esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[15] utilized data from the China Environmental Statistics Database and China Urban Construction Yearbook to estimate national wastewater treatment-related GHG emissions in 2020. Despite these efforts, none of the aforementioned studies incorporated comprehensive, plant-specific operational data, leading to uncertainties in carbon accounting. [PERSON] et al. [2] leveraged the China Urban Construction Statistical Yearbook and Ministry of Ecology and Environment reports, employing statistical modeling to analyze spatiotemporal GHG emission patterns in coastal prefecture-level cities and identify drivers of wastewater treatment emissions. [PERSON] et al. [16] investigated temporal-spatial GHG emission variations across eight Beijing WWTPs between 2007-2021, exploring seasonal impacts, effluent standard upgrades, operational parameters, and clean energy adoption. However, their analysis omitted indirect emissions from chemical usage and relied on foreign emission factors (e.g., Italian data for CO2), potentially introducing biases. Existing research predominantly relies on statistical yearbooks rather than granular, plant-level operational data, particularly neglecting chemical dosing details. To address these gaps, this study aggregates full-scope 2024 operational data from 98 WWTPs in eastern China, encompassing water treatment volumes, energy consumption, sludge production, and precise chemical dosing records (e.g., agent types, dosages, concentrations). This dataset ensures accuracy and comprehensiveness in carbon accounting. Furthermore, the study adopts the Assessment Standard for Carbon Mitigation in Municipal WWTPs (2023) published by the China Urban Water Supply and Drainage Association, with methodological refinements including China-specific emission factors for CH4 and N2O tailored to regional treatment processes. These enhancements enable a high-resolution quantification of actual 2024 carbon emissions, offering critical insights for targeted mitigation strategies. [PERSON] et al. [17] analyzed GHG emissions from 5155 municipal WWTPs in China between 2009 and 2019 using data from the Ministry of Housing and Urban-Rural Development's \"National Urban Wastewater Treatment Management Information System\" and local government disclosures. They proposed mitigation technologies--such as heat pump energy recovery, anaerobic digestion for biogas production, and high-solids side-stream digestion--and evaluated their economic viability. East China, the most economically and culturally developed region in China, encompasses Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, and Taiwan provinces. Based on daily operational data from 98 WWTPs across East China in 2024, this study established a high-precision carbon emission accounting framework to comprehensively quantify emissions from these facilities. Subsequent analyses elucidated regional emission patterns, low-carbon operational performance, and influencing factors. A low-carbon operation evaluation system was further developed to propose actionable strategies for synergistic pollutant reduction and carbon mitigation.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Modified-Graphene-Based ECG Electrodes
#### 2.5.4 Glycerol-Modified Graphene-Oxide ECG Electrode  
Compared with graphene, the preparation of GO is simpler and cheaper. It is an effective way to reduce the cost of graphene electrodes by transforming GO that can be prepared in large quantities into graphene materials. Based on esterification of glycerol (Gl) and a polyvinyl alcohol (PVA) polymer host, [PERSON] et al. proposed a GO/Gl/PVA ECG electrode [72]. First, GO was prepared by a modified Hummers' method and was mixed with Gl at pH 1-3. After a full reaction, the conductive GO/Gl was further mixed with PVA to form an elastic conductor. Finally, the GO/Gl/PVA was put into the ECG electrode mold (Figure 4d). As the carrier of the GO/Gl, PVA enables conductive reduced GO sheets to be evenly distributed. Besides, since PVA is a water-soluble polymer, after applying the electrodes to skin using water, the resulting ECG electrode shows high flexibility, good adhesion, and low impedance between the electrode and human skin. The detected ECG signal has a high signal-to-noise ratio.
## 1 Introduction
The publications of the diverse aforementioned studies led to extensive research for enhancing the specific heat of molten salts. [PERSON] et al. reported numerical and experimental results for enhancing the specific heat of a binary nitrate salt mixture [34]. In 2017, [PERSON] et al. reported that the mixture of sodium nitrate and potassium nitrate was enhanced by CuO nanoparticles that are produced in situ via the high-temperature decomposition of copper oxalate with the nitrate salt mixture [35]. Most recently, in situ synthesized MgO nanoparticles enhanced the specific heat of a nitrate salt by up to 118% in the solid phase and 168% in the liquid phase [36]. [PERSON] et al. explained the specific heat enhancement of molten salts according to the ionic exchange capacity of nanoparticles using Fourier-transform infrared spectroscopy tests [37]. Numerous studies have been performed to elucidate the specific heat increase of molten salt nanofluids and identify the mechanisms underlying the specific heat enhancement. However, the reason for the increase in the specific heat of molten salt nanofluids has not been clearly investigated. According to previous studies [20; 28], the increase of the specific heat of nanofluids is not solely a result of the nanoparticles, but is closely associated with interactions between the nanoparticles and the base fluids. For effectively harnessing thermal energy storage media, such as molten salt mixtures, it is important to understand their specific heat-enhancing mechanism. In this study, the specific heat of binary carbonate salt mixture-based graphite nanofluids was experimentally measured using a DSC. Several parameters--the chemical composition of the base fluid, the nanoparticle concentration, and the dispersion homogeneity of nanoparticles--were investigated, which are considered as influential parameters for the specific heat enhancement of nanofluids. Five different compositions of the base fluid (mole fraction of two salt components) were prepared, and the thermal property was measured for fixed nanoparticle concentrations. Conversely, the specific heat of the nanofluids was examined as the concentration of the nanoparticles varies for the uniform chemical composition of the base fluid. Moreover, concerning the dispersion of the nanoparticles, concentrations of a surfactant, used in order for the nanoparticles to be well dispersed, were also increased to appraise the effect of the particle dispersion. The experimental results obtained in this study helps us in understanding quantitative effects of each parameter on enhancing specific heat of the molten salt mixture by doping with the nanoparticles.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic
### Slot Bonding  
While, traditionally, TSCH only uses a single PHY per network, slot bonding allows the combination of multiple PHYs in one single TSCH network. As such, the performance of each node in the network can significantly improve by adapting its PHY to the local propagation characteristics and the application's requirements.  
To combine different PHYs in a single TSCH schedule, slot bonding takes into account the data rates of the different available PHYs. More specifically, for a specific PHY, it bonds multiple time slots of _regular_ length together to a _bonded_ slot that is long enough to fit the transmission of a data packet and the reception of an acknowledgement. As such, by adapting the length of each bonded slot to the data rate of the used PHYs, slot bonding can combine different PHYs in a resource-efficient manner. In contrast, as explained earlier, traditional TSCH relies on fixed-duration slots, long enough to send a packet of any size given the fixed data rate, which results in wasted airtime if different PHYs with different data rates are used simultaneously. In our previous work, it was shown that the resource-efficiency of slot bonding results in a better network overall packet delivery ratio [7]. For more results and a detailed explanation on slot bonding, the reader is referred back to that work.  
Figure 1 shows that node Y needs PHY2 to send its data to the root node. Therefore, it needs to bond three regular time slots together to one bonded slot of 30 ms. Similarly, the minimal shared cell is also configured with PHY2, as this is considered the PHY with the best robustness and range features, allowing all nodes to bootstrap and receive network and routing information.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Effect of Changing the Distance Factor of Adjacent Buildings  
By varying the distance between the adjacent building and the ongoing excavation work, this study aimed to summarize the impact of existing adjacent buildings on deep excavation projects. The distance factor was categorized into three types: one times the excavation depth (1 He), two times the excavation depth (2 He), and three times the excavation depth (3 He). The study set the building load to 5 F (91 kPa) and 12 F (195 kPa), with basement depths (Df) set to 0.25 times the He, 0.5 times the He, 0.75 times the He, and 1 times the He, where He is the excavation depth of 16.2 m used in the deep excavation case. Simulation analyses were conducted with fixed building loads and basement depths under three different soil behavior models. The illustration diagram of the distance factor is shown in Figure 9.
###### Abstract  
This paper presents a theoretical framework to enhance the prediction of dynamic responses in complex mechanical systems, such as vehicle structures, by incorporating both translational and rotational degrees of freedom. Traditional acceptance coupling methods often neglect rotational effects, leading to significant inaccuracies at higher frequencies. Additionally, approaches that implicitly include full dynamics frequently result in redundancy of generalized coordinates, especially at connection points. To address these limitations, the generalized acceptance coupling method using Frequency-Based Substructuring is extended to explicitly account for rotational dynamics resulting in a refined GRCFBS approach. This extension enhances both the understanding and prediction of system responses, which are represented through the acceptance matrix or Frequency Response Function. Building on [PERSON]'s foundational work, the proposed framework introduces a practical, generalized formulation that explicitly incorporates full translational and rotational dynamics at each substructure node. This explicit definition provides deeper insights into system behavior, particularly for complex interactions between substructures under weak and strong coupling scenarios at interface points. The Euler-Bernoulli beam theory is employed to model rotational behavior at critical points, yielding reduced-order and explicit acceptance matrices for substructures in the coupling process. The methodology's accuracy and applicability in capturing resonance and anti-resonance modes are validated through two case studies: the coupling of two flexible subsystems and the integration of flexible and rigid components. Results are benchmarked against numerical finite element analysis, and all limitations and potential improvements are discussed. By directly incorporating rotational dynamics directly, this approach enables more reliable dynamic response predictions under multi-directional loading conditions, particularly for vehicle and machinery system design. The GRCFBS method offers a versatile and reliable tool for dynamic system analysis, with significant potential for vibration analysis over a broad frequency range.  
Euler-Bernoulli beam theory; acceptance coupling; translational and rotational DoFs; frequency-based substructuring; generalized acceptance coupling; dynamic response prediction; system dynamics analysis +
Footnote †: journal: applied sciences
## 3 VRIS Image Steganography Model
### Image Preprocessing  
Objective: Through a series of preprocessing steps, increase the diversity of the training dataset, enhance the model's generalization ability, and optimize the model's learning of image features.  
Figure 1: The fundamental architecture of the VRIS Steganography Network.  
Input: Original image dataset \(D=\{I_{1},I_{2},\ldots,I_{N}\}\), where N is the total number of images.  
Output: Preprocessed image set \(D^{\prime}\).  
Steps:  
1. Random cropping (training set):  
For each image \(I_{i}\) in the training set, use the Random Crop function to randomly select a fixed-size area for cropping, generating the cropped image \(I^{\prime}_{i}\). Repeat this process until the entire training set is cropped, resulting in the cropped training set \(D^{\prime}_{train}\).  
2. Center Crop (Testing Set):  
For each image \(I_{j}\) in the testing set, use the Center Crop function to crop a fixed-size area around the center point of the image, generating the cropped image \(I^{\prime}_{j}\). Repeat this process until the entire testing set is cropped, resulting in the cropped training set \(D^{\prime}_{test}\).  
3. Convert to Tensor:  
For each image, \(I^{\prime}\) and \(I^{\prime}\) in \(D^{\prime}_{train}\) and \(D^{\prime}_{test}\), use the Tensor function to convert them to tensor format \(T^{\prime}\) and \(T^{\prime\prime}\). This results in the tenderizing set \(D^{tensor}_{train}\) and testing set \(D^{tensor}_{test}\).  
4. Image Normalization:  
For each tensor \(T^{\prime}\) and \(T^{\prime\prime}\) in \(D^{\prime}_{train}\) and \(D^{\prime}_{test}\), apply the transforms. Normalize () function for normalization, resulting in the normalized tensor \(T^{\prime}_{norm}\) and \(T^{\prime\prime}_{norm}\). This normalization process eliminates scale differences between features by adjusting pixel value scales, enhancing the model's learning efficiency regarding image features.  
5. Dataset Random Subsampling:  
If the dataset is too large, you can use a random subset. The sample function performs random subsampling \(D^{tensor,norm}_{train}\) to obtain the training set \(D^{sub}_{train}\) after subsampling, which helps to reduce the consumption of computing resources and time (as shown in Figures 2 and 3) and improves the efficiency of the training process.  
Figure 2: The curve of the loss function over time.
# Memory Efficient Implementation of Modular Multiplication for 32-bit ARM Cortex-M4
## 1 Introduction
### Comparison of CANS'19  
Previous work in CANS'19 [14] successfully evaluated the SIKE round 2 schemes on 32-bit ARM Cortex-M4 microcontrollers. In order to accelerate the execution timing, modular multiplication, and squaring operations are optimized for specific parameters of SIKEp434, SIKEp503, and SIKEp751. Unlike previous works, we proposed scalable modular multiplication and squaring. The proposed implementation reduces the code size significantly and supports all parameters, including RSA, ECC, and SIKE, in single code. For the implementation of RSA, the proposed implementation only supports the RSA parameters, as described in Table 1. For instance, we implemented the SIKE round 2 schemes on 32-bit ARM Cortex-M4 microcontrollers in Section 3. The proposed implementation obtained the smallest code size and reasonably fast execution timing. Previous work in CANS'19 [14] requires 44,688 bytes while the proposed implementation requires 28,816 bytes for SIKEp751, which is a code reduction of 35.5%. Furthermore, we first implemented the SIKEp610 protocol on 32-bit ARM Cortex-M4 microcontrollers. This result first covers all SIKE protocols.
# Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles
## 1 Introduction
Optimal and robust navigation and control systems are required to meet the performance challenges and energy requirements of the modern day. Increased levels of autonomy will also require advanced multimode navigation and control system architectures, including hierarchical mode-switching control strategies to handle challenges such as autonomous driving at junctions and roundabouts [6] and sensor fusion-based navigation algorithms in global navigation satellite system (GNSS) degraded environments [7]. Computationally efficient and optimal machine learning-based methods are also increasingly employed for autonomous vehicle path planning and following in urbanised or challenging environments or to deal with challenges linked to the vehicles themselves, as in the case of differential-drive ground vehicles [8]. When multiple vehicles are involved, some of the future GNC challenges will involve collaborative navigation and control problems, with accuracy, synchronicity and connectivity requirements, involving groups of similar (e.g., platooning with communication delays, as in [9]) or heterogenous vehicles to offer real-world solutions that other configurations cannot offer [10]. The Special Issue that led to this book captures the diversity of the research, hot topics and latest advances in GNC systems for a good range of transport and autonomous vehicles. It brings together some of the theoretical developments, as well as simulation-based and experiment-based studies in the field. This editorial aims to encourage the reader to explore these publications and to appreciate the commonalities between the main emerging GNC research priorities for a diverse range of transport and autonomous vehicles.
## 3 Results and Discussion  
### Volatile Profile and Composition  
HS-SPME is a standard method used for the isolation of volatile compounds; and it is considered as an environmentally friendly technique (due to no solvents are used), selective and very sensitive [19]. This technique has been successfully used to establish the volatile profiles of different matrices such as herbs, wines, vegetables, and fruits [20]. Volatile composition has been investigated in different pomegranate products, such as pomegranate juice [21; 22] and dehydrated pomegranate arils [16]. It was also studied in different fruits such as jujube [23], quinces [24] and figs [25]. However, the present study is the first one evaluating the combined effects of two factors (pomegranate cultivar and ratio pureejuice) on the volatile profile of smoothies blended with different Mediterranean fruits. Table 1 shows the retention indices used for the identification of the compounds, together with the main sensory descriptors of each of the volatiles. Twenty-three volatile compounds were isolated, identified, and their relative abundance determined in the pomegranate smoothies blended with fig, jujube and quintce puree samples using this method. Previously, 12 and 14 different compounds were identified in the PW and PM juices [22], indicating that the addition of Mediterranean fruits increased the volatile profile on pomegranate products. Among identified volatile compounds (Table 2), several common compounds were previously detected in _Mollar de Elche_ and _Wonderful_ pomegranate juices (V1, V3, V4, V7, V11, V13, V15, V17, V18, V19 and V20) [22], and in heat treated pomegranate-based products (dried arils) (V1, V3, V4, V13, V17, V18 and V21) [16]. On the other hand, V4, V6, V11, V12, V13, V14, V15, V18 and V19 were previously identified in quinces fruits [24], while V1, V3, V4, V8, V11, V12,V14, V14, V17 and V19 were identified in previous studies of jujube fruits [23; 26; 27]. As to figs fruits and dried figs, V1, V2, V4, V5, V6, V8, V10, V11, V13, V14, V15, V16 and V18 were already detected [25; 28]. The combinations of different fruit matrix and heat treatments for pasteurization could generate other volatile compounds not previously described and identified (V9, V22 and V23).
## 2 Related Work  
The research methodology presented in this paper primarily encompasses two key aspects: point cloud segmentation and motion vector estimation. Most existing studies focus on addressing specific components of these topics. Therefore, the following discussion will elaborate on and analyze related research efforts concerning both point cloud segmentation and scene flow estimation.
## Materials and Methods  
### Materials  
The soil sample was collected from a polluted farmland near a lead-zinc mine in Chongzuo city, Guangxi, China. Basically, soil was collected from the surface layer (0-20 cm) of a cropland area. Sampling took place in 25 points over a 0.5-ha area, totaling an amount of 50 kg of soil. After removing leaves, stones, and other miscellaneous, the soil sample was air-dried, crushed, and sieved to 1 mm to conduct general analyses. Part of the soil was sieved to 0.15 mm to conduct the total content measurement of Cd and Pb in soil. The soil sample GBW07456 (GSS-27) was used as the quality control (QC) sample of the total content measurement of Cd and Pb, and purchased from Shide Jia Technology Co., Ltd., in Shenzhen, China. The soil sample GBW07459 (ASA-8) was used as the quality control (QC) sample of the pH and cation exchange capacity (CEC) measurement, and purchased from Aike Yingchuang Biological Technology Co., Ltd., in Beijing, China. The physicochemical properties of the experiment soil are shown in Table 1.  
Preparation of Silk-worm excrement biochar (BC): silk-worm excrement was collected from Guangxi Silk-Worm Technology Promotion Centre. The silk-worm excrement was placed in a ceramic crucible for weighing. The crucible was then heated in a muffle furnace (Nabertherm LT40, heating rate: 20 \({}^{\circ}\)C/min; ventilatory rate: 2 mL N2/min) at 500 \({}^{\circ}\)C for 3 h, and then removed after cooling to room temperature. The crucible was then weighed to calculate the silk-worm excrement biochar yield at 500 \({}^{\circ}\)C pyrolysis.  
\[\text{Biochar yield }(\%)\ =\ (\text{m}_{1}/\text{m}_{0})\ \times\ 100\%, \tag{1}\]  
where m\({}_{0}\) (g) was the weight of silk-worm excrement before entering the muffle furnace, and m\({}_{1}\) (g) was the biochar weight of silk-worm excrement after charring.  
The ash content was calculated by placing a fixed amount of biochar in a ceramic crucible, charring at 800 \({}^{\circ}\)C in a muffle furnace, and recording the weight once a constant value was obtained.  
\[\text{Ash content }(\%)\ =\ (\text{M}_{1}/\text{M}_{0})\ \times\ 100\%, \tag{2}\]  
where M\({}_{0}\) (g) was the biochar weight of silk-worm excrement before entering the muffle furnace, and M\({}_{1}\) (g) was the biochar weight of silk-worm excrement after charring.  
Equation (1) was used to calculate yield rate which is 40.14%. Equation (2) was used to calculate ash content which is 32.57%. Then BC was sieved to 0.25 mm to conduct general analyses. The basic properties of BC are shown in Table 2.