esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Sensitivity Analysis of the Neighborhood Parameter  
In this study, the shadow detection result is initially acquired through binarizing the shadow index image with a certain optimal threshold by the NVEM thresholding algorithm, as presented in Step 4 of the workflow. However, according to the thresholding solution of Equations (26)-(30), the optimal threshold is sensitive to the neighborhood parameter \(m\). As noted in related studies, uncertainties appear in the binarization of natural images while determining the optimal threshold with different neighborhood parameter \(m\) values [27]. Hence, in order to further explore the impact of the neighborhood parameter \(m\) on the shadow detection performance of HSR multispectral satellite remote sensing images, we respectively run additional experiments in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ) with the neighborhood parameter \(m\) set from 1 to 40 with an  
Figure 8: Overall accuracies of shadow detection results with the initial shadow index and the logarithmic shadow index for test images. (**a**) Tripoli-1. (**b**) Tripoli-2.  
interval of 1 for test images Tripoli-1 and Tripoli-2. Figure 9 depicts the sensitivity of the \(LSI\) algorithm performance to the neighborhood parameter \(m\) of the NVEM thresholding method for test images Tripoli-1 and Tripoli-2, respectively.  
As illustrated in Figure 9,b, the overall accuracies keep relatively high values and a stable trend in various invariant color spaces with the neighborhood parameter \(m\) from 1 to 28 for Tripoli-1 and with the neighborhood parameter \(m\) from 1 to 20 for Tripoli-2, which together states that excellent performance and robustness are acquired with a not-very-big neighborhood parameter \(m\) for test images in these invariant color spaces. The difference between Figure 9,b also explains that the neighborhood parameter \(m\) depends on the target image. Accordingly, we process Tripoli-1 with an optimal neighborhood parameter \(m=25\), and Tripoli-2 with \(m=2\), respectively.
###### Abstract
Quite often, such heating is performed repeatedly. Moreover, when welding, a change in the mating metals' microstructure near the weld occurs [12; 13]. All the above-mentioned factors can increase the metal corrosion. Currently, effective methods of quality control of welding joints [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] as well as the remote monitoring of the operational characteristics of structures have already been developed. Recently, intensive research has also been conducted aimed at the early detection of corrosion [18; 19]. However, when assembling elements from reinforced concrete, it is practically impossible to monitor the corrosion near the weld joint after sealing the joint between the concrete goods by cement grout [9]. It is known that concrete pore liquid has a pH of approx. 12, which stipulates reinforcing steel passivation and prevents its corrosion in the bulk of concrete. During the operation, moisture and CO\({}_{2}\) diffuse inside concrete, with strongly alkaline solutions of concrete pore liquid (with pH \(\approx\) 11.3-12.5) reacting with CO\({}_{2}\) with the calcium carbonate generation [9]:  
\[\text{Ca(OH)}_{2}+\text{CO}_{2}=\text{CaCO}_{3}+\text{H}_{2}\text{O} \tag{1}\]  
The further development of the process results in hydrocarbonate formation, shifting the pH value towards neutral solutions, reaching a pH of 9, which results in the depassivation of the protective oxide coating on the metal, creating favorable conditions for corrosion. It is known that iron corrosion is the most intense, in the pH range from 4 to 10 [27; 28]  
\[\text{CaCO}_{3}+\text{CO}_{2}+\text{H}_{2}\text{O}=\text{Ca(HCO}_{3})_{2} \tag{2}\]  
Reinforcement corrosion results in the formation of a loose mass of hydrated ferric oxides, the volume of which exceeds the volume of the initial metal-reinforcing element. This, in turn, results in the mechanical failure of concrete [29]. A large body of research is devoted to concrete corrosion [30] (a review is given in paper [27]) and corrosion in saturated lime solutions [31]; however, it does not concern the peculiarities of the corrosion of weld joints and joining the reinforcement when assembling concrete goods. The aim of this paper is to study the metal corrosion near the weld joint in the standard test medium with a pH close to the pH of a solution in the concrete pores.
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 6 Conclusions  
H\({}_{2}\) production is a promising way to generate clean and green energy. Biological methods such as dark fermentation have enabled the production of renewable and carbon-neutral H\({}_{2}\). Dark fermentation studies using anaerobic and facultative anaerobic bacteria have been widely identified, as these bacteria can utilize simple sugars and even food and agricultural wastes. Optimization of parameters such as pH, temperature, type of substrate used and its concentration, type of bioreactor, HRT, type of inoculum, and its metabolic pathway regulation can significantly improve H\({}_{2}\) production. In addition, pretreatment, purification, and storage methods can be employed to make H\({}_{2}\) more efficient for potential applications in industry and transportation. However, the method still requires certain technological advances to be adequately scaled up for industrial applications.  
Bacteria play a critical role in the efficiency and feasibility of H\({}_{2}\) production. _Clostridium_ spp. has been widely preferred in studies. These bacteria possess highly active hydrogenase enzymes, which are crucial for the efficient conversion of reduced ferredoxin to H\({}_{2}\). This review provides a detailed analysis of the parameters and bacteria strains used in dark fermentation studies. Each strain is compared to identify the most suitable bacteria for specific environmental and process conditions, as preferred in several studies over the years and those available during individual research. This will allow researchers to obtain more reliable and easier data in the future by monitoring available results that still need to be optimized. In addition, it will provide a cost-effective waste treatment process that addresses energy and waste disposal issues and serves as a cleaner alternative to fossil fuels.  
Conceptualization, S.G., A.F., L.G. and J.S.; methodology, S.G. and L.G.; software, S.G. and L.G.; validation, A.F., A.L. and L.G.; formal analysis, L.G. and J.S.; investigation, S.G. and L.G.; resources, L.G. and J.S.; data curation, A.F., A.L. and L.G.; writing--original draft preparation, S.G., A.F., L.G. and J.S.; writing--review and editing, S.G., A.F., A.L., L.G. and J.S.; visualization, L.G. and J.S.; project administration, L.G. and J.S.; funding acquisition, A.F., L.G. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundacao para a Ciencia e a Tecnologia, FCT, project UIDB/00195/2020, PhD grant PRT/BD/154415/2023 awarded to [PERSON], and research contract CEECINST/00016/2021/CP2828/CT0006 awarded to [PERSON] under the scope of the CEEC Institutional 2021. This work was carried out as part of the project PR-H2 CVAL4-C1-2022-0049 \"Valorizacion de aguas residuales industriales para la generacion de hidrogeno biologico (H2 biO)\" from IDAE, financed by the EU Next Generation Funds.  
Not applicable.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Membrane Preparation and Testing  
The inclusion polymer membranes were prepared by the solvent evaporation technique. An amount of the polymer (0.4 g) was dissolved in the THF. After complete dissolution, a homogeneous solution was obtained, and Aliquat-336 was added at different ratios (1/0.5, 1/0.75, and 1/1). Homogeneous solutions were kept under low agitation for 1 h to eliminate air bubbles (degassing time). Finally, the polymeric solutions were poured into a 9 cm diameter Petri dish for solvent evaporation for 24 h. In order to get a smooth surface and dense membrane without any defects, the solvent was evaporated slowly by covering the Petri dish with a lid. The produced membranes were washed with distilled water for further characterization. For this purpose, several techniques were used, such as scanning electron microscopy (SEM) (Zeiss- EVO-Ma10, Oerzen, Germany), infrared spectroscopy (Nicolet iN10 710, Waltham, USA), and contact angle (CAM 100, Nordtest Srl, Serravalle Sciruia, Italy). In addition to the above characterization techniques, to better understand the membrane properties, it was important to carry out both thermal and mechanical tests. However, in this preliminary investigation, no thermal analysis was made, considering that the produced membranes were used up to 50 \({}^{\circ}\)C, in order to avoid any evaporation of aqueous solutions and concentrations of species, as well as loss of Aliquat-336. Also, no mechanical tests were carried out due to the small size of the membrane samples sandwiched between frames and to the low (atmospheric) operating pressures. The produced membranes were sandwiched between each two frames making a total of seven (07) membranes for eight (08) frames. After module preparation, the feed and receiving phases were fed at 0.3 mL/s and the desired temperature in a counter-current mode by a peristaltic pump, filling the chambers inside the frames. Samples were taken from the two phases to analyze the Cr(VI) content.
# Plate Size Effects in Gravelly Soil Based on In Situ Plate Load Tests and Finite Element Analysis
## 3 In Situ Plate Load Test  
### Experimental Setup  
Plate load tests (PLT) were conducted at distances of 15.8 m, 19 m, and 23 m from the tunnel entrance. The procedures for the PLT in Chinese code DL/T 5356-2006 are comparable to those specified in DD ENV 1997-3:2000 [8]. The setup of the horizontal PLT is illustrated in Figure 3. To minimize the impact of gravel particle size, a circular rigid plate with a diameter of 800 mm was selected as the test plate. A square rigid plate with a side length of 1.5 m was used on the reaction side to ensure that the stress on this side remained significantly lower than on the test side. The test point surface was leveled within a 1.6 m radius, and the gap between the rigid plate and the tunnel wall was filled with cement mortar. According to Chinese code DL/T 5356-2006, the maximum applied load should be no less than 1.2 times the design bearing capacity of 0.6 MPa, while the actual load reached 2 MPa.
# Augmented Reality in a Lean Workplace at Smart Factories: A Case Study
## 3 Research Methodology  
### Research Questions and Research Methodology Framework  
In order to understand the relationship between AR technology, Lean, and HF, the main research question of the project presented in this paper (RQ1) has been raised and addressed. This general question forms the basis of the project and was further divided into two sub-questions (RQ 1.1 and RQ 1.2), which were the main focus of this research. These questions, depicted in Table 1, seek to understand the ways in which this technology can be used to improve processes and create synergies between industry 5.0 and Lean.  
\begin{table}
\begin{tabular}{c c} \hline
**RQ\#** & **Research Question** \\ \hline RQ 1 & How can AR enhance human capabilities and senses in lean workplaces? \\ RQ 1.1 & How can AR enhance human capabilities and senses in order to mitigate risks? \\ RQ 1.2 & How can AR enhance human capabilities and senses in order to improve ergonomic conditions? \\ RQ 2 & Which AR solutions are more suitable for logistic processes? \\ \hline \end{tabular}
\end{table}
Table 1: Research questions.  
Figure 2: Integration of three lean domains [59].  
Following the definition of capabilities and senses in order to augment, the second research question (RQ 2) seeks to identify the most suitable AR solutions that best suit logistic processes. This question also aims to define the use cases of each technology to augment the capabilities and senses identified in the previous questions.  
In order to achieve the above-mentioned research questions, a research methodology framework was designed that consisted of the accomplishment of four main phases, namely:  
1. Literature review;
2. Case study analysis;
3. Methodology definition;
4. Analysis and discussion.
# An Assessment of the Impact of Gypsum Deposit Development on Changes in the Radiation Environment
## 3 Results and Discussions
### Soils, Quaternary Deposits and Gypsum
#### 3.3.2 Ambient Dose Equivalent Rate (ADER) of Gamma Radiation  
The range of variation in the ambient dose equivalent rate (ADER) of gamma radiation in the quarry and adjacent areas was from 11 to 69 nSv h\({}^{-1}\), with an average value of 38 nSv h\({}^{-1}\). Based on the obtained data (Table S5), the areal distribution of the ADER was constructed, as shown in Figure 6a.  
The results of the study showed that the lowest ADER values are observed in the areas of direct exposure of the gypsum formation surface, as well as in the areas of the sorting and storage of mined gypsum rock (see the white color in Figure 2b). This is due to the low radioactivity of gypsum [65,66] (Table 5). Higher values are confined to the locations of overburden dumps, which are represented by a mixture of clayey sediments with gypsum fragments (see Figure 1). Sandy-clayey sediments contain significant amounts  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**ID** & **\({}^{137}\)Cs** & **\({}^{228}\)Ra** & **\({}^{232}\)Th** & **\({}^{40}\)K** & **\({}^{238}\)U** & **\({}^{234}\)U** & **\({}^{234}\)U/\({}^{238}\)U** \\  & & &  
of radionuclides [66; 67] (Table 5); therefore, the ADER intensity is directly related to the relative content of sand and clay in this mixture. That is, the redistribution and artificial concentration of natural radionuclides in overburden dumps is a direct consequence of mining operations.  
In accordance with [68], the effective dose of radiation from natural radiation sources for all workers, including personnel, should not exceed 5 mSv yr\({}^{-1}\) under production conditions (any professions and industries). The maximum ADER values obtained in this study are significantly lower than the established level of effective dose for quarry workers. When converted to an 8 h working day with a total number of working days per year of approximately 250, they amount to 0.14 mSv yr\({}^{-1}\). At the same time, it should be noted that the Japanese government established a maximum permissible level (MPL) for the public of 1 mSv yr\({}^{-1}\) as an acceptable level of airborne exposure, which is calculated as 0.23 \(\mu\)Sv h\({}^{-1}\) (0.19 \(\mu\)Sv h\({}^{-1}\) from the Fukushima Dai-ichi Nuclear Power Plant accident and an average of 0.04 \(\mu\)Sv h\({}^{-1}\) background radiation in Japan) [69; 70].  
In general, it is worth noting that the obtained ADER values do not exceed the background values typical for the region. They also comply with the requirements of SP 2.6.1.2612-10 [71], according to which when selecting areas for the construction of residential and public buildings, areas with an equivalent dose rate of gamma radiation of less than 0.3 \(\mu\)Sv h\({}^{-1}\) should be selected.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Gray-Level Co-Occurrence Matrix (GLCM) Analysis  
The GLCM is a statistical test used in image encryption to analyze the texture of an image by considering the spatial relationship of pixels. It consists of a two-dimensional matrix of joint probabilities between pairs of pixels with specific values. By calculating the frequency of a pixel with gray-level value \(i\) occurring horizontally adjacent to a pixel with gray-level value \(j\), the GLCM is useful for feature extraction and texture analysis. From the GLCM, statistical measures (namely homogeneity, contrast, and energy) can be extracted to provide information about the image's texture features. In the following, we further discuss each of these measurements.
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
## 3 Results  
### Resolution  
To measure the ability of the proposed beamformer to resolve a point target, a phantom made of nylon threads of 0.2-mm diameter was immersed in water. The distance between the target to the transducer surface is 15.1 mm. Four beamformers are used and the results are compared. Figure 4 presents the beamformed images with a 30-dB dynamic range. It can be seen that NDSB produces a slightly larger point spread function (PSF) compared with DAS. It can be seen in Figure 4c,d that the side lobes for proposed beamformers are slightly asymmetric. The reason is in Section 2.2 we assume the pressure field contributions from the two aperture ends are equal. But for real discrete transmit sub-aperture, these contributions might not be perfectly the same. Weighting strategy can be used for each contribution, but the design of weighting parameters is beyond the scope of current work. The proposed compound method in Figure 4c,d improves the resolution of NDSB. Figure 5 plots the lateral and axial profile of the beamformed point target. The detailed resolution in the lateral and axial direction is in Table 1. The proposed Comp1 method provides the best resolution in both directions, which is 1.37 mm in the lateral direction and 0.43 mm in the axial direction. The proposed two compounding methods (Comp1, Comp2) outperform DAS and NDSB in terms of resolution.
## Materials and Methods
### Image Segmentation (by Thresholding as Comparative Method)  
Two segmentation algorithms, aiming to separate different image features based on different properties, were used: the Otsu histogram-based threshold technique [28] and the deep learning semantic segmentation method.  
The former is based on the choice of a segmentation threshold applied to the grey level histogram that separates pixels into two groups that have more similar grey levels (proportional to the physical densities--mg/cm\({}^{3}\)), usually foreground and background [29]. Otsu calculates the maximum variance between them and sets the threshold on this value [30]. In this study, the Otsu algorithm was applied to each volume selected (consisting of 300 slices), and it chose a single threshold for each of them.  
Conversely, the deep learning semantic segmentation method assigns a label to each pixel, not only relying on grey level distribution but also on the image morphometric characteristics. Thus, if objects inside the image have a different shape or direction, they will be classified as two distinct subgroups, and, therefore, associated with a different color. The use of the semantic segmentation method involves neural networks; we chose to use U-Net. It was developed by [PERSON] et al. [31] and was designed for biomedical image segmentation. The architecture is divided into two parts; the first is called encoder, which is the contracting path to capture context in the image and find key elements, while the second is the symmetric expanding path called decoder, which employs the learned pooling layers to enable precise localization of features.
## 1 Introduction
[12] presented a new control method for multi-input, multi-output, stationary, non-Gaussian random vibration tests using time domain randomization, and analyzed the generation process of stationary and coupled reference non-Gaussian signals by specified reference skewness, kurtoses and spectra. [PERSON] et al. [13] studied the generation and closed-loop control of multi-shaker, non-stationary, non-Gaussian random vibration signals with the modified time domain randomization technique and theoretically derived the kurtosis relationship between the one frame pseudo-random signal and consecutive true random signal. Domestic and foreign scholars mostly consider the vibration response of liquid-filled pipelines only under the action of an external random vibration load, and few studies have been conducted on the fluid-structure interaction vibration response characteristics of liquid-filled pipelines under the action of existing external random vibration load and internal fluid pulsating pressure. The pipeline of a hydraulic system is constantly subjected to two kinds of loads: complex external random vibration and internal fluid pulsation pressure. The problem of the complex fluid-structure interaction vibration response needs to be studied and solved, especially when the external load belongs to non-Gaussian vibration, as the problem cannot be solved by traditional Gaussian vibration analysis methods (such as the power spectral density method). Different from previous studies, this paper studied the fluid-structure interaction vibration response of an aircraft liquid-filled pipeline under an external random vibration load and internal pulsating pressure, and explored the influence of external random vibration with different kurtoses, different pipe wall thicknesses and different working conditions on the vibration response danger points of aircraft liquid-filled pipelines. We believe that the research result has great practical significance for optimizing the design of aircraft oil pipelines.
## 4 Discussion
However, the operation of the HE can be affected by other exchangers serving the same process streams (antecedent exchangers); examples of such exchangers in the HENs can be found in Figures 2 and 3. As fouling builds up on the heat transfer surfaces of the antecedent exchangers, temperatures of process streams at HE inlet are increased. Due to that, although heat transfer intensity has been reduced by fouling, the thermal power of the HE may remain unchanged. Similarly, the indices of control quality in the associated control loop may also remain unchanged (Figure 16a,b). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**The Number of**} & \multicolumn{2}{c}{**The Number of**} & \multicolumn{2}{c}{**The Level of**} \\
**Case Study** & \begin{tabular}{c} **of HEs in a** \\ **Control Loop** \\ \end{tabular} & \
\begin{tabular}{c} **An antecedent HEs** \\ **on Hot** \\ **Stream** \\ \end{tabular} &
\begin{tabular}{c} **Fouling Influence on the** \\ **Control-quality Indices.** \\ \end{tabular} \\ \hline
**Case no. 1** & & & & \\ (**Figure 2**) & & & (see Table 5) \\ for control loop 1 & 13 & 9 & 11 & negligibly low & (Figure 16a) \\ for control loop 2 & 13 & 2 & 24 & negligibly low & (Figure 16b) \\ \hline
**Case no. 2** & & & \\ (**Figure 3**) & & & & (see Table 6) \\ for control loop 3 & 1 & 7 & 7 & low & (Figure 17a) \\ \hline
**Cases in work [6]** & & & & \\ (**Figure 18**) & & & & (see Table A1) \\ for HE11 control loop & 1 & 0 & 0 & high & (Figure A1a) \\ for HE15 control loop & 1 & 2 & 0 & medium & (Figure A2a) \\ for HE30 control loop & 1 & 2 & 2 & medium & (Figure A3a) \\ for HE35 control loop & 1 & 0 & 0 & high & (Figure A4a) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The number of HEs in a control loop and the number of antecedent HEs compared to the level of fouling influence on the control-quality indices.