methods
### Catalytic Performance  
We investigated the catalytic performance of atmospheric pressure Ar plasma with/without (W/O) ZnO nanoparticles in the degradation of the dye methylene blue (MB) C\({}_{16}\)H\({}_{18}\)N\({}_{3}\)SC\({}_{1}\). An aqueous solution of MB (analytical reagent grade--Merck Germany) was prepared at a concentration of 35 mg/L. Aqueous solution of MB (30 mL) was used as the treated sample volume and placed in a beaker. To study the combined effect of plasma and a photocatalyst, we added ZnO NPs with a concentration of 0.2 and 0.4 gL\({}^{-1}\), respectively. The argon plasma orifice was placed at ~5 mm above the MB solution so that the plasma plume impinged on the solution. The degradation of the MB dye solution was evaluated by measuring their absorption spectra with an ultraviolet-visible (UV-Vis) spectrophotometer (UV-5200 Spectrophotometer, Shaanxi, China). The catalytic efficiency of the degradation of MB was calculated using [24]:  
\[Degradation~{}Efficiency~{}(\%)=\frac{A_{o}-A_{t}}{A_{o}}\times 100, \tag{1}\]  
where \(A_{o}\) and \(A_{t}\) are the absorbance of untreated MB and plasma W/O ZnO NPs treated with MB, respectively, at a given time \(t\).
# Dynamic Initial Weight Assignment for MaxSAT
## 4 Experimental Results and Analyses  
This research aimed to determine whether the newly published algorithm from [24] had a similar positive effect on improving the weighting DLS algorithms' efficiency based on the allocation of weights when applied to solve MaxSAT problems. For this purpose, we conducted this research in three steps as follows: (1) a survey of the structural information of the problem sets, (2) an investigation of the distribution of clauses weights, and (3) an experimental comparison of the InitWeight mechanism implemented in DDFW against the state-of-the-art YaLSAT, Sparrow, and ProbSAT.
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 2 Time-Dependent FMEA Model (TD-FMEA Model)
### Loss Evaluation  
#### 2.3.1 Expected Value of Loss  
The expected value of the loss function is calculated by integrating the loss function. From Equations (1), (4), and (8), the probability density function of failure occurrence time \(h_{T}(t)\)  
Figure 3: Conditional probability density function \(g_{S|T=t}(s)\) of detection time \(s\), given \(t\) of the failure occurrence time \(T\).  
failure-cause detection time \(g_{S|T=t}(s)\), and the loss function \(L(s,t)\) are easily multiplied. Then, the expected value of the loss \(E[L]\) is calculated by the following.  
\[E[L] =\int_{0}^{\infty}\int_{0}^{\infty}L(s,t)g_{S|T=t}(s)h_{T}(t)dsdt\] \[=\int_{0}^{\infty}\int_{0}^{t}L(s,t)g_{S|T=t}(s)h_{T}(t)dsdt+\int_{ 0}^{\infty}\int_{t}^{\infty}L(s,t)g_{S|T=t}(s)h_{T}(t)dsdt\] \[=\int_{0}^{\infty}\int_{0}^{t}\alpha\frac{\mu}{c+\mu t}\lambda e^{ \gamma Lt}dsdt\ +\int_{0}^{\infty}\int_{t}^{\infty}[\alpha+\beta+\theta(s-t)] \frac{c\mu}{c+\mu t}\ e^{-\mu(s-t)}\lambda e^{-\lambda t}dsdt \tag{9}\] \[=\int_{0}^{\infty}\alpha\bigg{[}\int_{0}^{t}\frac{\mu}{c+\mu t}ds +\int_{t}^{\infty}\frac{c\mu}{c+\mu t}\ e^{-\mu(s-t)}ds\bigg{]}\lambda e^{- \lambda t}dt+\int_{0}^{\infty}\int_{t}^{\infty}[\beta+\theta(s-t)]\frac{c\mu }{c+\mu t}\ e^{-\mu(s-t)}\lambda e^{-\lambda t}dsdt\] \[=\alpha+\bigg{(}\beta+\frac{\theta}{\mu}\bigg{)}\frac{c\lambda}{ \mu}\ e^{\frac{\omega}{\mu}}E_{1}\bigg{(}\frac{c\lambda}{\mu}\bigg{)}\]  
(See Appendix A)  
For \(x>0\), \(E_{1}(x)\) is the exponential integral function defined by  
\[E_{1}(x)=\int_{1}^{\infty}\frac{e^{-xt}}{t}dt=\int_{x}^{\infty}\frac{e^{-t}}{t }dt=-\gamma-\ln x-\sum_{k=1}^{\infty}\frac{\left(-x\right)^{k}}{kk!}. \tag{10}\]  
where \(\gamma=\lim\limits_{n\rightarrow\infty}\left(\sum_{k=1}^{n}\frac{1}{k}-\ln n \right)=0.577\ldots\) is an irrational number called the Euler constant.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 4 Computational Experiments
### Static RID Model with Limited Vehicles  
After a disaster environment, mostly, supply points have a limited number of available vehicles. Therefore, the effectiveness of the distribution plan depends on the available number of vehicles. In this model, the distribution plan is generated under the static condition to reflect the impact and benefits of a dynamic RID model in a disaster environment. The number of vehicles of each type at each supply point is synthesised. With the limited vehicles and static distribution, the available vehicles can carry relief items only to a few numbers of disaster regions.  
Distribution time is calculated by applying the relation: total distance travel/vehicle speed + service time. The operational cost is calculated as vehicle cost per hour times with the number of hours the vehicle is being used. For the complete distribution schedule, total distribution time and total operational cost are calculated to measure the performance of the distribution schedule. Since the unit of time and cost are different, we applied normalisation to both time and cost. The minimum sum of the normalised distribution time and operational cost is considered the optimised distribution schedule. After 70 generations, the best distribution schedule has:  
Total distribution time: 8.60 h  
Total operational cost: \(\mathcal{E}\) 17,882.86  
The demand status of all disaster regions after the complete distribution schedule has been analysed. It has been observed that with the utilisation of all available vehicles, all the disaster regions do not get the relief items they needed as there was only a limited number of vehicles available. Under the static condition, the simulation result showed that some of the demand regions get their demand fulfilled, some get only partial, whereas some do not get any relief items as can be observed in Figure 3. Unmet demand supply is the major limitation of this approach, which shows that static relief distribution planning with a limited number of vehicles is not an effective approach in a disaster condition. However, optimisation of distribution time and operational cost can be applied by altering the sequence of the distribution sequence. The optimisation distribution sequence finds the minimised distribution time and operational cost of the distribution of relief items with the available resources. To overcome this unmet relief item, demand in disaster region supply points need enough vehicles. However, in most disaster environments, there is always a limitation on the available number of vehicles. Therefore, a static RID model with enough vehicle conditions reflects an infeasible option in most disaster cases. This limitation reflects that the dynamic RID model is essential for relief item distribution in disaster environments.
## 3 Results
### Experimental Results
Initially, Bayes noise reduction was performed to enhance the acquired MRI data, followed by CLAHE for contrast adjustment before transforming them into grayscale. ImFCM was then employed to cluster the images. Subsequently, watershed segmentation was conducted twice with specified marker values (10-90) to capture inner brain features, and again with marker values (10-200) to reveal outer brain details. The resulting images were combined to produce the final segmentation. Finally, postprocessing steps including gamma correction and Gaussian blur were applied to enhance the images. The final segmentation map from the dataset was then fed into the hybrid CNN-LSTM classifier after multiple rounds of tuning to determine the appropriate structure and hyperparameters, as detailed in Tables 1 and 2. The accuracy and loss curves derived from the classification outcomes obtained from both the training and validation sets, once the image data were segmented using the improved ImFCm-WS algorithm and processed with the classification model outlined in this article, are presented in Figure 9. This figure illustrates that the training approach's accuracy and loss converged by the third epoch, indicating high training and testing accuracy levels. The training accuracy was approximately 100%, with a loss of around 7.21  
By combining two robust segmentation techniques and two hybrid classification models, the updated model reveals enhanced details. The attributes acquired from these hybrid methods diverge; their integration strengthens the features, thereby improving the categorization results. Table 4 displays the classification report for the training model for each class. The precision values for the Mild, Normal, Moderate, and Very Mild instances are 97%, 100%, 99%, and 97%, respectively. The recall rates for these classes are 99%, 87%, 98%, and 99%, respectively. Lastly, the F1 scores stand at 98%, 93%, 98%, and 98%. The outcomes of the adapted model, as utilized in this study, consistently demonstrate outstanding performance, underscoring the effectiveness of employing sophisticated MRI segmentation to enhance AD diagnostic classification. Following thorough training, the system was tested using a separate testing set, comprising images not included in the training process. With our recommended segmentation technique, the CNN model achieved a 98.13% accuracy and effectively operated on the MRI data.
## 3 Results and Discussion
### Development of MW-FLR Method
#### 3.2.2 Optimization of Method Variables
The potential interference from the matrix of capsules (inactive ingredients used for manufacturing) was studied. Microcrystalline cellulose, colloidal silicon dioxide, crospovidone, and magnesium stearate were mixed in various proportions with known amounts of DUV to produce the samples. After excitation at 275 nm, the fluorescence intensity of DUV was then measured at 405 nm. A satisfactory recovery% of 99.4 \(\pm\) 2.4% was attained, showing that the excipients had no effect on the analysis accuracy. This was attributed to the fact that the proposed MW-FLR relies on the measurement of the native fluorescence of DUV; however, none of the inactive ingredients that can be co-extracted from copiktra capsules has native fluorescence. The effect of the plasma matrix was also evaluated by analyzing blank DUV-free human plasma samples and plasma samples spiked with varying concentrations of DUV (100, 450, and 600 ng/mL). The fluorescence spectra of these samples were recorded, and recovery values were calculated for each case. It was found that the fluorescence emitted from blank plasma samples after excitation at 275 nm is overlapping the emission spectrum of DUV leading to interference and accordingly high recovery values (Figure 5, line 1). However, this interference was completely avoided when the sample was excited at 340 nm instead of 275 nm as evidenced by the fluorescence spectrum that showed negligible fluorescence at the emission maximum of DUV (Figure 5, line 2). Meanwhile, the fluorescence emitted from DUV-containing plasma samples after their excitation at 275 was increased with the DUV concentrations (Figure 5, lines 3, 4, and 5). Recovery values obtained from these samples were satisfactory (98.2-103.5%). Based on these results, fluorescence intensities were measured at 405 nm after excitation at 340 nm in all the subsequent validation experiments.
## 5 Discussion  
### Aquifer Geometry  
Aquifer geometry assessment was performed using two conceptual hydrogeological cross-sections from the ICGC [42; 43] and comparing data from the detailed 2D ERT cross-sections and lithological logs of boreholes from SAT facilities project.  
Among the geometry model (Figure 6) and hydrogeological cross-sections (Figure 3), there are thickness and general morphology discrepancies. However, the model shows a similar contact morphology when compared to longitudinal geological cross-section and similar maximum depth when comparing to transverse conceptual section. In both directions, average aquifer thickness is consistent.  
Based on the information from the lithological logs of research piezometers, we identify also issues in correlating geoelectrical response and lithological logs if boreholes are more than eight meters apart. As an example, the ERT cross-section P12 results are easy to correlate with the lithological log of piezometer S3 located eight meters apart. We could identify an increment in resistivity values at two meters above sea level (masal) and a lithological change at the same position. On the other hand, the P11 ERT cross-section and the borehole S6 have 40 m distance among them. The ERT results show a resistivity change at five masal and borehole log S6 identifies a lithological change at three meters below sea level (Figure 8). This fact is probably due to high heterogeneity and non-regular contact between aquifer/aquiclude in the studied area and the majority of classical quantitative hydrogeophysical studies do not specify the issue as they have been performed at the local scale (\(\sim\)10 m), where the scale disparity between direct (wellbore) and indirect (geophysical) measurements is often not significant [74].  
Figure 7: 3D resistivity plain view and cross-section model of the study area inferred from ERT data. Resistivity values lower than 10 Ohm.m are assumed as a threshold of seawater intrusion delineation. Distances are in meters.  
As stated by [PERSON] [75], subsurface imaging is a convenient asset, as it can help describe the geometry of a heterogeneous geological system and as previously stated by the authors of [76; 77], small-scale data obtained using only wellbore-based methods and in any case simple correlation of layers identified in stratigraphic logs may not provide information about the full 3D geometries of the aquifers to be reconstructed.
## 4 Discussion
### The Impact of Blow-Up Ratio (BUR) on Mechanical Properties  
When correlating the BUR with the mechanical properties of the samples, an interesting result was obtained. Normally, the BUR has a direct impact on tensile properties only in the transverse direction, because the BUR is related to the width given to the product based on the amount of air contained in the bubble, which when increased, stretches the film in the transverse direction during the process. This is the case, but there was also a correlation found in the machine direction. All the values for the correlation coefficient (R\({}^{2}\)) regarding the BUR and the four mechanical properties measured (elongation at break in the MD and TD, and tensile strength at break in the MD and TD) were between 0.91 and 0.94, as seen in Figures 4-7, as second-degree polynomial functions.  
Figure 4: Polynomial correlation between BUR and \(\varepsilon\) MD.  
As a general observation, all the functions with the best correlation in the case of the BUR, which is mainly a parameter with an impact on axial or transverse direction properties, are polynomial second-degree functions.
_Article_  
**Electrical Monitoring as a Novel Route to Understanding the Aging Mechanisms of Carbon Nanotube-Doped Adhesive Film Joints**  
**[PERSON] \({}^{1,2,*}\)**, [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\)** and [PERSON] \({}^{2}\)  
\({}^{1}\) Department of Aerospace Materials and Processes, Escuela Tecnica Superior de Ingenieria Aeronautica y del Espacio, Universidad Politecnica de Madrid, Plaza [PERSON] 3, 28040 Madrid, Spain;  
[EMAIL_ADDRESS]  
\({}^{2}\) Materials Science and Engineering Area, Escuela Superior de Ciencias Experimentales y Tecnologia, Universidad Rey Juan Carlos, Calle Tulipan s/n, 28933 Mostoles (Madrid), Spain;  
[EMAIL_ADDRESS] (A.J.-S.); [EMAIL_ADDRESS] (M.S.); [PERSON] (S.G.P.);  
[EMAIL_ADDRESS] (A.U.)  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +34914884771  
Received: 19 March 2020; Accepted: 3 April 2020; Published: 8 April 2020  
**Featured Application: Structural Health Monitoring of novel carbon nanotube doped adhesive joints under aging conditions.**  
**Abstract: Carbon fiber-reinforced plastic bonded joints with novel carbon nanotube (CNT) adhesive films were manufactured and tested under different aging conditions by varying the surfactant content added to enhance CNT dispersion. Single lap shear (SLS) tests were conducted in their initial state and after 1 and 2 months immersed in distilled water at 60 \({}^{\circ}\)C. In addition, their electrical response was measured in terms of the electrical resistance change through thickness. The lap shear strength showed an initial decrease due to plasticization of weak hydrogen bonds, and then a partial recovery due to secondary crosslinking. This plasticization effect was confirmed by differential scanning calorimetry analysis with a decrease in the glass transition temperature. The electrical response varied with aging conditions, showing a higher plasticity region in the 1-month SLS joints, and a sharper increase in the case of the non-aged and 2-month-aged samples; these changes were more prevalent with increasing surfactant content. By adjusting the measured electrical data to simple theoretical calculations, it was possible to establish the first estimation of damage accumulation, which was higher in the case of non-aged and 2-month-aged samples, due to the presence of more prevalent brittle mechanisms for the CNT-doped joints.**  
**Keywords: carbon nanotubes; aging; structural health monitoring; water uptake; adhesive film; surfactant**
# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 4 Proposed Study
### Proposed Feature Extraction Models  
For image data, the study plans to extract four mandatory features, namely color, edge, height, and width, from the image data for effective fusion. Two innovative models have been proposed for edge detection and color identification.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 3 DVB-T Waveform and BFISAR Signal Synthesis
### BFISAR Signal Synthesis  
Assume the object depicted in a 3-D regular grid with scattering points placed in its nodes is illuminated by DVB-T waveforms. Partial signals reflected from the target's scattering points are superposed according to time delays defined by distances \(R_{ijk}(p)\) and the light speed. The segments' repetition period during the aperture synthesis can be defined by \(T_{p}=\Delta.l.T_{S}\), where \(\Delta l\) is the interval between segments' indices used. The total time of the aperture synthesis can be calculated by \(\hat{T}=m\times\Delta.l.T_{S}\), where the parameters' values, \(m\) and \(\Delta.l\), depend on the synthetic aperture length, object's velocity, and realized azimuth resolution. For example, to realize an appropriate segments' repetition period, symbols indexed by \(l=0,\ 5,\ 10,\dots,\ 65\) for \(m=\overline{0,\ 9}\) frames can be registered and used for aperture synthesis.  
The complex BFISAR signal reflected from the \(ijk\)-th scattering point can be expressed as:  
\[S_{ijk}(k,p)=\text{rect}\biggl{(}\frac{t-t_{ijk}}{T_{U}}\biggr{)}a_{ijk}.\exp \Bigl{\{}j.2\pi.\Bigl{[}\bigl{(}f_{0}+k.\Delta f.\bigr{)}\Bigl{(}t-t_{ijk} \Bigr{)}-k.\Delta f.\widetilde{t}\Bigr{]}\Bigr{\}} \tag{10}\]  
for \(k=\overline{-(K/2)+1,(K/2)}\), and \(p=\overline{0,\ N-1}\), where \(a_{ijk}\) is the intensity of the \(ijk\)-th scattering point, and \(t_{ijk}=t_{ijk}(p)=\frac{R_{ijk}(p)}{c}\) is the signal time delay from the \(ijk\)-th scattering point.  
The rectangular function \(\text{rect}\frac{t-t_{ijk}}{T_{U}}\) determines the DVB-T symbol size in the synthesized BFISAR signal, and can be defined as:  
\[\text{rect}\frac{t-t_{ijk}}{T_{U}}=\Bigg{\{}\begin{array}{ll}1&\text{if}\ -0.5 \leq\frac{t-t_{ijk}}{T_{U}}<0.5\\ 0&\text{otherwise}\end{array} \tag{11}\]  
The complex BFISAR signal reflected from the 3-D object can be written as:  
\[S(k,p)=\sum_{ij,k}\text{rect}\Big{(}\frac{t-t_{ijk}}{T_{U}}\Big{)}a_{ijk}. \exp\Big{\{}j2\pi.\Big{[}\big{(}f_{0}+k.\Delta f.\big{)}\Big{(}t-t_{ijk}\Big{)} -k.\Delta f.\tilde{t}\Big{]}\Big{\}}. \tag{12}\]  
for \(k=\overline{-(K/2)+1,(K/2)}\) and \(p=\overline{0,\ N-1}\).  
While BFISAR signal modeling, the current time \(t\) measured in the range direction is defined by the expression:  
\[t=t_{ijk\text{min}}(p)+\tau, \tag{13}\]  
where \(t_{ijk\text{min}}(p)=\frac{R_{ijk\text{min}}(p)}{c}\) is the reference time defined by the signal time delay from the nearest scattering point, \(\tau=k.\Delta T\) is the fast time, \(\Delta T=T_{U}.K^{-1}=0.1314\)\(\upmu\)s is the time width of the DVB-T waveform's sample, \(\Delta f=T_{U}{}^{-1}=1.116\) kHz is the carrier spacing, and \(\Delta F=\left(\Delta T\right)^{-1}=7.6104\) MHz is the DVB-T sample's frequency bandwidth.  
Equation (12) describes the process of the BFISAR signal formation and can be interpreted as a spatial transformation of the 3-D image function \(a_{ijk}\) into a 2-D BFISAR signal \(S(k,p)\), defined in discrete range \(k\) and cross-range \(p\) coordinates.