esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion
### Maps Convergence during Training  
Figure 11 presents some prediction maps associated with temporary models that were generated throughout training. The maps were computed from the first 60 frames of subject #1. The associated histograms are presented in bottom row.  
We can visually observe that the pixels labeled with pulse rates converged into relevant areas (i.e., forehead and cheeks) as the neural network learned, while pixels that contain no PPG information (blue pixels, e.g., from the background) were properly identified. In addition, the maps at the beginning of the learning procedure (Figure 11b,c contain disparate pulse rate values, resulting in high entropy histograms. After several epochs (Figure 11d,e), the entropy is lowered because the maps contain one or two prevailing pulse rate values. The histogram entropy, computed using [PERSON]'s formula, can here be assimilated to a confidence index: the lesser the entropy, the better the prediction.
## 5 Experimental Study  
To demonstrate the effectiveness of the method involving weakened integral sliding mode compensation combined with weight factors suggested by the simulation, an experiment was performed using the PMSM experimental platform, shown in Figure 12, with a 750 W SPMSM. The motor electrical parameters are listed in Table 1. The power supply used a 60 V DC voltage source, the control board and the driver board used 24 V voltage input, the DSP control chip selected was a TMS320F28335, the PWM frequency was 10 kHz, the frequency of the speed loop was 1 kHz, the M method was used to measure the motor speed, the dead time was 3.2 us, and the weight factors were set as \(x=y=0.5\). Figure 11: Comparison of \(dq\)-axis current simulations of two methods at 450 rpm with torque variation (1 Nm) under simultaneous resistance, inductance, and flux linkage mismatches (\(R_{0}~{}=~{}0.5R_{s}\), \(\psi_{f0}~{}=~{}0.5\psi_{f}\), and \(L_{0}~{}=~{}0.5L_{s}\)): (**a**) DPCC; (**b**) DPCC with weakened integral sliding mode compensation combined with weight factors. Figure 10: Comparison of \(dq\)-axis current simulations of two methods at 450 rpm with torque variation (1 Nm) under simultaneous resistance, inductance, and flux linkage mismatches (\(R_{0}~{}=~{}2R_{s}\), \(\psi_{f0}~{}=~{}2\psi_{f}\), and \(L_{0}~{}=~{}2L_{s}\)): (**a**) DPCC; (**b**) DPCC with weakened integral sliding mode compensation combined with weight factors. With the purpose of proving the effectiveness of introducing weight factors to suppress current fluctuation, the experiment was designed on the basis of the simulation. The DC voltage was set to 60 V, the rotational speed was set to 450 rpm, the load torque increased from 0.2 to 0.5 Nm at 0.5 s and then decreased from 0.5 to 0.2 Nm at 3.5 s, and the model inductance value was 1.4 times the stator inductance of the motor (\(L_{0}=1.4L_{s}\)). Figure 13a shows a large fluctuation in the \(dq\)-axis current in DPCC, which leads to system instability, while Figure 13b adds weight factors to DPCC, which can greatly reduce the current fluctuations, but which produce a certain amount of steady-state error. To effectively verify the feasibility of the anti-integral saturation of the weakened integral sliding mode, the current loop experiment was set up according to the simulation and the \(q\)-axis reference current was increased from 0 to 1.5 A at 0.05 s. Figures 14-16 indicate that the three parameters of the model were 1, 1.5, and 0.75 times the motor parameters, respectively, where current loops (a) and (b) were both optimized via DPCC using the sliding mode compensation and weight factors, and they differed only in their surface selection, with (a) using the ordinary integral sliding mode surface and (b) using the weakened integral sliding mode surface. As is shown in Figure 14, when there were no  
Figure 12: PMSM experimental platform. Figure 13: Experimental comparison of \(dq\)-axis currents of two methods at 450 rpm with torque variation (0.2 to 0.5 to 0.2 Nm) under inductance mismatch conditions (\(L_{0}=1.4L_{s}\)): (**a**) DPCC; (**b**) DPCC with weight factors.
# Reverse Time Migration Imaging Using SH Shear Wave Data
## 5 Conclusions  
The depth domain pre-stack migration practice was performed for P-wave and SH shear wave seismic data in the Sanhu area of the Qaidam Basin in Qinghai Province, including depth domain velocity model building, P-wave to SH shear wave velocity ratio estimation, and reverse time migration (RTM). The layer- and grid-based tomography methods were used to build the depth velocity model. Additionally, the pre-stack time migration results of the P-wave and SH shear wave were utilized to approximately calculate the distribution of the P-wave and SH shear wave velocity ratio in the study area, and the ratio was used to constrain the P-wave and SH shear wave velocity model building so that the reflectors of the P-wave and SH shear wave pre-stack depth migration results were matched in the depth domain. An eighth-order finite difference scheme was used for P-wave RTM on a variable grid, and a sixth-order CCD was used for SH shear wave RTM on a regular grid. The results showed that the SH shear wave results have obvious imaging advantages compared with the P-wave results in the gas-cloud region, which verified the accuracy of the SH shear wave RTM algorithm and demonstrated great potential in seismic imaging application.  
**Author Contributions:** Conceptualization, C.Z.; Formal analysis, J.Y.; Funding acquisition, X.L.; Investigation, C.Z., W.Y. and H.N.; Methodology, C.Z. and W.Y.; Project administration, J.Y. and X.L.; Resources, J.Y. and H.N.; Software, C.Z.; Supervision, X.L.; Validation, H.N.; Writing--original draft, C.Z. and W.Y.; Writing--review & editing, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The study is supported by the Science and Technology Research and Development Project of CNPC (2021 DJ3506) and (2021 ZG03) and R&D Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications, 2022 DQ0604-02).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Excavation Design Based on SSR and Stress Unloading Effect  
The change of geological conditions, rock strength or in situ stress level of surrounding rock will ultimately affect the bearing capacity. The existing codes and research generally describe the influence of stress on the stability of surrounding rock by strength-stress ratio (SSR), such as rock-burst classification, failure mode of surrounding rock and the layout design method of underground caverns [41; 42]. Therefore, the strength-stress ratio (SSR) is introduced to improve the blasting excavation design.  
SSR is defined as the proportion of rock strength to in situ stress. The formula for calculating the criterion is as follows:  
\[\text{SSR}=\frac{R_{b}}{\sigma_{\text{max}}} \tag{1}\]  
where \(R_{b}\) is the uniaxial compressive strength of the saturated rock; considering the most unfavorable situation, \(\sigma_{\text{max}}\) is the maximum principal stress of surrounding rock, which may be calculated by the depth from the ground surface multiplied by the average rock density.  
In the current design code (GB 50287-2016) [73], SSR and \(\sigma_{\text{max}}\) are used as the classification standard of initial in situ stress, as shown in Table 2. To a great extent, SSR can represent the rock bearing capacity of underground caverns. When the strength-stress ratio of surrounding rock is higher, the bearing capacity of surrounding rock is stronger, otherwise it is weaker. In the practical application of engineering, it is easily obtained and used.  
Forty-eight underground powerhouses are collected and listed in order of the SSR to introduce the bearing capacity of surrounding rock, as shown in Table 3.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 3 Framework
### Carrier Feature Block Attention
#### 3.2.2 Sparse Attention  
In comparison to channel attention, our proposed sparse attention module emphasizes the informative spatial locations \"where\" in a complementary manner.  
We perform spatial segmentation on \(F_{channel}\) by merging the first and second dimensions of the image features together, which effectively reduces the dimension, and obtain a feature vector \(F_{p}\in R^{HW\times C}\). Subsequently, we employ cosine similarity to assess the similarity between different spatial positions within the image, yielding a similarity matrix \(S_{s}\). \(S_{s}\) represents the pairwise similarities between various spatial positions. The calculation formula is presented below:  
\[S_{s}=\frac{F_{p}^{i}\cdot F_{p}^{j}}{\left\|F_{p}^{i}\right\|\times\left\|F_{ p}^{j}\right\|} \tag{4}\]  
where \(F_{p}^{i}\), \(F_{p}^{j}\), respectively, denote the feature vectors corresponding to the \(i\)-th and \(j\)-th spatial position within the image, with \(i,j\in[1,HW]\), and \(S_{s}\in R^{HW\times HW}\). Then, the feature \(F_{p}\) is subjected to matrix multiplication with the similarity matrix \(S_{s}\), resulting in the spatial attention vectors \(A_{s}\in R^{HW\times C}\). We obtain \(F_{sparse}\) by multiplying \(F_{channel}\) and \(A_{s}\), \(F_{sparse}\in R^{H\times W\times C}\).  
This module aims to enhance the attention given to various spatial positions of carriers in the image, which leads to improved recognition of the edge's spatial features, while potentially minimizing the impact of background.
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 6 Applications of Microbial Biopolymers
### Microbial Biopolymers as Coagulants and Flocculants
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymer** & **Pollutant** & **Acagortion** & **References** \\  & & **(mg.g-1)** & \\ \hline Cellulose/Poly(acrylic acid) & Methylene blue & 1492.99 & [296] \\ Chitosan/polyacrylate/graphene oxide & Methylene blue & 296.5 & \\  & FY3 & 280.3 & [297] \\ Chitosan/cellulose & Congo red & 380 & [298] \\ Chitosan/poly(in-vinyl-2-pyrrolidone) & Orange G & 63.7 & [299] \\ Xanthan gum/amandating composites & Methylene blue & 565 & [300] \\ Polyaniline/Xanthan Gum Nanocomposite & Methylene Blue & 22.52 & [301] \\ Xanthan gum/polyacrylic & Methyl violet & 1052.63 & [302] \\ acid/graphene oxide & Methylene Blue & 793.65 & \\ Pullulan-graft-poly(3-acrylamidopropyl trimethylammonium chloride) & Azocarmine B & 113.63 & [303] \\ microspheres & & & \\ Sodium alginate-polyaniline nanotube & Methyl orange & 370.4 & [304] \\ Sodium alginate/coffee waste & Acidine orange & 805.3 & [291] \\ Crosslinked chitosan films & SDRS & 714 & [305] \\ Nanofibrilated cellulose & Cationic surfactants & 462.28 & [306] \\ Hydrogel chitosan beads & SDS & 1300 & [307] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._tive, particularly attributed to its reduced cost and environmental impact, along with its enhanced safety for human well-being and efficiency in terms of time. It is a simplified method based on the dual functionality of cationic or anionic polymers, first neutralizing particle charges and then bridging their aggregation. Unlike coagulation, direct flocculation has demonstrated its effectiveness for high levels of organic contaminants across a large pH spectrum [315; 327]. Several factors and mechanisms are involved, ranging from the chemical structure, properties, and charge of both the particles to be removed and the flocculant to their concentration, the pH of the environment, temperature, mixing rate, ionic strength, and even the mechanism of the process, which can significantly influence the flocculation. Thus, despite the significant progress that has been made in understanding this process, ongoing research continues to further uncover its complexities [328].
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### Network Model
#### 2.1.1 Semantic Segmentation Network  
Image segmentation entails dividing an image into specific regions with unique properties and extracting targets of interest, which is a key step in the transition from image processing to image analysis. Typically, segmentation can be divided into semantic segmentation, instance segmentation and panoramic segmentation according to its purpose. The technique used in this paper is semantic segmentation, which is the classification and labelling of each pixel point in an image. As the segmentation result in Figure 1 shows, the cages in the image are labelled. The main task of this paper is to mark the breeding cages in the remote sensing images and calculate the breeding density of the cages through the statistics of the pixel points of the cages through semantic segmentation.  
Figure 1: An example of semantic segmentation: (**a**) original image; (**b**) semantic segmentation image.
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 1 Introduction
### Challenges  
While the approaches above have made progress, three core challenges persist. Firstly, a lack of quantitative heterogeneity modeling. Existing methods often adopt simplistic metrics, such as class imbalance or label distribution, failing to model statistical and structural heterogeneity in the feature space. This results in limited optimization guidance. Secondly, inadequate aggregation responsiveness. Mainstream aggregation algorithms such as Federated Averaging (FedAvg) apply static or sample size-based weighting schemes, overlooking the severity of client heterogeneity, which can lead to conflicting parameter updates and a degraded performance. Thirdly, strategy-distribution mismatch. Although regularization and personalization improve robustness, many methods inadequately integrate local adaptation with global learning. This issue is exacerbated under highly heterogeneous or dynamic conditions, where convergence instability and reduced generalization remain prevalent.  
To address these limitations, a heterogeneity-aware federated optimization framework is proposed. The contributions of this work are summarized as follows:  
* Heterogeneity Metric Design: A statistical heterogeneity measurement approach is developed by extracting the local mean and variance from latent feature distributions. A differentiable heterogeneity function is integrated into both training and aggregation to effectively quantify client-level discrepancies.
* Multi-Loss Optimization Mechanism: A composite loss function is formulated by combining cross-entropy loss with heterogeneity-aware loss, feature center align ment, and L2 regularization, thereby enhancing convergence and generalization in non-IID environments.
* Heterogeneity-Aware Weighted Aggregation: A dynamic aggregation strategy is implemented on the server to adjust client contributions based on heterogeneity levels, reducing the negative impact of high-variance clients and improving global model fidelity.
* Empirical Validation Across Non-IID Scenarios: Extensive experiments conducted on datasets such as CIFAR-10 under Dirichlet-based heterogeneity (alpha = 0.1 or 0.5) validate the superiority of the proposed method in terms of accuracy, convergence stability, and interpretability. The results indicate enhanced global performance, with client adaptability maintained.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 2 Inorganic Stabilization Materials  
During the past decade, the chemical stabilization and remediation of chromium and arsenic in contaminated soil have been extensively investigated. For example, the leaching behavior of chromium in contaminated soil stabilized using Portland cement and slag was studied by [PERSON] et al. [26]. A variety of remediation techniques for heavy metal contaminants in soils were reviewed by [PERSON] et al. [27]. In addition, the effectiveness of sulfate-reducing bacteria in the removal of chromium from acidic mine wastewater was investigated by [PERSON] et al. [28]. The stabilization materials of chromium and arsenic are shown in Figure 3. A range of stabilization materials were continuously developed and researched. These materials are predominantly designed to alter the existing forms of contaminants in soil through physicochemical interactions with Cr/As, resulting in reduced mobility, bioavailability, and toxicity of the target elements. Within the domain of chromium and arsenic pollution mitigation, inorganic stabilizing agents are recognized as pivotal components; they produce effects in chromium and arsenic contamination remediation through multifaceted mechanisms. These materials not only demonstrate excellent technical performance but also offer significant economic advantages. For example, [PERSON] et al. [29] utilized 150 kg of low-cost iron-based materials to treat 360 L of mine wastewater per hour. Additionally, zinc peroxide material can simultaneously remove chromium and arsenic, thereby further reducing processing costs [30]. Although graphene has a high production cost, its efficient adsorption performance and renewability make it cost-effective in treating pollutants [31].  
The principal categories of inorganic stabilization materials, including clay minerals, lime-based substances, phosphate compounds, and nanoscale metal oxides, are systematically examined in subsequent sections. A comprehensive analysis was conducted to  
Figure 3: Chromium and arsenic stabilization materials [32; 33; 34; 35].  
evaluate the stabilization mechanisms and treatment efficiencies of these materials for chromium and arsenic, along with their operational advantages and technical constraints in field applications. This critical assessment was undertaken to establish a robust theoretical framework and practical guidelines for chromium/arsenic contamination management, with the dual objectives of promoting fundamental research in environmental remediation technologies and addressing this pressing global ecological challenge.
## 1 Introduction
LPWANs can be categorized into two main groups: 3 GPP-supported technologies (e.g., EC-GSM-IoT, LTE-M, and NB-IoT) that work over the licensed spectrum and proprietary technologies (e.g., LoRa, SigFox, RPMA, Telensa) that work over an unlicensed spectrum [18; 19]. LoRa and NB-IoT are prominent technologies that have recently experienced fast growth. Thanks to these popular technologies developed to deliver wireless connectivity to wide geographical areas, large-scale IoT applications can be easily implemented in smart environments (home, city, factory, grid, and farm) [20; 21]. This study proposes an agricultural vehicle tracking application for smart farm management through LoRa and NB-IoT technologies by establishing an IoV system architecture. In the proposed IoV-assisted agricultural vehicle tracking application, LoRa and NB-IoT technologies have been employed to connect agricultural vehicles and machinery to a cloud server in two separate scenarios. Also, the flexible VTU hardware platform was prototyped for cloud-connected vehicle tracking among farmland borders. Considering the RF transceiver's specifications on reference VTU hardware, the coverage range of the LoRa and NB-IoT technologies was analyzed using the Hata path loss model for different antenna heights, environmental settings (urban, suburban, and rural), and obstacle features on the test field. According to the obtained results from theoretical analysis, thanks to sub-GHz radio benefits, both LoRa and NB-IoT connectivity provide satisfactory coverage for most of the practical agricultural vehicle tracking applications. In addition, if LTE infrastructure is present in the area of need, it has been shown that NB-IoT can provide wider coverage compared to LoRa connectivity. The remainder of the paper is organized as follows: Section 2 reviews the state-of-the-art academic studies on vehicle tracking applications based on LoRa and NB-IoT. Section 3 explains the LPWAN-based agricultural vehicle tracking application and describes using LoRa and NB-IoT technologies for wireless connectivity. Section 4 introduces the designed reference VTU hardware platforms for IoV connection. Section 5 presents the coverage analysis from different perspectives. Finally, Section 6 concludes the study and highlights the contributions presented in this research.
## 5 The Simulation Validation and Results Analysis
### Performance of the Algorithm in a Complex Scenario
#### 5.3.1 Three UAVs Tracking One Moving Target
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Base Station** & \(A_{1}\) & \(A_{2}\) & \(A_{3}\) \\ \hline Average estimation error for position (m) & 1.2454 & 6.2889 & 1.7638 & 4.0183 \\ Average estimation error for speed (m/s) & 0.2338 & 0.4282 & 0.2457 & 0.3552 \\ Average estimation error for direction (\({}^{\circ}\)) & 1.1365 & 1.1575 & 1.7017 & 1.4710 \\ Average transmission probability & - & 0.8485 & 0.4653 & 0.6953 \\ Average trace of the FIM & 72.4623 & 41.2674 & 57.2770 & 43.1731 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The average estimate errors, the average transmission probability, and the average trace of the FIM in Scenario 3. Figure 31: The track of FIM of \(A_{1}\), \(A_{2}\), \(A_{3}\) and base station (Scenario 3). Figure 32: The minimum distance between the UAVs during the whole simulation process (Scenario 3).
## 2 Materials
### Estimating the WUE Based on Remote Sensing Technology  
The previous studies applied the GPP and NPP as parameters of the carbon cycle [10,18,19,22]. These parameters have been widely used and applied to many studies as the amount of carbon produced from photosynthesis and the respiration generated from the stomata of the vegetation, respectively. This study introduced the new carbon cycle index to understand the change in vegetation due to climate change as a hydrological factor. The WUE is the amount of carbon included in 1 kg of water in the atmosphere and it can be used as an index representing the carbon and water cycles [19]. The WUE can be defined in many ways at an ecosystem scale. The three mainly used definitions include the GPP based WUE: GPP/ET, the NPP based WUE: NPP/ET, and the net ecosystem carbon production (NEP) based WUE: NEP/ET. The ET value can be replaced by the annual rainfall and the photochemical reflectance index to calculate the rainfall use efficiency (RUE, [44,45]) and light use efficiency (LUE, [12,27]), respectively. In this study, the first definition (GPP-based WUE) was primarily used to deal with our objectives, since the GPP can reflect the annual net carbon fixation in the plant biomass and also examined how the WUE is distributed nationally.
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 3 Experimental Results and Analysis
### Comparison of Three Deep Learning Networks  
The loss function and the accuracy of the proposed MobileNet-SSD surface defect detection algorithm on the test set (Figure 7) were compared to those of the VGGNet [28], an excellent detection network in 2014 ImageNet and MobileNet. The three algorithms were trained via migration learning and data enhancement. The training parameters and results are shown in Table 3.  
Figure 6: Image acquisition device. CCD = charge-coupled device. LED = light-emitting diode.  
Figure 7: Loss function and accuracy of the three detection networks. VGG = visual geometry group.  
In the training process, the detection networks were tested once after two hundred iterations of the training set. The loss function and accuracy in Table 2 were mean values obtained from 40 to 50 iterations of the test set. It is clear that the MobileNet-SSD detection algorithm achieved better accuracy than the other two networks with fewer network parameters.