esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 4 Effect of the Modification with Nanomaterials in the Mechanical Performance of Asphalt Mixtures
### [PERSON] and [PERSON] [49] evaluated the mechanical properties of a stone mastic asphalt (SMA) mixture modified with 0.3%, 0.6%, 0.9%, and 1.2% of nano Fe\({}_{2}\)O\({}_{3}\). The 60/70 asphalt binder was the control binder used in the study and, all the SMA were produced with 6.6% binder content. The authors tested for permanent deformation using the wheel-tracking test, for stiffness using the indirect tensile stiffness modulus (ITSM) and for fatigue using the indirect tensile fatigue test (IIFT). In all the tests, initially the performance improved, reaching peak with the 0.9% dosage, and then decreased for higher dosage. Regarding the 0.9% nano Fe\({}_{2}\)O\({}_{3}\) modification; stiffness modulus increased by 28%, 14%, and 45%, at 5 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, and 25 \({}^{\circ}\)C, respectively; fatigue life increased between 15% and 35%, depending on the temperature and stress level; and permanent deformation decreased by 48%, 35% and 18%, at 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, respectively.  
[PERSON] et al. [50] studied an asphalt mixture (NMAS 12.5 mm) modified with nano Fe\({}_{2}\)O\({}_{3}\). The 60/70 asphalt binder (PG 64-22) was modified with the nanomaterial dosages of 0.1%, 0.4%, 0.8%, and 1.2%. The authors studied the fracture properties of the asphalt mixture by conducting semi-circular bending (SCB) tests. Regarding the nano Fe\({}_{2}\)O\({}_{3}\) modification, the fracture resistance initially increases reaching the peak at 0.8% dosage (27% increase), and then decreases for higher dosage.  
[PERSON] et al. [25; 36] studied an asphalt concrete (AC 14) mixture modified with 4% nanioron (zero-valent iron). For the mixture production was used granitic aggregates, limestone filler, 35/50 asphalt binder (control binder), and 4.5% mixture binder content. The modification had the following effects: 5% increase in Marshall stability; improvement in retained Marshall stability from 77% to 80%, 5% increase in indirect tensile strength; improvement in indirect tensile strength ratio from 57% to 82%, improvement in affinity aggregate-binder (from 40% to 70% of binder coverage); 20% reduction in permanent deformation; 7.3% and 1.5% reduction in stiffness modulus at 20 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, respectively; and better fatigue resistance (4% increase in the strain for 10\({}^{6}\) cycles).  
The authors studying the use of nanioron to modify the asphalt mixture indicate several improvements in mechanical performance, such as, higher Marshall stability, higher indirect tensile strength, enhanced water sensitivity (indicated by higher indirect tensile strength ratio and higher retained Marshall stability), lower permanent deformation, better fatigue, and fracture resistance.
## 3 Results and Discussion
### Characterization
#### 3.2.4 TGA  
Figure 11 shows the TGA curves of both the PAC-PDMDAAC composite flocculant and PAC-PDMDAAC hybrid flocculant. Figure 9 provided the effect of temperature on product weight and the rate of weight change (dw/dt). Due to their inherent hygroscopicity, water molecules were evaporated during the heating process, as depicted in Figure 11, thus exhibiting a substantial weight loss prior to reaching temperatures above 150 \({}^{\circ}\)C due to water loss. As the temperature increased further, structural units within the flocculant molecules commenced decomposition, leading to bond breakage along molecular chains and a subsequent increase in weight loss until the complete decomposition  
Figure 10: Variation in conductivity of PAC-PDMDAAC hybrid flocculant and PAC-PDMDAAC composite flocculant with concentration.  
Figure 9: SEM pictures at 1.5k of PAC-PDMDAAC composite flocculant (**a**) and PAC-PDMDAAC hybrid flocculant (**b**).  
of organic components occurred. Notably, it can be observed in this figure that the maximum thermal decomposition rate for the hybrid flocculant was achieved at approximately 264.38 \({}^{\circ}\)C. However, the maximum thermal decomposition rate of the composite flocculant appeared at 256.36 \({}^{\circ}\)C. The TGA test report indicated an initial decomposition temperature of 230.29 \({}^{\circ}\)C for the PAC-PDMDAAC hybrid flocculant, which was significantly higher than that of 214.53 \({}^{\circ}\)C for the composite flocculant. The calculated half-life temperature for thermal decomposition of the hybrid flocculant was 671.93 \({}^{\circ}\)C, which was also higher than 621.29 \({}^{\circ}\)C for the composite flocculant. This may be because the organic and inorganic components of the hybrid flocculant were bonded in the form of covalent bonds, and the organic macromolecular chain end was fixed by polyaluminum chloride aluminum particles, which weakened the thermal motion of the macromolecular chain, thereby improving its heat resistance and increasing its thermal decomposition temperature.
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 3 Experimental Results
### Settling Time  
Figure 11a,b shows the settling time for conventional SD systems and SD systems with phase compensation, respectively. The response time of conventional SD systems predominantly relies on the settling time of filters used in the output stages. SD with phase compensation exhibits a slightly longer response time with step changes in input voltage amplitude. The reason for this is that the final settled value of output low-pass filters depends on the time required by the all-pass filter to add an adequate delay to synchronise the sense voltage Vsense with the demodulation signals in phase.  
The system's settling time at 1 kHz with a typical load of 4 k\(\Omega\) was monitored by capturing the output of the low-pass filter at a step input of 0.9 V. Settling time was measured as the time required for the system to settle at 97% (1.74 V) of the final expected value of 1.8 V for a filter gain of 2. The SD system with phase compensation was 445 ms slower than without compensation. This difference may not be a significant concern for most bioimpedance applications, where the measured impedance is relatively slow, varying with applications where specific frame rate requirements are required for multiple channel recordings, as in electrical impedance tomography [3; 31].  
Figure 11: System step response. (**a**) Step response for SD system without phase compensation. (**b**) Step response for SD system with phase compensation.  
Figure 10: Impedance magnitude and phase percentage error for RC loads as a function of frequency. (**a**) Percentage impedance magnitude error for RC loads. (**b**) Percentage phase error for RC loads.
###### Abstract
These effects change the microstructure of the cement paste, inevitably leading to changes in the macroscopic mechanical behavior of concrete. Macroscopically, studies often use cubic, cylindrical, and prismatic specimens to analyze HPC's mechanical and durability properties. Extensive research indicates that fly ash and mineral powder, as mineral additives, can effectively enhance HPC's mechanical and durability properties [16; 18; 19; 20; 21; 22; 23; 24]. However, the fatigue performance of bridges is related to their actual size [25], and these testing methods do not account for the actual size of bridges in engineering projects, thus failing to obtain the flexural fatigue behavior of bridge structures directly. To address this issue, in recent years, some scholars have designed scaled-model experiments of HPC bridges based on similarity theory. The HPC bridges' strain, displacement, and crack distribution were tested through cyclic loading, thus elucidating their fatigue behavior. These studies are listed in Table 1[26; 27; 28; 29; 30]. Table 1 shows that increasing the fly ash content and concrete strength positively affects HPC bridges' fatigue behavior. However, existing scaled-model experiments mainly focus on HPC bridges with fly ash, with little research on PB-DA. Moreover, fly ash and mineral powder are industrial waste products from the power and steel industries. Previous research on prisms and other experiments indicates that utilizing fly ash and mineral powder can enhance the working performance of HPC and effectively solve the problem of industrial waste disposal. Therefore, using scaled-model experiments to explore the flexural fatigue behavior of PB-DA holds significant engineering value. For this purpose, this paper derives the similarity criteria for simply supported beam bridges under concentrated load based on similarity theory and, according to this criterion, scales down a bridge with a 30 m span to a 6 m span. A total of 18 stages of loading schemes were designed, including (1) preloading to test equipment reliability; (2) cyclic loading to simulate repetitive loads during operation; (3) graded loading to test the PB-DA's flexural fatigue behavior; and (4) static loading to test the mechanical properties of the flexural fatigue behavior after fatigue loading. During the cyclic loading process, the PB-DA's flexural fatigue behavior is measured at specific intervals using graded loading. Three testing schemes were designed: (1) a strain gauge layout to measure the full-section strain variation from the top to the bottom of the PB-DA; (2) a dial indicator setup to measure the PB-DA's maximum deflection; and (3) a grid drawing scheme to measure the crack distribution pattern of the beam. By constructing PB-DAs with concrete strength grades of C50 and C80 and admixture contents of 10% to 32%, this study obtains the strain and flexural stiffness evolution under fatigue loading, as well as the mechanical properties and crack distribution patterns after the fatigue loading of the PB-DA. The research conclusions provide a reference for the application of PB-DA.
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 3 Methodology
### Estimation of Incident Radiation
As a result, Equations (2)-(4) are transformed into:\[E_{b}^{y}=\Delta t\sum_{i=1}^{N}F_{i}\mathrm{DNI}_{i}\cos(\mathrm{AOI}_{i})=\sum_{ p=1,q=1}^{N_{p},N_{q}}F_{p,q}E_{p,q}^{\mathrm{DNI}}\cos(\mathrm{AOI}_{p,q}) \tag{5}\]  
\[E_{d}^{y}=\Delta t\frac{1+\cos\beta}{2}\sum_{i=1}^{N}\mathrm{DHI}_{i}=\frac{1+ \cos\beta}{2}\sum_{p=1,q=1}^{N_{p},N_{q}}E_{p,q}^{\mathrm{DHI}} \tag{6}\]  
\[E_{g}^{y}=\Delta t\rho\frac{1-\cos\beta}{2}\sum_{i=1}^{N}\mathrm{GHI}_{i}=\rho \frac{1-\cos\beta}{2}\sum_{p=1,q=1}^{N_{p},N_{q}}E_{p,q}^{\mathrm{GHI}} \tag{7}\]  
where \(N_{p}\), \(N_{q}\ll N\), and, using [PERSON]'s notation,  
\[E_{p,q}^{\mathrm{DNI}}=\Delta t\sum_{\alpha_{i},\beta_{i}}[\Delta\alpha(p-1) \leq\alpha_{i}<\Delta\alpha p][\Delta\theta(q-1)\leq\theta_{i}<\Delta\theta q ]\mathrm{DNI}_{i} \tag{8}\]  
\[E_{p,q}^{\mathrm{DHI}}=\Delta t\sum_{\alpha_{i},\beta_{i}}[\Delta\alpha(p-1) \leq\alpha_{i}<\Delta\alpha p][\Delta\theta(q-1)\leq\theta_{i}<\Delta\theta q ]\mathrm{DHI}_{i} \tag{9}\]  
\[E_{p,q}^{\mathrm{GHI}}=\Delta t\sum_{\alpha_{i},\beta_{i}}[\Delta\alpha(p-1) \leq\alpha_{i}<\Delta\alpha p][\Delta\theta(q-1)\leq\theta_{i}<\Delta\theta q ]\mathrm{GHI}_{i} \tag{10}\]  
Equations (8)-(10) allow for the calculation of the average direct, diffuse, and global components of solar irradiance for each portion of the sky as a function of the \(p,q\) indices of azimuth and zenith. As a result, the shading calculation for each pixel only needs to be performed a few hundred times, i.e., for combinations of \(\alpha_{p}\) and \(\theta_{q}\) angles for which \(\mathrm{DNI}_{p,q}>0\). By repeating the computations for each pixel of the DSM raster belonging to the area inside the perimeter of the buildings, we thus obtain the 1 m resolution map of the mean annual irradiance on the plane of the array \(E_{\mathrm{FOA}}^{y}\), and the components \(E_{b}^{y}\), \(E_{d}^{y}\) and \(E_{g}^{y}\) for all the buildings in Sardinia. Figure 8a shows an extract of the global horizontal irradiance (GHI) map, whereas Figure 8b depicts the irradiance on the plane of the array (POA). It is immediately noticeable how the darker areas, i.e., those facing north or shaded by other buildings, receive less radiation than the light-colored areas do, i.e., those facing south or are taller than the surrounding buildings. \[E_{b}^{\text{y}}=\int_{0}^{T}E_{b}(t)dt\approx\Delta t\sum_{i=1}^{N}E_{b,i}= \Delta t\sum_{i=1}^{N}F_{i}\text{DNI}_{i}\cos(\text{AOI}_{i}) \tag{2}\]0 _Cont._
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 5 Energy-Saving Policy and Consumption Estimation
### Energy-Saving Policy
As soon as the module is turned on, then the ATmega328 firmware arranges the transmission by means of the SPI bus (MISO and MOSI signals for data and CS and SCK for controlling the communication) and when it is over, it turns off the module lowering the logic level of the pin connected to the aforementioned MOSFET. 5. At this point, the tasks of the IoT node (i.e., the measurement and transmission of the bin filling level) are accomplished. Therefore, the microcontroller, via firmware, raises the logic level of the digital pin connected to the Master Reset input of the HEF4060B (i.e., pin 12) involving the reset of the counting process which obviously means that the MSB logic level lowers resulting in the deactivation of the ATmega328. Since the counting process has just restarted, due to the fact that the HEF4060B is constantly powered, the IoT node is starting at the same time another functioning cycle. For a matter of completeness, the actual period of the policy can be evaluated. A cycle is mainly made up of two parts: the first one which corresponds to 57 m within which the MSB is at a low logic level, and the second one which roughly corresponds to 30 s within which the MSB is at a high logic level allowing the measurement and the transmission. The extend of the second part of the period was empirically measured: there exist ad-hoc firmware instructions consenting the measurement of the time spent by the microcontroller to carry out its tasks. It was found out that, on average, 30 s are enough to guarantee the proper functioning of the microcontroller. In other words, the yellow bar in  
Figure 6: Flow chart of the energy-saving policy. Figure 6 is 30 s length. Therefore, a single sampling measurement period \(T_{m}\) has a duration of 3460 s and 30 s: \(T_{m}\) = 3490 s. This figure will turn out to be useful in the following subsection.
# A Survey of Deep Learning for Data Caching in Edge Network
## 3 Deep Learning Outline
### Recurrent Neural Network (RNN)
#### 3.3.1 Echo-State Network (ESN)  
As aforementioned, simple RNN contains more parameters in the training step, where the recurrent weights and input weights are difficult to learn [43]. The basic idea of ESN is fixing these two kinds of weights and only learn the output weights (as links highlighted in Figure 4). The hidden layer is renamed as reservoir in ESN, where the neurons are sparsely connected and the weights are randomly assigned. The recurrent weights keep constant so the information of previous moments is stored in the reservoir with constant weight, like voice echoing. To avoid echo explosion, the eigenvalues of recurrent weights should not be greater than 1. On the one hand, the preassigned weights of input and reservoir layers reduce the number of training parameters then accelerate the training process; on the other hand, since the connectivity and weight value are random, so the result is unlikely to be optimal.  
Figure 4: Recurrent Neural Network (RNN) variants.  
ESN has been successfully utilized in speech processing, stock price prediction, language modeling, etc. However, when it comes to high dimensional temporal task, ESN is less competitive because only the output weights are flexible during training. In order to extract different features, the reservoir should be enough large, which challenges the ESN design.
## 6 Experiments and Results
### Comparison with Basic Classifiers  
Based on the analysis of the previous experiments, the SLFN classifier with \(k\) value of 3 and SVM-SMOTE oversampling technique with 0.9 oversampling ratio were considered for evaluating the proposed framework, by comparing the results with SVM, SGD, LR, SLFN, and NB. Table 4 shows the values of ACC, PREC, REC and GM for SLFN-SVM-SMOTE (\({}_{\text{r = 0.9, k = 3}}\)), SVM, SGD, LR, SLFN, and NB. Ignoring the results achieved by NB, as discussed in Section 6.4, the maximum values from the other techniques are determined in bold. Thus, the table shows that the PREC\({}_{\text{N}}\), REC\({}_{\text{I}}\), and GM values of the proposed approach are larger than those of the other classifiers. Consequently, the GM value indicates an advanced performance for the proposed framework.
## 3 Proposed Method
### Network Architecture  
SSIN structure is based on deep convolutional GAN (DCGAN) [20] with one convolutional generator and a convolutional discriminator. We adopt the U-Net-based [5] convolutional encoder-decoder [14] model for better generator performance. The discriminator follows the basic structure of DCGAN, consisting of 6 convolution layers and 1 fully connected layer. The details of the generator structure are shown in Table 1. At the encoder stage of the SSIN, it extracts features from the segmentation map with convolutional filters, reducing the resolution. Strided convolution with a rate of 2 is used to reduce it by one-fourth of the original size to improve the performance. After the encoder stage,  
Figure 4: Dynamic removal results under different settings of the model: (**a**) Input Image; (**b**) M1: \(L_{CE}\); (**c**) M2: \(L_{CE}+L_{GAN}+L_{SE}\); (**d**) M3: \(L_{CE}+L_{GAN}+L_{SE}+L_{TV}\). the decoder restores the missing area with the extracted features of the input map. Skip connections are added to help the decoder directly obtain features from the encoder for better restoration [14]. Unlike other architectures that use deconvolution to increase the resolution, our model increases the resolution twice using nearest neighbor interpolation to avoid generating checkboard artifacts of the output map [36]. The discriminator network architecture is based on convolutional layers that extract features by steadily reducing the resolution. In the last layer, the fully connected layer is connected to provide domain information on the input. The discriminator structure follows [20] for a discriminator network as listed in Table 2.  
Leaky rectified linear unit [37], defined as \(\sigma(x)=\max(a\times x,x))\), is used for the mid-layer activation function of the networks and is placed after the convolutional layers for nonlinear activation functions, which prevents the dying problem of ReLU. In addition, batch normalization [38] is performed between the activation function and the convolutional layer to stabilize the propagation of gradients through the network. The normalization forms the output of each layer with the Gaussian distribution of the zero mean and unit variance, which resolves the gradient vanishing problem and boosts the model to learn. This structure applies to all generator and discriminator layers except the last ones. The softmax function is applied in the last layer of the generator to make the segmentation map, and the sigmoid is placed on the discriminator's last layer to represent the probability that the input is real rather than restored.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 4 Traffic Performance Analysis
### Unidirectional Communication  
#### 4.1.1 In EFA  
In EFA, the first sent frame is forwarded and circulated in all DANH and QuadBox rings except the destination DANH ring. The number of network frames generated in the network upon delivering the sent first frame, denoted by \(nf^{1}_{EFA}\), is calculated as:  
\[nf^{1}_{EFA}=\sum_{i\in NR}2n_{i}-n_{d} \tag{1}\]  
where \(NR\) is the set of all rings in the network; \(n_{d}\), the number of nodes in the destination DANH ring; and \(n_{i}\), the number of nodes in the \(i\)th ring.  
When the destination receives the first sent frame, it sends a Locking message back to the source to discover passive QuadBox rings in the network. The Locking message is forwarded to the destination DANH ring and active QuadBox rings. The number of network frames generated by the locking message, denoted by \(nf^{lock}_{EFA}\), is calculated as:  
\[nf^{lock}_{EFA}=n_{d}+\sum_{i\in AQR}n_{i} \tag{2}\]  
where \(AQR\) is the set of all active QuadBox rings.  
For the \(k\)th\((k\geq 2)\) frame, EFA does not forward the frame to nondestination DANH ring and passive QuadBox rings. The number of network frames generated by the \(k\)th frame (\(k\geq 2\)), denoted by \(nf^{k}_{EFA}\), is calculated as:  
\[nf^{k}_{EFA}=n_{s}+n_{d}+\sum_{i\in AQR}n_{i} \tag{3}\]  
where \(n_{s}\) is the number of nodes in the source DANH ring.  
Generally, the number of network frames when the source sends \(N\) unicast frames to the destination in unidirectional communication in EFA, denoted by \(nf^{uni}_{EFA}\), is calculated as:  
\[nf^{uni}_{EFA}=nf^{1}_{EFA}+nf^{lock}_{EFA}+(N-1)nf^{k}_{EFA} \tag{4}\]  
By substituting \(nf^{1}_{EFA}\), \(nf^{lock}_{EFA}\), and \(nf^{k}_{EFA}\), \(nf^{uni}_{EFA}\) can be calculated as:  
\[nf^{uni}_{EFA}=\sum_{i\in NR}2n_{i}+(N-1)(n_{s}+n_{d})+N\sum_{i\in AQR}n_{i} \tag{5}\]
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 2 Experimental
### Preparation of Nanomaterials  
GO nanosheets were synthesized by the graphite-modified Hummers method [18].  
Nitrogen-doped TiO\({}_{2}\)(NT) nanoparticles were prepared by sol-gel method and high-temperature calcination. First, 10 mL of tetrabutyl titanate was added to 20 mL of absolute ethanol to form Solution A. Solution B was a mixture of 10 mL of absolute ethanol, 15 mL of deionized water and 4.5 mL of acetic acid, added to 0.01 g urea. After adjusting the pH of solution B to 2-3 with HCl aq., solution B was slowly added dropwise to solution A under vigorous stirring. A mixed solution was obtained after the completion of the titration and stirring was continued for about 1 h under the action of a magnetic stirrer. Then it was aged for 2 h to form a yellow transparent gel. The mixture was dried in an oven at 100 \({}^{\circ}\)C and yellow crystals formed. The solid was converted into a white powder by grinding. It was then calcined at 500 \({}^{\circ}\)C for 2 h to complete the preparation of NT. The preparation of TiO\({}_{2}\) was the same as in the above case, except urea was not added.  
Nitrogen-doped TiO\({}_{2}\)/ GO(NTG) was prepared by a hydrothermal method. First, 0.05 g of GO was dispersed in 80 mL of deionized water and sonicated for 1 h. An additional 1 g of NT was added and a further 1 h sonication was required. Then the blend suspension was transferred to a 100 mL Teflon-lined stainless steel autoclave, which was kept at 180 \({}^{\circ}\)C for 18 h, and cooled to room temperature. Finally, the suspension was centrifuged at 5000 rpm, then washed with deionized water three times, and dried under a vacuum at 60 \({}^{\circ}\)C. TG was prepared by the same method but without urea.