esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present study reports the engineering of functionalized chitosan (CH)-based biosorbent material. Herein, a two-step reaction was performed to chemically modify the CH using 1,4-bis(3-aminopropyl) piperazine to incorporate nitrogen basic centers for cations sorption from the aqueous environment. The resultant functionalized chitosan-based sorbent material was designated as CH-ANP and characterized using various analytical techniques, including elemental analysis, Fourier-transform infrared spectroscopy (FTIR), \({}^{13}\)C NMR (in solid-state), X-ray diffraction, and thermal analysis. Then, the newly engineered CH-ANP was employed for the removal of copper, lead, and cadmium in the aqueous medium. Langmuir sorption isotherm analysis revealed that the highest sorption abilities achieved were 2.82, 1.96, and 1.60 mmol g\({}^{-1}\) for copper, cadmium, and lead, respectively. Linear and nonlinear regression methods were deployed on the sorption data to study the behavior of the Langmuir, the Freundlich, and the Temkin sorption isotherms. Among the four different forms, the Langmuir isotherm type 1 fit well to the experimental data as compared to the other models. It also showed the lowest values of error, and a higher correlation coefficient than the Freundlich and Temkin models; thus it was the best fit with the experimental data compared to the latter two models. In conclusion, the findings suggest that chemically modified novel materials with enhanced Lewis basic centers are useful and promising candidates for the sorption of various toxic cations in aqueous solution.  
sorption; chitosan; functionalization; glycolidyl methacrylate; characterization; cation removal; isotherm +
Footnote †: journal: Applied Chemistry and Chemical Engineering
## 4 Experiments
### Performance Evaluation on Synthetic Distortion  
In this subsection, we describe the verification of the performance of the proposed HFNet on the synthetic distortion datasets. For performance comparison, this paper selected 18 state-of-the-art BIQA algorithms to perform the same test. Among them, there were six handcrafted quality methods, including DIIVINE [61], BRISQUE [22], CORNIA [26], NIQE [27], ILNIQE [21], and HOSA [28], and 12 representative deep-learning-based quality metrics, including BIECON [34], PQR [35], WaDIaM-NR [24], RAN4 IQA [36], SFA [37], NSSADNN [38], HyperNet [25], DBCNN [39], CaHDC [40], UNIQUE [62], CLRIQA [63], and VCRNet [64]. The experimental results of the synthetic image databases are shown in Table 2, where the top two results are shown in bold.  
It can be seen from these results that the proposed HFNet achieved excellent performance. For the CSIQ database, the proposed HFNet achieved the highest SRCC value and the second best PLCC value. The WaDIaM-NR [24] achieved comparable performance to the proposed HFNet. Further observation shows that the performance of the DBCNN and VCRNet were similar and better than other algorithms. For the TID2013 database, the proposed HFNet obtained the optimal SRCC and PLCC of 0.893 and 0.911, respectively. Although the proposed HFNet ranked first, its performance value was far lower than its performance on CSIQ database. This is not hard to understand, as the TID2013 contains more distortion types and levels, which itself is more challenging. In addition, most of the deep-learning-based quality methods were superior to the traditional handcrafted methods. From the results in the table and the above analysis, it can be seen that the proposed metric showed excellent performance in evaluating the synthetic distortion.
## 3 Results and Discussion
### Bioactive Compounds  
The amount of antioxidant activity (AA) of the samples was obtained through the DPPH test and determination of free radical inhibition percentage. The results showed that the amount of antioxidant activity of the dried samples was very close (66.50-68.44%) and no significant difference was observed between the methods (Table 5). The details of the bioactive compounds of the dried samples in different strategies are shown in Table 6. The amount of antioxidant activity in the samples dried by the infrared method (first strategy) was slightly higher than that of the samples dried by the convection method or other combined methods. Among the combined strategies, the amount of antioxidant activity of the dried samples in the fourth strategy (IR+HA) was higher than that of the other samples (Table 7). According to the results, the highest antioxidant activity recorded among the combined methods (IR500-HA70) was not much different from the highest antioxidant activity recorded in the hot air method (HA40). The amount of total phenolic compounds (TPC) for the dried samples varied from 3.14 to 21.84 (mg GAE/g), and it was higher for the samples that were dried through infrared waves than for the other samples. In the combined strategies, the amount of total phenolics in the third strategy (HA+IR), with an average of 8.92 mg GAE/g, was higher than that of the other strategies. The highest amount of total phenolic content among the combined methods (HA55-IR750) was about 11.2% higher than the highest amount of total phenolic content recorded by the hot air method (HA40). The amount of total flavonoid content (TPC) was in the range of 3.14-21.64 (mg CE/g). The total flavonoids value in the first strategy was higher than in the other strategies. Among the combined strategies, the third strategy (HA+IR) had better conditions of total flavonoid content (7.99 mg CE/g on average). The highest amount of total flavonoid content recorded among the combined methods (HA55-IR750) was not much different from the highest amount of total flavonoid content recorded in the hot air method (HA40). Figure 7: Drying efficiency in different strategies.
## 4 Discussion  
The software prototype based on an artificial neural network is a software application that uses machine learning algorithms to analyze meteorological observation data and predict the optical and geometric characteristics of HLC. The prototype can be used to test different models and determine the most effective approach. It can also be used to demonstrate the capabilities of the final product. The resulting tool allows a preliminary evaluation of the BSPM elements, boundaries, and detection altitudes of the HLC. At the moment, a weak dependence on the meteorological parameters for some HLC parameters can be noted. But to answer this question unambiguously, more data are needed to clarify the result and to add additional information about the environment in the area of the experiment, which we plan to realize in the near future.
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 5 Underwater Concrete Crack Detection Using Machine Learning Approaches  
Cracks in underwater concrete structures can significantly impact their structural integrity and longevity. As described in the sections above, the traditional methods for detecting cracks in underwater structures are often time-consuming, costly, and require skilled operators. In recent years, machine learning approaches have shown promise for detecting cracks in underwater structures quickly and accurately in onshore environmental conditions [1; 2]. In this section, we explore the potential of using machine-learning-based approaches to identify surface cracks on concrete structures in underwater conditions. The concrete cracks in underwater environments are impacted by illumination and presence of marine life, which makes the problem harder to solve compared to surface crack detection in onshore or above-water environments. Two tests are conducted: First, a test involving the standard machine learning network Alexnet [2], trained on surface crack detection for onshore environments described in [1], is used to identify underwater cracks. Next, a test involving an improved version of the network presented in [1], where the network is further trained using augmented underwater images database prepared in [59], is used. The two test cases and their results are presented next.
## 4 Conclusions  
This study conducted a comparative LCA of hydrogen production through biogas reforming and agricultural residue gasification, offering critical insights into their environmental performance. The results demonstrate that agricultural residue gasification exhibits a substantially lower global warming potential (GWP) of 1.30 kg CO\({}_{2}\)-eq/kg H\({}_{2}\), which is 4.89 times lower than biogas reforming (5.047 kg CO\({}_{2}\)-eq/kg H\({}_{2}\)). This advantage stems from gasification's reliance on carbon-neutral feedstocks, which offset process-related emissions and reduce dependency on fossil-based energy inputs. Conversely, biogas reforming's higher GWP is driven by methane leakage during anaerobic digestion and fossil-intensive electricity consumption.  
While gasification outperforms biogas reforming in most environmental impact categories, including fossil resource scarcity, toxicity, and eutrophication, it presents trade-offs in water consumption (5.37 m\({}^{3}\)/kg H\({}_{2}\) vs. 0.05 m\({}^{3}\)/kg H\({}_{2}\)) and land use. These challenges highlight the need for optimized feedstock management to prevent competition with food production and the adoption of dry gasification technologies to mitigate water demands. Biogas reforming, though less sustainable overall, benefits from lower water usage and existing waste valorization pathways, emphasizing the importance of methane leakage mitigation and renewable energy integration.  
The findings underscore agricultural residue gasification as a promising pathway for low-carbon hydrogen production, aligning with global decarbonization goals. However, achieving sustainability requires addressing systemic inefficiencies, such as grid electricity reliance and emission control during syngas cleanup. Future research and policy efforts should jointly prioritize the advancement of agricultural residue gasification, particularly in biomass-rich regions, by integrating renewable energy sources, improving gasification technologies, and aligning life cycle assessments with socio-economic analyses to support holistic and sustainable energy planning.  
Conceptualization, M.A. and K.P.; methodology, M.A. and M.L.; software, M.A.; resources, K.P.; writing--original draft preparation, M.A.; writing--review and editing, M.A.; supervision, K.P. and M.L.; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript.  
This publication was funded by the research subsidy allocated for 2025 (08/030/BK_25/0151).  
Not applicable.  
This study's original contributions are detailed in this article; any further inquiries can be addressed to the corresponding author.  
The authors express their gratitude to the Silesian University of Technology for supporting this research through funds and materials.  
The authors declare no conflicts of interest.
## 5 Conclusions and Prospects  
### Conclusions  
(1) Herein, the test methods for measuring strength in the UHPC-NC interface bonding zone were reviewed. For external load applications, the direct tensile test can cause eccentric force, making it unsuitable for measuring adhesion, because the interfacial transition is easily damaged. The split test is easily affected by additional bending deformation when the direct shear test is performed. Moreover, the slant shear test is easily affected by the interface angle and roughness. Bending tests have a low usage rate and small load bonding area, which can easily cause stress concentration. Therefore, different test methods considerably affect the measurement of the interface bonding strength of composite structures.  
(2) A considerable difference between the UHPC-NC interface bonding shear strength test and formula values calculated from the relevant codes in various countries was reported. In particular, the conservative calculated values were obtained from specifications because they only focus on the NC-reinforced interface and do not fully consider the influence of the mechanical properties of UHPC.  
(3) The main factors affecting the interface bonding strength are interface roughness, fiber type and content, interfacial agent type, interfacial moisture content, existing concrete strength, cementified materials, curing conditions, chloride ions and other erosive substances, and the freeze-thaw cycle.  
(4) Among the multiple factors affecting the interface bonding strength, the interface bonding performance can be considerably improved by appropriately increasing the interface roughness. However, excessive interface treatment can damage the substrate and reduce the interface bonding performance. Fiber addition effectively improves the interface bonding performance of UHPC-NC by reducing UHPC shrinkage and improving the mode of interfacial load transfer. Although most interface agents can improve the interfacial bond strength, the effect of using UHPC as an interface agent is controversial and needs further study. The bonding properties of the UHPC-NC interface are affected by the water content of the interface. The interface transition area density and bonding strength improved under wet saturation and SSD condition.  
Figure 21: Ratio of the slant shear strength under freeze–thaw cycles [96].  
(5) The presence of cementitious materials, such as silica fume and fly ash, in UHPC can improve the interface microstructure, which is suitable for increasing the interface bonding strength. The strengthening effect of the interfacial agent can be improved by increasing the existing concrete strength. As the curing system affects the UHPC interface bonding strength, selecting a suitable curing system is essential. The interface bonding strength decreases when harmful chloride ions and other erosive substances enter the UHPC-NC interface. An interfacial agent and fiber can effectively improve the impermeability of the interface between old and new concrete and the bonding strength of the UHPC-NC interface.
## 1 Introduction
Some simple digital filtering algorithms, such as digital filter and adaptive filter, easily achieved processing online but did not show good performance [11]. Besides, signal preprocessing algorithms show significant defect segmentation and extraction, baseline estimation, and channel balance methods, mainly including subsection mean method [5], wavelet multiresolution analysis [9], Hilbert-Huang transformation [10], and morphological filtering algorithm. Aiming at traditional devices of complex structure, big weight, and low reliability, we proposed a novel wire rope testing technology using unsaturated magnetic excitation (UME). A GMR sensor array was used to acquired two-dimensional MFL signal distribution, acquiring high SNR signal. In this paper, we directly utilized digital image processing technology to filter, locate, and extract defect images after baseline estimates and channel balance. Because the sensor's size caused low circumferential resolution, a super-resolution (SR) reconstruction method based on Tikhonov regular multiframe was proposed. We calculated a suitable down-sampling interval, then axial down-sampling data formed multiframe images and reconstructed high-resolution images. Finally, parts of statistical texture and invariant moment characteristics were selected to train an RBF neural network. Through experimental testing, classification accuracy rate was up to 91.43% with one wire-limiting error. Comparing the remanence method in [9; 10], the detection system is smaller, lighter, and more convenient than previous papers, and the UME signal is smoother. The filtering algorithm system has less computation; in addition, recognition error is smaller than previous papers.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 1 Introduction  
One of the biggest problems facing modern electric utilities is a requirement to offset increased demand using demand-side management and demand-response algorithms [1] or optimize the supply [2]. The aggregate load is increasing monotonically, and the structure of the grid is evolving to support distributed resources. The grid is already stressed supporting modern loads with switching power supplies, highly dynamic usage profiles, and the proliferation of consumer/personal electronics. These new stressors overlay the already complex configuration of large conventional loads such as air conditioning systems, electric motors and water heaters. The continuing deployments of mobile loads such as electric vehicles will create complex new peak load patterns that were not foreseen by grid architects. As a result, utilities are evaluating a broad range of approaches to offset the increase in demand and complexity of aggregate load profiles. These approaches range from changing customer behavior via Demand Side Management (DSM) [3; 4; 5; 6] to large capital expenditures and new generation facilities [7; 8]. Simultaneously, the growth in functionality of machine-learning techniques has created an intersection point which may provide benefits in the optimization of distributed systems. Current ML-enhanced applications range from tourist guidance [9] to malware detection [10] to cataract detection [11], and similar approaches may provide substantial benefits in the energy market. In particular, ML approaches directed at implementation in embedded and/or \"server-less\" or \"edge\" computing systems [12] make the combination of ML and applications such as voltage optimization a very fertile environment for exploration. Finally, ML approaches have been found useful for energy disaggregation algorithms that provide important insights for optimal demand response [13].
###### Abstract  
High-accuracy and high-precision inter-satellite ranging enhances the orbital accuracy of the Global Navigation Satellite System and facilitates Autonomous Navigation without requiring ground stations. This study proposes a novel phase discrimination method based on pseudo-random code phase modulation coherent laser ranging, which solves the problem of mutual restriction between ranging accuracy and ranging precision in the traditional method. The early-late correlation peaks are obtained via data shifting, while the early and late codes remain unchanged. The characteristic parameters of the early-late discriminator model are calibrated by the actual ranging system, which achieves enhanced ranging accuracy and precision simultaneously. Ground test results indicate that for the static target, the accuracy of the distance measurement is 0.56 mm, while the precision is 0.34 mm. The ranging accuracy of the proposed method has improved by a factor of 91 compared to the traditional method. For dynamic targets, the accuracies of the distance and speed measurements are 0.38 mm and 0.44 mm/s, respectively.  
pseudo-random code; range and velocity measurement; laser ranging +
Footnote †: journal: _photonics_  
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Footnote 0: 0000-0000
###### Abstract  
Fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) was applied to a qualitative and quantitative analysis of gadolinium (Gd) in mixed oxide samples, simulating nuclear fuel debris in the damaged reactors of the Fukushima Daiichi Nuclear Power Station. The surrogate debris was prepared from mixed oxide materials containing Gd\({}_{2}\)O\({}_{3}\), with varying Gd concentrations. The emission spectra of the surrogate debris show that the optical emission lines at 501.5 nm and 510.3 nm are suitable for Gd detection in the nuclear fuel debris. LIBS measurements were further performed under gamma irradiation (0-10 kGy/h), resulting in a decrease in spectral intensities due to radiation-induced damage to the optical fiber. For quantification of Gd, robust calibration curves against gamma irradiation were established from the intensity ratio of Gd (501.5 nm)/Ce (474.5 nm) emission lines, yielding the limits of detection for Gd in the range of 0.03-0.08 wt%. These results demonstrate that FO-LIBS is a potential tool for in situ and remote analysis of nuclear fuel debris.  
laser-induced breakdown spectroscopy; fiber-optic; rare earth elements; nuclear fuel debris; gamma irradiation +
Footnote †: journal: Applied Physics Letters
# Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
## 4 Simulation Verification and Discussion
### Circular Contour Tracking  
Circular contour tracking control is more complicated than the linear contour tracking because of the nonlinear property of the contour error coefficients in Equation (4). In this simulation, a circular contour with a radius of 0.6 m and the center at (0, 0) is tracked in 10 s, using the same control gains listed in Equation (30).  
Figure 7 presents the comparison results based on PD and CC-PD control laws. Figure 7a shows the tracking errors in the joint space, Figure 7b is the corresponding controlled torques, and Figure 7c is the tracking and contour errors at the end-effector under PD and CC-PD control, respectively. The maximum contour errors are \(8.8\times 10^{-4}\) m under PD control and \(7.0\times 10^{-4}\) m under CC-PD control. It is shown that, under very similar controlled torques shown in Figure 7b, the CC-PD control obtained better contour tracking performance than the PD control, even in a degraded tracking performance for joint 3 compared with the counterpart of the PD control, as shown in Figure 7a. Therefore, the effectiveness of the proposed CC-PD control is verified for complex contour tracking problems.  
Figure 7: Comparison of tracking errors for circular contour under PD and CC-PD; **(a)** Tracking errors in joint space for PD control and CC-PD control; **(b)** Controlled torques for PD control and CC-PD control; **(c)** Tracking and contour errors of the end-effector for PD control and CC-PD control.  
To test the effort of the CC control gains on the contour tracking performance improvement for circular motions, different factors are used with varying CC gains and the contour tracking results are shown in Figure 8 where the red curve CC-PD (\(\times 0\)) represents the PD control contour error. It also shows that, with the increase of the CC control gains, the maximum contour errors become smaller and smaller, resulting in a similar conclusion obtained in linear contour tracking control.  
To verify the effect of the control gains on the contour tracking performance improvement, high control gains with a factor of 10 are used for both PD and CC-PD control in the circular contour tracking example, and Figure 9 shows the contour errors for both control laws. Compared with Figure 8, one can see that the contour errors are reduced around 10 times when the control gains are increased 10 times. It demonstrates that the control gains have significant contributions for contouring control, and it proves the theorem developed in this paper.