esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Impact of Different Influencing Factors on Carbon Emissions in Wastewater Treatment  
Comparing the three carbon accounting methods, T/CUWA 50055-2023 has a more comprehensive calculation scope, considers more factors, and has a richer variety of chemical carbon emission factors, making it more suitable for the carbon emission accounting of Chinese WWTPs. The subsequent analysis adopts the calculation results of T/CUWA 50055-2023.  
Figure 4: Electricity consumption of different treatment units in WWTPs.  
According to the calculation of T/CUWA 50055-2023, the total carbon emissions of 49 WWTPs in Shandong Province in 2022 were 280,308.040 tCO\({}_{2}\)-eq/a, of which the direct emissions were 121,073.7 tCO\({}_{2}\)-eq/a, accounting for 43.2%. The indirect emissions were 159,234.4 tCO\({}_{2}\)-eq/a, accounting for 56.8%, with an emission intensity of 0.452 kgCO\({}_{2}\)-eq/m\({}^{3}\). This intensity is higher than that of Shenzhen [36] (0.30 kgCO\({}_{2}\)-eq/m\({}^{3}\)) and Shanghai [21] (0.29 kgCO\({}_{2}\)-eq/m\({}^{3}\)), indicating that there is still room for optimization in terms of process level, energy consumption, and management of WWTPs in Shandong Province. The differences in the accounting results are also related to some factors, including the level of regional economic development, the scale of WWTPs, and the quality of influent water [37].  
Due to the differences in production conditions, technical levels, and process flows of WWTPs, the selection of emission factors may lead to uncertainties in the estimation results. WWTPs can obtain more accurate CH\({}_{4}\) and N\({}_{2}\)O emission factors through actual measurement or model simulation to improve the accuracy of the accounting.
# Smart Firefighters PPE: Impact of Phase Change Materials
## 3 Results  
### Preliminary Tests of PCM Thermal Performance  
In the first stage of this study, the PCM-assembly thermal performance was evaluated by exposing the samples to a heat source followed by a cooling period, as described in Section 2.1. The temperature evolution over time was obtained for four samples: three with different PCMs and another without PCMs (Figure 4).  
Figure 4: Thermal performance evaluation of assembly without and with different PCMs (* temperature measured on the opposite side to heat exposure).  
Figure 3: Simulated environment tests in the container used to simulate urban fire scenarios: temperature monitoring with a thermal camera.  
The figure above shows the positive effect of integrating the PCM into the assembly regarding thermal protection because the temperature increase was delayed compared to the results obtained in the ensemble without the PCM. Furthermore, PCM 1 has better results in terms of thermal protection (higher delay in the heating stage), as expected, since this PCM presents a higher melting point and storage capacity. However, the duration of the cooling phase is increased due to the presence of the PCM material. For instance, at minute 65 (i.e., 5 min of the cooling phase start), a temperature drop of \(\sim\)14 \({}^{\circ}\)C is obtained with the assembly with PCM 1, while a drop of \(\sim\)37 \({}^{\circ}\)C is obtained with the same assembly without the PCM. The same tendency is observed with the remaining PCMs.
# ColorMedGAN: A Semantic Colorization Framework for Medical Images
###### Abstract  
Colorization for medical images helps make medical visualizations more engaging, provides better visualization in 3D reconstruction, acts as an image enhancement technique for tasks such as segmentation, and makes it easier for non-specialists to perceive tissue changes and texture details in medical images in diagnosis and teaching. However, colorization algorithms have been hindered by limited semantic understanding. In addition, current colorization methods still rely on paired data, which is often not available for specific fields such as medical imaging. To address the texture detail of medical images and the scarcity of paired data, we propose a self-supervised colorization framework based on CycleGAN(Cycle-Consistent Generative Adversarial Networks), treating the colorization problem of medical images as a cross-modal domain transfer problem in color space. The proposed framework focuses on global edge features and semantic information by introducing edge-aware detectors, multi-modal discriminators, and a semantic feature fusion module. Experimental results demonstrate that our method can generate high-quality color medical images.  
medical image colorization; CycleGAN; visualization +
Footnote †: journal: Medical Image
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
###### Abstract  
Soil stabilization problems like liquefaction, bearing capacity, permeability, excessive settlement and swelling can be solved by improving soil's engineering properties by using various methods. The use of fly ash as a stabilizer has become popular in recent years since it is eco-friendly and effective on soil stabilization, especially for fine-grained soils. This study investigated the reuse of fly ash as a stabilizer (5-25% by weight) and cement (constant 3% by weight) as an activator to enhance the geotechnical properties of poorly graded sand (SP). Standard proctor tests were conducted to determine optimum water content and maximum dry unit weight, followed by direct shear box, falling head permeability and CBR tests at the determined optimum water content. Direct shear box experiments were carried out at two relative densities (30% and 80%) and CBR experiments were performed after 7 and 28 days of curing time. The results demonstrated that the addition of fly ash and cement improved the geotechnical properties, including shear strength, permeability and bearing capacity of the fine sand soil.  
fine sand; fly ash; cement; soil improvement; soil stabilization Article
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 3 Proposed Method  
Our proposed method aims to address the issues that arise during the enhancement of low-light images through the following approaches:  
* Estimating illumination components in low-light images using histogram smoothing and Retinex theory.
* Color correction by selecting a correction channel among the RGB channels in an image with enhanced illumination.
* Removal of noise from the resulting image with a non-local mean based on a local steering kernel.  
The proposed method is specifically categorized into three parts based on the methods presented above. First, there is the illumination improvement part, which improves the illumination of low-light images. Secondly, there is the color correction part, which corrects distorted colors during the illumination improvement process. Finally, there is the noise removal part, which removes the noise amplified during the illumination improvement and color correction. Figure 1 shows a block diagram of the processing of the proposed algorithm.
## 3 Mechanical Properties
observed a reduction in hardness of LPBF AISI 316L as a consequence of an annealing heat treatment. The microstructural analyses did not reveal any change in the microstructure but only a substantial reduction of the dislocation density at the cell boundaries and surrounding the non-metallic inclusions. The dislocation networks were then considered responsible for the high mechanical properties of as-built AM parts [85]. Several authors also highlighted that the plastic region of DED AISI 316L samples is relatively flat, indicating that these samples have a low strain hardening ability with respect to conventionally processed samples [84, 86, 39]. The strain hardening ability (\(H_{\text{C}}\)) is generally calculated as [86]:  
\[H_{c}=\frac{(US-YS)}{YS} \tag{2}\]  
By comparing the tensile data of DED AISI 316L steel samples reported in Table 4, it can be noted that there is a wide variation in their mechanical properties. Vickers hardness values, for example, vary between 190 and 370 HV, while the US values fall in the range 536-900 MPa. Nonetheless, the strongest variations can be found in the YS and the \(\varepsilon\) values, which vary from 300 to 610 MPa and \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **P** & **V** & **Direction** & **Gas** & **Hv** & **YS** & **US** & **US** & **\(\varepsilon\) (\%)** & **Hc** & **Ref.** \\  & **(W)** & **(mm/s)** & & & & **(MPa)** & **(MPa)** & **(MPa)** & & **(\%)** & **Hc** & **Ref.** \\ \hline  & 328 & 17 & V & Ar BC & - & - & 550 & - & - & [39] \\  & 360 & 16 & V & Ar BC & 222–260 & 448–455 & 545–634 & 4–25 & 0.22–0.39 & [84] \\ Atmosphere & 400 & 15 & V & Ar SG & - & 352 & 536 & 46 & - & [83] \\  & - & - & H & N2 SG & - & \(469\pm 3\) & \(628\pm 7\) & \(31\pm 2\) & 0.34 & [64] \\  & - & - & H & N2 BC & - & \(530\pm 5\) & \(670\pm 6\) & \(34\pm 1\) & 0.26 & [64] \\ \hline \hline \end{tabular}  
* The other building parameters were selected based on an orthogonal experimental design, the mechanical properties are intended to be the results of the application of the statistical method, ** Fresh powder, ** Re-used powder.  
\end{table} 
Table 4: _Cont._from 4% to 46%, respectively. These discrepancies are generally due to the AM building conditions, powder quality, and tensile sample direction and geometry. Consequently, most of the considered studies related to the mechanical properties of these materials were focused on the understanding of the effect of the building conditions on the YS, US, and \(\varepsilon\) values. The main investigated aspects are described and summarized here.
# Intercalating Sn/Fe Nanoparticles in Compact Carbon Monolith for Enhanced Lithium Ion Storage
## 2 Materials and Methods
### Performance Evaluation  
The working electrode was prepared by mixing the active materials (SFO@C or SFO), conducting carbon and polyvinylidene fluoride binder in N-methyl-2-pyrrolidinone, to form a homogeneous slurry with a weight ratio of 8:1:1. The slurry was doctor bladed on copper foil current collector and then dried in a vacuum oven (Kunshan Rongshida Electronic Equipment Company, Jiangsu, China) for 12 h. The copper foil coated with active materials was then stamped into rounds with a diameter of 14 mm. The areal active material loading in each electrode reaches about 2 mg/cm\({}^{2}\). Electrochemical measurements were performed by using the CR-2032-type coin cells, which were assembled in a glove box (O\({}_{2}\) and H\({}_{2}\)O contents \(<\) 1 ppm) filled with highly pure argon. A pure lithium foil was utilized as the counter electrode, the Celgard 2400 microporous polypropylene membrane (Celgard, 2400, 25\(\upmu\)m in thickness, NC, USA) acted as the separator, and a mixture of 1 M LiPF\({}_{6}\) in ethylene carbonate (EC) and dimethyl carbonate (DMC) (Kelon, Chengdu, China) with a volume ratio of 1:1 was applied as the electrolyte. The galvanostatic charge/discharge measurements were done on a Neware battery measurement system (CT-3008, Neware technology, Shenzhen, China). Both the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) tests were implemented on a CHI 660E electrochemical workstation (CHI 660E, Shanghai, China). The charge or discharge capacity (Q) was computed according to the formula: \(Q=I\Delta t/m\), wherein \(I\) denotes the current density, \(\Delta t\) represents the discharge or charge duration, and \(m\) means the mass of active materials.
## 3 Results  
### Influence of Background Gas Mixture  
Figure 3a compares the results of measurements of methane in carbon dioxide in a concentration range between C\({}_{\text{CH4}}\) = 0% and 100% using the three different methods: (1) the aforementioned device, (2) the autotrigger-approach without phase modulation, and (3) data extracted from the resonance curves employing conventional QEPAS; the resonance curves are given in Figure 3b. Methane was excited at \(\
u\) = 1276.85 cm\({}^{-1}\).  
The black dots show the differences of measurements with and without photoacoustic excitation with manually set phase difference as depicted in the schematic in Figure 2a. The phase difference was adjusted at a concentration of C\({}_{\text{CH4}}\) = 50% to maximize the signal. The difference represents the photo-acoustically induced component of the signal. The red dots show a measurement performed with the phase modulation approach sketched in Figure 2b and the composition of colored symbols  
Figure 3: (**a**) Comparison of results achieved with three different measurement techniques: black dots: autotrigger measurement with fixed phase. The values are derived by subtraction of measurements with blocked laser from those with photoacoustic excitation. Red dots: reading of lock-in amplifier 2 using phase modulation. Colored dots: values extracted from conventional quartz-enhanced photoacoustic spectroscopy (QEPAS) resonance curves given in (**b**) in the respective color. (**b**) Resonance curves for different mixtures of methane and carbon dioxide.  
is extracted from the measurement of whole resonance curves which are given in Figure 3b in the respective colors. The three measurement methods show similar results disregarding a slight offset which is probably due to the different electric circuits used. The photoacoustic signal is proportional to the laser power absorbed in the vicinity of the tuning fork. Since the concentrations are high, the lambert-beer law which governs the absorption cannot be linearized as for trace gases and a nonlinear increase of the signal is expected. The Q-factor changes as the mixture of methane and carbon dioxide is altered. An increase in Q-factor is observed for increasing methane concentrations. On the other hand, not all the output power of the laser reaches the tuning fork; the higher the concentrations get, the more laser radiation is absorbed on the optical path before the tuning fork which is roughly 4 mm long. This leads to a decrease in signal since only the acoustic waves generated between the prongs excite the piezoelectric active oscillation. This explains the maximum of the curve and its decrease at higher concentrations. The part of the curve for concentrations higher than 70% shows two peaks that cannot be explained with the nonlinear absorption behavior but is consistent with all three measurement methods. They are either due to changes in relaxation dynamics or acoustic resonances inside the spectrophone body and will be further analyzed.
## 2 Background and Related Works
### Point-to-Point QKD  
In classical QKD communication, two parties, [PERSON] and [PERSON], exchange secret keys via quantum and classical channels, as depicted in Figure 1. [PERSON] sends \"Qubits\" to [PERSON], each determined by two random classical bits: one for the encoding basis and another for the bit to be sent (0 or 1) [9]. [PERSON] measures the received \"Qubits\" using a randomly chosen basis and informs [PERSON] of his measurement choices over a public channel. They then compare their chosen bases over a classical channel and retain only the measurement results where their bases match--a process known as sifting. In particular, during sifting, [PERSON] and [PERSON] agree on a subset of raw data for subsequent post-processing, which typically involves error correction, verification, and privacy amplification to establish a secret key [20]. If an eavesdropper, [PERSON], intercepts the quantum states during transmission, the no-cloning theorem ensures that any measurement by [PERSON] will disturb the quantum states, introducing detectable errors. Consequently, any potential eavesdropping on QKD can be detected.  
However, the practical implementation of point-to-point QKD is limited by factors such as photon loss and decoherence, which constrain the effective communication distance. To overcome these limitations, various methods, including the use of trusted relays and quantum repeaters, have been proposed to extend the range of QKD systems [21].  
Figure 1: The illustration of point-to-point and long-distance QKD.
# Design and Development of an Embedded Controller for a Hydraulic Walking Robot WLBOT
## 2 WLBOT System Overview  
### WLBOT Structure and Onboard Hydraulic System  
The hydraulic walking robots can walk in different gaits to overcome irregular terrains and obtain better environment adaptability, higher stability, and larger load capacity. WLBOT has a size of about 1.1 m (L) \(\times\) 0.72 m (W) \(\times\) 1.0 m (H) at the standard standing position and a total of about 220 kg weight with an extra load capability of about 50 kg. As is shown in Figure 1, the robot consists of a trunk and four legs, while two front legs both have a three-DOF structure and two hind legs only own a retractable joint with a wheel. Each front leg of WLBOT adopts the same mechanism, which consists of three DOFs (degree of freedom) to meet the locomotion requirement, including root abduction/adduction (RAA), hip flexion/extension (HFE) [29], and knee flexion/extension (KFE). The driving hydraulic cylinders for the hip joint and knee joint are fixed on the thigh, while the root joint cylinder is set on the pelvis, as depicted in Figure 1. Furthermore, WLBOT has two spring damping feet, which can attenuate the shock and vibration and thus improve the energy utilization during the locomotion.  
In order to meet the requirements of heavy load and motion stability of the hydraulic walking robot, each joint unit uses a valve-controlled hydraulic cylinder as its actuator. There are eight joint units in the onboard hydraulic system. The hydraulic pressure is generated by an internal gear pump connected to a relief valve with an adjustable pressure range of 2 to 31.5 MPa. The flow rate of the hydraulic system can be adaptive because the speed of the pump is controlled by a servomotor. The accumulator provides extra flow if needed and reduces pressure fluctuation. All components are powered by a 48 V-100 Ah lithium battery. The manifolds are made of aluminum alloy to reduce mass. A schematic of WLBOT onboard hydraulic system is illustrated in Figure 2 where only two joint units are displayed and the other six joint units are omitted.
## 5 Outage Probability (OP) Performance Prediction Based on BP Neural Network
### The Flowchart of OP Performance Prediction Algorithm  
Figure 3 shows the flowchart of the OP performance prediction algorithm.  
Figure 2: The back-propagation (BP) neural network structure.
## 3 Setups to Extract Equivalent Modal Circuits
### Measurement Setup for Three-Phase EMI Filters  
To estimate the in-circuit IL, it is necessary to characterize the filter in addition to the equivalent modal circuit of a particular configuration. To accomplish this, the S-parameters of the three-phase EMI filter are measured by a four-port VNA (see the setup in Figure 9). Three sets of measurements are performed at different combinations of the filter ports with the other ports terminated in 50 \(\Omega\) loads to get the complete 6\(\times\)6 S-parameter matrix. In order to determine the in-circuit IL, the ABCD parameters of the filter are then deduced from the S-parameters using the analytical expressions in [30]. To compare the IL of different filters, we characterize two different topologies: one-stage and two-stage (manufacturer and model are undisclosed since they are not relevant).
## 1 Introduction  
Coal is one of the most important energy sources and the collapse of roofs caused by weak rock and structural discontinuities is a challenge in the coal mining industry [1]. Grouting materials, which can connect and support the weak structures in mines, have been commonly used to reinforce broken coal and rock in mines to prevent such mine fatalities [2]. Several inorganic materials have been used as grouting materials, for example, water glass (WG) [3; 4; 5], silica sol [6], and cement because they are inexpensive, easily obtained, and non-flammable; however, they shrink easily and the short grouting length that results from poor penetration limits their application [7].  
Meanwhile, there are also many kinds of organic grouting materials, such as polyurethane and epoxy resin, and organic materials burn easily. Due to their ability to penetrate small cracks and voids easily, polyurethane (PU) grouting materials have good fluidity and strong injectability, and they have played an increasingly important role in coal mining [8]. Until recently, rigid polyurethane foam (RPUF) was used as grouting material due to the advantages of easy molding, corrosion resistance and high strength compared with traditional grouting materials. However, considering the real conditions of underground engineering, the poor ventilation and high amount of gas in the working environment, the application of polyurethane in underground engineering was limited as polyurethane is highly flammable. Therefore, it was necessary to modify traditional polyurethane and decrease the reaction temperature, combining the advantages of organic materials and inorganic materials while also enhancing the strength of grouting materials as far as possible to avoid potential danger [9; 10; 11]. However, because of the high heat release during the reaction process, which can easily cause a fire in a poorly ventilated area with high amountsof gas, the application of PU grouting materials in underground engineering has been limited [12; 13]. Recently, several investigations have been performed on the modification of PU grouting materials. [PERSON] et al. selected sodium silicate as a reactive component to form inorganic microspheres-reinforced PU composite [14]. [PERSON] et al. used water glass to prepare polyisocyanurate-WG materials; the strength increased up to 0.16 MPa and the reaction temperature was lower than pure PU [15]. [PERSON] et al. composed a novel PU modified with epoxy resin via copolymerization between polyurethane prepolymer (PUP) trimer and epoxy resin (E-44) [16]. However, these studies mainly focused on improving the performance of grouting materials, and no reports have focused on reducing heat release.  
Herein, WG and low-viscosity epoxy resin (NPEF-170) were simultaneously employed to reduce the heat released and improve the strength of the grouting material. The reaction temperature, mechanical properties and the microstructure of the polyurethane composite foams were studied in detail.
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 4 Results and Discussions
### Model Performance
By including the northern zones, even those without direct soil samples, our research presents a comprehensive SOM overview crucial for future land-use strategies. Crucially, the integration of remote sensing allows for ongoing, extensive monitoring, capturing both temporal and spatial SOM changes--an advantage unattainable with traditional soil analyses alone. Within the discussion on model confidence relative to spatial resolution, it is crucial to highlight the inherent advantages of employing satellite remote sensing. The capability of satellite imagery to correlate spatial attributes of image pixels with the terrestrial granularity of the ground cells offers a substantial edge in large-scale SOM mapping, such as in our study of the Batifa region. Our results with the XGBoost model yielded a high accuracy, suggesting that the chosen resolution of satellite imagery is apt for generating robust estimates in the region. Nevertheless, elucidating the relationship between the confidence level, particularly the 95% confidence interval, and varying spatial resolutions warrants a more exhaustive study. Such an analysis would necessitate the resampling of datasets at diverse resolutions and subsequently evaluating the performance of machine learning models. While this aspect was outside the purview of our current research, it poses a significant avenue for future investigations. For upcoming studies, understanding the intricate interplay between spatial resolution and prediction confidence can illuminate the selection of optimal satellite datasets, especially pertinent in regions characterized by diverse land cover types and soil heterogeneity.