esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Alkaline Environment Evolution  
Figure 6a-c systematically reveal the dynamic influence patterns of humidity gradients on pore solution pH under varying temperature conditions. Under identical testing depths, specimens at 70% RH demonstrated the highest pore solution pH values and achieved dynamic equilibrium most rapidly, stabilizing within the 12.4-12.6 range. This phenomenon originates from humidity gradient-driven ion migration effects: under low humidity (60% RH), alkaline ions preferentially migrate toward the specimen surface along the humidity gradient, resulting in internal alkalinity reduction and suppressed C-A-S-H gel formation efficiency during the post-curing stages. Concurrently, surface alkaline ion accumulation accelerated carbonation reaction kinetics, thereby inducing instability in harmful pore structure filling processes and a consequent strength degradation. In contrast, high humidity (80% RH) environments triggered solution dilution effects through external moisture infiltration, significantly reducing internal pore liquid alkalinity and impairing C-A-S-H gel generation. Specifically, the humidity difference between the environment and the specimen induces the directional flow of a pore solution from the surface to the interior of the specimen. Water condensation on the specimen surface causes a dilution of the surface ions relative to the interior. This drives a diffusion transport of ions from the interior to the specimen surface and results in alkali depletion in the pore solution [48].  
Figure 6: Humidity effects on pore solution pH at varying depths under different temperatures: (**a**) 10 \({}^{\circ}\)C, (**b**) 20 \({}^{\circ}\)C, (**c**) 30 \({}^{\circ}\)C.  
Figure 7a-c illustrate the dynamic regulatory effects of temperature variations on pore solution pH under different humidity conditions. The experimental data revealed that pore solution alkalinity at equivalent depths peaked at 20 \({}^{\circ}\)C and minimized at 30 \({}^{\circ}\)C. This phenomenon is attributed to the densified carbonation product layer formed at 20 \({}^{\circ}\)C, which acts as a CO\({}_{2}\) diffusion barrier to preserve internal alkalinity. Conversely, low-temperature conditions (10 \({}^{\circ}\)C) suppressed CO\({}_{2}\) diffusivity, limited Ca\({}^{2+}\) dissolution rates, and retarded pore water evaporation, collectively inducing alkaline ion sequestration that accumulated internal alkalinity. When the temperature increased to 30 \({}^{\circ}\)C, accelerated pore solution evaporation triggered a surface enrichment of the alkaline ions, intensifying the carbonation reactions and depleting the internal alkalinity, consequently impairing the hydration reaction efficiency during subsequent curing.
## 3 Smart Sensor Monitoring Approach  
In this work, a new methodology for smart sensor monitoring to estimate tool wear during the drilling of composite materials is proposed. The logic scheme of the proposed framework is reported in Figure 4. The raw sensor signals acquired during the drilling process are provided in input to a pre-trained autoencoder network which performs feature extraction from the signal and acts as a denoising filter. The features extracted by the encoder part of the autoencoder architecture are used as input for the following software modules, which, using a pre-trained memory-based neural network, provide as output the estimated flank wear value, VB. It is worth mentioning that, in this approach, the memory-based neural network receives as input both current and past information coming from the autoencoder.
## 3 Results
### Technical and Diagnostic Success Rates
#### 3.5.3 Whole Procedure  
In total, there was no significant difference in whole procedure duration or in the number of control acquisitions (\(p\) = 0.497 and \(p\) = 0.066, respectively). However, the total radiation dose was significantly lower (\(p\) < 0.01) in the NAV-group than in the CT-group (1059 mGy.cm (747; 1456) versus 1481 mGy.cm (1255.5; 1873); \(p\)< 0.0001).
## 3 Background
### Cross-Technology Interference Sources
#### 3.1.2 Microwave Ovens  
The energy leakage from the residential MWOs usually affects the whole 2.4-GHz band. However, as depicted by various studies [29; 35], the RF emissions from MWOs peak at about a 2.45-GHz frequency, while the number and center frequencies of peaks may vary slightly according to the specific model, as shown in [36]. As a result, the IEEE 802.15.4 Channels 20 and 21 have a high probability of being strongly affected by the MWO operation. A prominent feature of MWO is the periodicity of _on_ and _off_ phases during the heating process, where the time from one _on_ phase to the next is \(\frac{1}{2f}\) s, with \(f\) the frequency of the power supply (i.e., 50/60 Hz).
## 3 Data Analysis Setup
### Feature Engineering  
The new summative approach to normalized wavelet coefficients has condensed the features with respect to the specific time series from sphere response, equivalent to a particular incident wave characteristic. The common sense is to relate each feature within the vector K to the given wave specification as the class label, i.e., parameters such as peak period, significant wave height, and sea state condition in general. However, the direct establishment of this relationship ends up with the non-uniqueness problem for features, and some operations are required to turn the features in K into more unique and specific  
Figure 8: Manifestation of features after operation given in Equations (5)–(7) in new form with respect to scalograms, with green dots for left image and red dots for right scalogram.  
combinations relating to the given label. As per seminal study, this can be done by the reorganization of features within K based on the intensity such that K is divided into two vectors of equal size, A and B. Vector A\({}_{\text{a,b}}\) and B\({}_{\text{a,b}}\) consist of dummy values set to 0.5 in their arguments, with K \(\leq\) 0.5 for A and K \(>\) 0.5 for vector B. This simple operation turns the K\({}^{\prime}_{(1,\text{b})}\) to K\({}^{\prime}_{(2,\text{b})}\) that must be related to any specific class. Having said that, increasing the number of thresholds for reconstruction leads to a more unique combination of features to the label, for example K\({}^{\prime}_{(5,\text{b})}\). However, this setup is kept minimized to examine the approach feasibility for primary demonstration, and discussion is given in the further subsection. Regardless, Figure 9 indicates the projection of vector K into two planes based on pixel magnitude regarding Figures 7 and 8, where features can be better visually monitored.  
Indeed, this operation acts as a filter that sieves the signal into two sets of features based on their low and high-frequency characteristics. What sets this mechanism apart from conventional low and high-pass filters is its ability to function simultaneously as both. As discussed extensively in [29], the main issue within the features for classification is the level of entropy. The proposed projection helps to sieve the data not only into two spectral bands but also into two entropy levels, where the left-hand-side picture in Figure 9 indicates the projection with high entropy and vice versa for the right image. To examine this, the best is to perform statistical tests on the data, where the higher level of variance in the features amounts to higher entropy, eventually enhancing the chance of classification, which has been addressed in Section 4.1.  
Figure 9: 3D view of feature spread projection (**a**), projection to black plane (**b**), and white plane (**c**).
## 3 Results
### Carbon Emission Calculation of the Case Study
#### 3.2.2 Carbon Reduction of Building Refurbishment Compared to New Construction  
Compared with the new construction, the whole life cycle carbon emissions of the refurbished building are reduced by 213.56 tCO\({}_{2}\)e, accounting for 11.07% of the total carbon emissions of new buildings, and the carbon emission reduction of the production phase of building materials accounted for 77.97% of the total reduction. The carbon emission of major building materials is analyzed separately for comparison, as shown in Figure 9. The carbon emission reduction of major building materials, such as steel, concrete, cement mortar, clay, and insulation materials, reached 118.93 tCO\({}_{2}\)e, accounting for 71.42% of the overall carbon emission reduction of building materials. In the process of building refurbishment, the original foundation of the refurbished building offsets the carbon emissions of building materials that may be generated by the construction of new buildings, avoiding the repeated emissions caused by reconstruction after demolition.
# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 2 Materials and Methods  
### Datasets  
In order to evaluate our proposed method, we evaluated our method with four datasets: the Cracktree200 [11], CFD [12], Crack500 [13], and OAD_CRACK datasets. Detailed descriptions of these three datasets are shown as follows:  
The Cracktree200 dataset--The Cracktree200 dataset was mainly collected on road pavements. This dataset was a visible-light dataset containing various kinds of cracks in complex interference environments such as shadows, occlusion, low contrast, noise, and other interferences. It contained 206 crack images, sized 800 \(\times\) 600. For the Cracktree200 dataset, 80% of the images were used for training, and 20% of the images were used for testing.  
The CFD dataset--The CFD dataset consisted of 118 images, sized 480 \(\times\) 320 pixels. Each image had manually labeled crack contours. The device used to acquire the images was an iPhone 5 with a focus of 4 mm, an aperture of f/2.4, and an exposure time of 1/135 s. For the CFD dataset, 80% of the images were used for training and 20% of the images were used for testing.  
The Crack500 dataset--The Crack500 dataset was a pavement crack dataset, including 3368 images captured using a cell phone on the main roads of Temple University, USA, sized either 1440 \(\times\) 2560 or 2560 \(\times\) 1440 pixels. Additionally, this dataset was divided into training datasets and testing datasets by the author. It had 1896 images in the training dataset and 1124 images in the testing dataset.  
The OAD_CRACK dataset--As there were few crack images in the public datasets above, we collected a dataset by ourselves, which we named the OAD_CRACK dataset. This dataset had 5000 images, each with a 1920 \(\times\) 1080-pixel resolution. All images were taken by us with a Huawei P30. Some sample images of the OAD_CRACK dataset are shown in Figure 1:  
In this dataset, crack images were taken in Shenzhen, which were divided into four classes: linear crack, circular crack, void, and background. All images were manually annotated by the EISeg (an image segmentation labeling tool provided by Baidu) and labeled as 0, 1, 2, and 3, respectively. For the purpose of preventing the over-fitting problem during training process, these images were transformed with light and shadow to create more varied training data. Through the transformation above, the whole dataset was extended into 30,000 images. Finally, we divided the whole dataset into two parts; 70% of the images were used for training and 30% of the images were used for testing.  
Figure 1: Some sample images of the OAD_CRACK datasets, (**a**) is the samples of linear crack, (**b**) is the samples of circular crack, (**c**) is the samples of void crack.
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 2 Materials and Methods
### ZTD Estimation from GNSS
Several approaches have been carried out: the investigation of the vertical column of the atmosphere over a single station [41], the exploitation of existing national GNSS networks [42; 43; 44; 45], and the implementation of specifically designed GNSS networks [46; 47; 48; 49]. One element of innovation in this field was introduced by [8], who proposed to use existing regional, national, and international GNSS CORSs for the retrieval of ZTD, to be used in meteorological and climatological applications and studies.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 4 Graphene-Based Composites
### Graphene@Iron Composites
#### 4.1.2 Interpretation of Graphene@Iron Composites Articles
[95] utilized a vacuum-assisted filtration method to form a shielding composite of RGO/CNF@Ag-Fe\({}_{3}\)O\({}_{4}\), where shielding effectiveness of 21 dB was achieved in X-band frequency range with 0.11 mm sample thickness. Table 1 shows a summary of graphene and iron-based composites. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Thickness**} & \multirow{2}{*}{**Loading**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Shielding**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Reference**} \\ \hline
1 & ZnFe\({}_{2}\)O\({}_{2}\)@graphene/TiO\({}_{2}\) & 2.5 mm & - & Hydrothermal method & 3.8 GHz & \(-\)55 dB & 2017 & [81] \\ \hline \multirow{2}{*}{2} & Cellulose/reduced graphene & 0.5 mm & 3 wt.\% & \multirow{2}{*}{Scalable method} & \multirow{2}{*}{8-12 GHz} & 49.4 dB & \multirow{2}{*}{2020} & [83] \\  & oxide (RGO)/Fe\({}_{2}\)O\({}_{4}\) aerogels & 2 mm & 8 wt.\% & & & & & \\ \hline
3 & NiFe\({}_{2}\)O\({}_{4}\)/RGO & 2 mm & - & Solovthermal method & 10.8 GHz & 38.2 dB & 2020 & [84] \\ \hline
4 & MoS\({}_{2}\)-RGO/CoFe\({}_{2}\)O\({}_{4}\) & 1.4 mm & - & Hydrothermal method & 8-12 GHz & 19.26 dB & 2019 & [85] \\ \hline
5 & Fe\({}_{2}\)O\({}_{4}\)@CNP hybrids & 1 mm & - & 1. Co-precipitation technique & 8-12 GHz & 9.6 dB & 2020 & [86] \\  & & & 2. Solvent-less approach & & & & \\ \hline
6 & Fe\({}_{2}\)O\({}_{4}\) C-MIL-88/GNP & 0.11 mm & - & \multicolumn{2}{c}{Filtration-assisted} & 8-12 GHz & 28 dB & 2019 & [87] \\  & & & & self-assembly method & & & & \\ \hline
7 & Fe\({}_{2}\)O\({}_{4}\)@f-CNPs & 2 mm & - & Solovthermal method & 12 GHz & 25 dB & 2020 & [64] \\ \hline
8 & FC-Fe\({}_{2}\)O\({}_{4}\) & 6.1 mm & - & In-situ growth & 5.4 GHz & \(-\)53 dB & 2017 & [88] \\ \hline
9 & CO@CIP & 1.9 mm & - & Wet stirring process & 0-18 GHz & \(-\)56.4 dB & 2019 & [89] \\ \hline
10 & Ag@Fe\({}_{2}\)O\({}_{4}\)@RGO & 2 mm & - & Solovthermal method & 2-18 GHz & \(-\)40.05 dB & 2015 & [90] \\ \hline
11 & Fe\({}_{2}\)O\({}_{4}\)@RGO & 3.57 mm & - & Solovthermal method & 2-18 GHz & \(-\)99.23 dB & 2020 & [91] \\ \hline
12 & NRMG & 1.6 mm & - & Self-assembly method & 8-12 GHz & 26.4 dB & 2018 & [92] \\ \hline
13 & Fe\({}_{2}\)O\({}_{4}\)@Cosmopherne & 1.5 mm & - & Hydrothermal method & 2-18 GHz & \(-\)50.2 dB & 2018 & [92] \\ \hline
14 & C-F & 19 mm & - & Hydrothermal method & 2-18 GHz & 20 dB & 2016 & [93] \\ \hline
15 & Ni\({}_{2}\)Co\({}_{3}\)Fe\({}_{2}\)O\({}_{4}\)/graphene & 4 mm & - & Hydrothermal method & 0.58–119 GHz & \(-\)30.92 dB & 2018 & [94] \\ \hline
16 & RGO/CNF@Ag-Fe\({}_{2}\)O\({}_{4}\) & 0.11 mm & - & Vacuum-assisted filtration method & 8-12 GHz & 21 dB & 2020 & [95] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Graphene@fron-based composites.
## 3 Proposed Watermarking Framework
### Structure of Watermarking Network to Be Trained
#### 3.2.6 Loss Function of the Network for Training  
With the two-loss terms of Equations (1) and (2) for host information and WM information, respectively, the loss function of the whole network for training is constructed as Equations (3) and (4) for WM embedding and WM extraction, respectively.  
\[L_{emb}=\lambda_{1}L_{1}+\lambda_{2}L_{2} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack Type** & **Attack** & **Strength** & **Ratio** \\ \hline No attack & Identity & - & 1/12 \\ \hline \multirow{6}{*}{Pixel-value change attack} & Gaussian filtering & 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, 9 \(\times\) 9 & 2/12 \\  & Average filtering & 3 \(\times\) 3, 5 \(\times\) 5 & 2/12 \\  & Median filtering & 3 \(\times\) 3, 5 \(\times\) 5 & 1/12 \\  & Salt \& Pepper & \(p\) = 0.1 & 0.5/12 \\  & Gaussian noise & Sigma = 0.1 & 0.5/12 \\  & Sharpening & 5-point stencil, & 1/12 \\  & JPEG & Quality factor = 50 & 1/12 \\ \hline \multirow{2}{*}{Geometric attack} & Rotation & 0\(\sim\)90\({}^{\circ}\) (random) & 1/12 \\  & Crop & 0.5\(\sim\)0.8 (random) & 1/12 \\  & Dropout & 0.3\(\sim\)0.9 (random) & 1/12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attacks for attack simulation used in training.  
\[L_{ext}=\lambda_{3}L_{2} \tag{4}\]  
In these two equations, \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are set to the hyper-parameters that control invisibility and robustness. \(\lambda_{1}\) represents the strength of the \(L_{1}\) loss applied to the embedding network, \(\lambda_{2}\) represents the strength of the \(L_{2}\) loss applied to the embedding network, and \(\lambda_{3}\) is the strength of the \(L_{2}\) loss applied to the extraction network. Because the \(L_{1}\) loss and the \(L_{2}\) loss have different properties, it is not easy to analytically determine the three hyper-parameters. Therefore, they are obtained empirically.
## 3 Magnetic Characterization Instruments
### Superconducting Quantum Interference Device (SQUID)
The asymmetric topology of nanoSQUID provides \(10^{-4}\)\(\Phi_{0}\) magnetic flux sensitivity, and the setup is proven appropriate for the characterization of nanoparticle clusters. In [102], a SQUID magnetometer is used for the detection of the minute magnetic field perturbations related to biocompatible nanoparticles when a novel sensing method is applied. In [103], a high-temperature SQUID magnetometer is presented, constructed by a YBCO thin film of 30 nm thickness, while the researchers' ultimate goal is to create a helium ion beam fabricated nano-slit SQUID. In [106], a new bulk SQUID magnetometer, which contains two holes, is presented. The authors tried to optimize the connection of the RF-coil and the hole of the SQUID. In [111; 112; 113; 114; 115], several SQUID topologies are proposed to face the flux vortex motion phenomenon. In [111], a SQUID loop with narrow linewidths is proposed, while in [112] the flux dams are discussed. In [113; 114], SQUID loops containing perforations are investigated, while the study in [115] slotted pick-up loops. In [116], an axial wire-wound SQUID and a planar thin-film SQUID magnetometer, as Figure 3 depicts, are compared for super-paramagnetic relaxometry measurements. The comparison revealed that a thin-film planar device is easier to manufacture in comparison to an axial wire-round SQUID as well as allows integrated chip design. However, in the thin-film planar topology, the flux can be trapped, and this phenomenon can prevent their appropriate function in the case that an external magnetic field is present. Figure 3: (**a**) The axial wire-wound measurement system, (**b**) the planar thin-film measurement system [116]. In [117], magnetic characterization of glycerin and serum solvents was done using high-temperature SQUID magnetometers. The analysis is based on both AC and DC magnetometers: a hybrid magnetometer [58], with a vibrating and rotating sample, depicted in Figure 4, and an AC magnetometer for immunoassay. In [118], a novel magnetic nanoparticle imaging (MPI) method is investigated. This method is the result of the combination of the MPI system, a high-temperature SQUID, and a laser scanning system. This new MPI method presents higher resolution compared to the conventional MPI methods. In [119], the activity of the small animal brain is studied, investigating the blood flow with magnetic nanoparticles, using three, connected in one chip, high-temperature SQUIDs. Finally, in [120] a novel YBCO micro-SQUID magnetometer, which uses a helium ion microscope, is proposed. Using this setup, the critical temperature decreases when the ion irradiation dose increases.
## 5 Theoretical and Managerial Implications  
From a theoretical perspective, the current research provides new contributions to the literature by focusing on a firm's transparency function, including operational and sustainability performance. For example, this study recognizes antecedents of OPT based on the RBV logic of inter-organizational links in terms of sharing information. RBV logic focuses on the resources and capabilities of a firm. For theoretical contributions, firstly, the mediating impact of OPT was analyzed among the EBPr and SUP of a firm. Secondly, the mediating influence of OPT among EBPr and EnS was examined. Third, the direct theoretical impact of EBPr on OPT was examined. Fourth, this research provides a clear picture and understanding of transparency. Lastly, the current work also provides a theoretical implication after testing a theoretical model in the context of a company. The manufacturing industry is knowledge-intensive and heavily based on novel information about updated products. Thus, understanding information sharing for operational and sustainable issues will provide new information on how focal firms can adopt environmental business practices that would create the capability to enhance firm performance. This study will also increase the importance of updated technology for firms to resolve issues, which will help adopt best practices in operations.  
For supply chain managers and practitioners, this work offers some implications. The results confirmed that OPT is beneficial for EBPr to improve the sustainability and operational issues of a firm. Thus, firstly, companies should use OPT as a strategic resource in a positive way to maximize a firm's performance. Companies can apply OPT from a trustable, formal, safe joint structure to resolve conflicts. Secondly, the current work explored the impact of environmental business practices, which projected that corporate administrations outline their ideas for OPT resources. With the help of a partner's cooperative orientation, management implication stems from a study assessing environmental business practices' effect on a firm's performance outcomes. Lastly, problems related to sustainability and operations can be resolved by exchanging knowledge about business practices. Managers should absorb the OPT resource that can advance the impact on a firm's overall performance, which may generate hurdles for managers.