esa-sceva/satcom-chunk-collection huggingface dataset:
# UnifiedFace: A Uniform Margin Loss Function for Face Recognition
## 2 Related Works
### Deep Face Recognition  
Significant advances in deep face recognition are due to the rapid development of deep convolutional neural networks [28; 29; 30]. Early studies consider open-set face recognition to be essentially a multi-classification problem [28; 29]. DeepID2 [31] elaborately designs convolutional neural networks(CNNs) and trains CNNs with softmax loss and contrast loss to achieve excellent performance. FaceNet [32] uses triplet loss for training and learns the mapping of face images to Euclidean space directly. Furthermore, it has achieved state-of-the-art results in large-scale face recognition tasks. Center loss [3] enhances the discriminative ability of deep learning features by penalizing the distance between deep features and their corresponding class centers and substantially improves the performance. Other methods based on contrastive loss and triplet loss, such as [33; 34], achieve excellent performance and illustrate the importance of metric learning in open set validation.  
Margin-based loss function is the main research direction of deep face recognition in recent years and has been widely popular due to its simplicity and effectiveness. L-softmax [4] introduces large angular margins in deep feature learning, and subsequent margin-based loss functions follow this design idea. SphereFace [5] introduces multiplicative margins to achieve tighter decision boundary and introduces weight normalization. To learn large angular margin face embedding features, [35; 36; 37] introduce feature normalization. SphereFace does not fully implement the multiplicative margin intuition within \([0,\pi]\) and its training process is unstable. Therefore, SphereFaceR [7] proposes a simpler and more efficient approach. SphereFaceR can apply multiplicative margins in any of the ranges \([0,\pi]\), and SphereFaceR v2 applies margins in non-target angle activation functions for the first time. CosFace [6; 8] and ArcFace [9] achieve impressive results by using additive margins instead of multiplicative margins. Specifically, CosFace adds the cosine margin penalty directly to the target logit, and ArcFace adds an angular margin penalty to supervise the network training. Although margin-based loss functions have achieved great success in the field of face recognition, intuitively, introducing both multiplicative and additive margins is more conducive to network training and obtaining superior performance. Other margin-based loss functions achieved significant performance, such as Circle loss [38], Adacos [39], AdaptiveFace [40], CurricularFace [41], and DiscFace [42].
## 1 Introduction
Thirdly, while SIF data has been widely proven effective in tracking GPP and crop growth observation, its potential for identifying specific crop growth stages remains unexplored [45; 46]. This study focuses on a specific agronomic observation site situated in the North China Plain, aiming to harness the potential of SIF data obtained from both ground-based measurements and satellite retrievals. With a specific emphasis on two key crops, summer maize and winter wheat, this research endeavors to achieve the following objectives: (1) Alignment of phenological characteristics: To establish a clear correspondence betweenthe time-series phenological characteristics derived from the remote sensing data and the actual growth stages observed on the ground; (2) SIF data for growth stage estimation: Assessing the capability of SIF data to effectively estimate critical crop growth stages within the framework of agronomy; (3) Comparison with vegetation indices: A comparative analysis to determine whether SIF data exhibits superior performance when compared to traditional VIs (EVIs) in accurately identifying and characterizing crop growth stages; (4) Exploration of acceptable portfolios: Exploring and identifying acceptable combinations of remote sensing time-series phenological estimation processes encompassing preprocessing, modeling, and transition characterization that prove effective in the precise identification of crop growth stages.
## 5 Discussion & Conclusions  
In this paper, an Autostereoscopic-Raman Spectrometry-based (ARS) measurement methodology and its associated measurement system is proposed and developed accordingly, which can perform 3D positioning, 3D measurement and identification of material composition structure of certain depth at the same time in volume, that is verified by the experimental studies in this paper. Moreover, the research of the proposed ARS system shows the potential abilities of detecting multilayer glass structures without scanning, accurately locate the spatial position of impurities, and conduct material analysis through the proposed ARS system. As a pilot study, the autostereoscopic module and Raman spectrum module are currently add-on modules for commercial microscopes without changing the original system structure. A 20\(\times\) objective that fixedly mounted on this commercial microscope was used to verify the performance of the proposed ARS system by testing the 3D positioning and identification capabilities in a volume that is constructed by a multiple-layer glass structure and three types of fibers. After Raman spectral signal correction, the average positioning error of the ARS system was sub-micron level, and the success rate of spectrum collection has been enhanced to 98%.  
The proposed ARS system has proved its high potentials for locating, tracing, measuring and identifying various sources of impurities to accurately control the industrial production process and improve the yield rate with low-cost and high-efficiency methods. It is interesting to note that this ARS system is able to have a larger field of view and depth of field which are obtained by quickly altering the objective magnification and matched micro lens array, enabling better measurement scale and efficiency for industrial scenarios. In this sense, this comprehensive measurement method will have a wider range of applications in the future.  
Conceptualization, [PERSON], X.Z. and D.L.; methodology, [PERSON], X.Z. and D.L.; software, J.W., Y.W. and W.W.; validation, J.W. and D.L.; formal analysis, J.W.; investigation, J.W., X.Z. and D.L.; resources, X.Z., B.W., X.X. and H.B.; data curation, J.W.; writing--original draft preparation, J.W. and D.L.; writing--review and editing, X.Z. and D.L.; visualization, J.W. and D.L.; supervision, X.Z. and D.L.; project administration, X.Z.; funding acquisition, X.Z. and W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the National Key R&D Program of China (2018 YFB0504400), Tianjin Natural Science Foundation (No. 19 JCZD)C36600), and the Tianjin Key R&D Program (No. 19 YFZCSY00250).  
Not applicable.  
The authors declare no conflict of interest.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 3 Results and Discussion
### XRD Analysis of the Biogenic IONPs  
The X-ray powder diffraction analysis was employed to characterize the crystalline structure and identify the phase of the nanoparticles. XRD analysis, as depicted in Figure 8, indicates that the synthesized nanoparticles possess distinct and sharp peaks. This observation suggests that the nanoparticles exhibit a high degree of crystallinity and closely align with the established reference values for the hexagonal rhombohedral \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) phase (JCPDS-33-0664). In addition, the synthesized nanoparticles exhibit characteristics that classify them as photocatalysts due to the presence of distinct and intense peaks at 20 values of 33.2 and 35.6\({}^{\circ}\). These peaks correspond to the preferential orientations along the (104) and (110) crystallographic planes, respectively [59]. The XRD pattern demonstrated the presence of ten diffraction peaks at two theta degrees of 24.52, 33.52, 35.62, 40.53, 49.44, 57.78, 62.74, 64.35, and 71.64\({}^{\circ}\), corresponding to the lattice planes of (012), (104), (110), (113), (024), (116), (018), (214), (300), and (010), respectively (Figure 7). These results were consistent with those of prior studies [59; 60; 61]. The crystalline grain size of the biogenic Fe\({}_{2}\)O\({}_{3}\) was detected according to [PERSON]'s formula as follows: L = K\(\lambda\)/\(\beta\). cos \(\theta\), whereas K is Scherrer's constant (0.94), \(\omega\) is the X-ray wavelength (for copper, \(\lambda\) = 1.5406 A), and \(\beta\) is the full width at the half-maximum (FWHM) of the most intense peak at 20 of 33.52\({}^{\circ}\), which was found to be 0.1824. The estimated crystalline size of the phytosynthesized IONPs was found to be 47.59 nm.
## 1 Introduction
Furthermore, the fabric-based strain sensors can provide better wearing comfort, thereby being more suitable for wearable sensing devices. At present, one of the most popular techniques for applying graphene to fabricate the fabric-based strain sensor is coating, including dip-coating, spray coating, and roller coating [25]. For example, based on dip-coated conductive graphene networks, fabric-based strain sensors were manufactured by using nylon/spandex knitted fabric [26], non-woven fabric [27], nylon/polyurethane fabric [28], and polyester knitted elastic bands [29]. In addition, multi-functional fabric sensors were fabricated by coating conductive networks of graphene, which are capable of measuring strain, pressure, and vibrations, and blocking ultraviolet (UV) [30; 31]. Although the high sensitivity and stretchability of the fabric-based sensors fabricated by coating have been achieved in the literature mentioned above, high linearity and stability are still challenging. For example, it is found that the resistance of a polyurethane (PU) fiber coated by Ag nanowires (AgNWs) at the zero strain experienced a substantial drift after cycling deformations, increasing 0.37 times with respect to its initial value after 50 cycles [32]. In principle, one of the main reasons arising the drift is the poor stability of the conductive network, especially the irreversible deconstruction of some conductive paths. As an approach to increase the stability, the combination of conductive filler materials of different dimensions was used [33]. For example, the stability of a PDMS-based strain sensor was much improved by hybridizing carbon nanofibers (CNFs) with graphene nanoplates (SNPs) [34]. In this study, with the expectation of enhancing the linearity and stability of the fabric-based strain sensor, we dip-coated the fabric with three conductive networks, i.e., one multi-wall carbo nanotubes (MWCNTs) network and two rGO networks. In particular, microfolded structures between the rGO networks were developed by stretching the fabric during dip-coating process. In addition,we also improved the durability of the strain sensor by strengthening the interactions between the conductive nanomaterials and the fabric surfaces via covalent immobilization [35]. The rest of the paper is organized as follows. Section 2 presents the materials and fabrication process, which is followed by sensor characterization and mechanical experiments in Section 3. Then, Section 4 gives some potential applications of the strain sensor in human physiological signals monitoring. Finally, the conclusions are drawn in Section 5.
## 2 Results
### Target Prediction of miRNAs in C. sativa  
The psRNATarget software was used to predict the target genes of the miRNAs in _C. sativa_. As a result, the 178 conserved and 365 _C. sativa_-specific miRNAs were found to potentially target 1822 and 3951 transcripts, respectively (Table S10). These transcripts were involved in multiple functions. Among these target transcripts, 2673 of them were annotated by the KEGG database. KEGG enrichment showed that 15, 25, 160, 271 and 29 transcripts were involved in cellular processes, environmental information processing, genetic information processing, metabolism and organismal systems, respectively, in which the top five enrichment pathways were RecQ-mediated genome instability protein 1, CCR4-NOT transcription complex subunit 4, KAS II, GPI ethanolamine phosphate transferase 3 subunit O and alpha-mannosidase (Figure 3). Then, we considered the target genes potentially involved in the biosynthesis of cannabinoids, FA and cellulose. As a result, a total of 21 miRNAs including four conserved families of miR156/miR477/miR5658 and 17 _C. sativa_-specific miRNAs including families of CsmiRNA-n28/n33/n34/n39/n57 were found to potentially target _AAE_, _DXR_, _DXS_, _HDR_ and _LOX_ structural genes in cannabinoid biosynthesis. MiR530 and CsmiRNA-n52 f targeted _ACCase_ structural genes in FA biosynthesis. Nine conserved miRNAs families including miR1508, miR171, miR172, miR482 and miR569 and 35 _C. sativa_-specific miRNAsincluding families of CsmiRNA-n1/n4/n15/n22/n23/n24/n25/n27/n32/n34/n37/n56 were found to potentially target _CESA_, COBRA-like protein, _CSL_, _FRA_, kinesin-like protein, UDP-galactose transporter and _VILLIN_ genes in the biosynthesis of cellulose (Table 1). The regulatory protein genes targeted by miRNAs were also investigated. Results showed that 218, 99 and 186 miRNAs potentially targeted 337 TFs, 148 TRs and 290 PKs, respectively (Table S11). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**miRNA Name** & **Target ID** & **Target Annotation** & **Expectation** & **Pathway** \\ \hline \multirow{8}{*}{CsmiRNA-n33j-1-3p} & T\_00033798 & 3 & 3 \\  & T\_00033799 & 3 & \\  & T\_00033800 & 3 & \\  & T\_00007721 & 3 & \\  & T\_00007722 & 3 & \\  & T\_00069673 & 3 & \\  & T\_00069674 & 3 & \\  & T\_00069676 & 3 & \\ \hline \multirow{8}{*}{miR85658} & T\_00030776 & AAE & 3 \\  & T\_00030777 & 3 & cannabinoids \\ \cline{1-1}  & T\_00030778 & 3 & \\ \cline{1-1}  & T\_00030779 & 3 & \\ \hline miR477a-5p & & 3 & \\ miR477b-5p & T\_00014806 & 3 & \\ miR477a-5p & & 3 & \\ miR477b-5p & T\_00014807 & 3 & \\ miR156e-3p & T\_00050643 & 1.5 & \\ \hline \hline CsmiRNA-n39d.1-3p & T\_00043759 & DXR & 3 \\ \cline{1-1}  & T\_00043759 & DXR & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MiRNAs target structural genes potentially involved in cannabinoids, fatty acid and cellulose biosynthesis. Figure 3: KEGG enrichment of miRNA targets.
## 4 Research on Image Acquisition and Theoretical Methods
### Scene 3D Reconstruction Study
With the known camera position and the existing sparse reconstruction model, MVS is used to perform the dense reconstruction to find the points with pixel consistency in the space, then to find the image consistency through the camera position, and finally to get the dense point cloud to generate a smooth, continuous and precise 3D model. The reconstructed 3D model consists of a myriad of 3D point coordinates, and the formation of a 3D point cloud is significantly different from the way a 2D image is formed. A 2D image is an optical phenomenon in which light from the environment is captured using a lens that produces an inverted image on a 2D image plane, which is then adjusted to form an orthographic image. In contrast, 3D point clouds are typically acquired using cameras, scanning instruments, sensors, and so on [51]. The main characteristics of 3D point clouds compared to traditional 2D image data are shown in Table 2.
# Space Habitat Data Centers--For Future Computing
###### Abstract  
Data from sensor-bearing satellites requires processing aboard terrestrial data centres that use water for cooling at the expense of high data-transfer latency. The reliance of terrestrial data centres on water increases their water footprint and limits the availability of water for other applications. Therefore, data centres with low data-transfer latency and reduced reliance on Earth's water resources are required. This paper proposes space habitat data centres (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by 11.9-33.6% and 46.7-77% on average, respectively.  
space habitats; data centers; computing platforms; asteroids +
Footnote †: journal: _Journal of the Royal Society Interface_
## 2 Numerical Model and Methods
### Boundary Conditions  
As presented in Figure 1b, the following boundary conditions were used to solve the above numerical equations:  
1. Inlet: mass flow inlet boundary. The injection total pressure \(P_{t,in}\) of 0.3 MPa and total temperature \(T_{t,in}\) of 430 K were fixed and remained constant in this study. The detailed injection principle and setup of this boundary can be found in [35].
2. Outlet: pressure outlet boundary. The back pressure of the outlet was 0.1 MPa.
3. Left and right sides: periodic boundary.
4. Ignition region: CJ ignition kernel. As mentioned above, an instantaneous CJ ignition kernel was used to initiate detonation in the combustor channel.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 3 Convergence Analysis and Problem Formulation  
To guide the design of effective power control strategies in the proposed multi-task Air-FL system, we develop a convergence analysis framework that explicitly accounts for the impact of aggregation errors. In this framework, we adopt the optimality gap as a key performance indicator, which quantifies the deviation between the current global loss function value and the corresponding optimal value for each federated task during the \(n\)-th communication round.
## 5 Discussion
(3) The ESTARFM and STDFA algorithms were proposed to enhance the temporal resolution of high spatial resolution data. High spatial and temporal remote sensing data can be generated using these methods. However, spectral fusion was not considered for these methods. Therefore, these methods can only be used for spatial and temporal fusion, and we recommend using the spectral fusion method in the future to improve the spectral resolution of sensors [50]. In addition, it has been proposed that these methods could be used to fuse optical images that are easily affected by cloudy weather. Although these methods can improve the proportion of useful data, a significant proportion of the data will be polluted by cloud. The development of an optical and radar data fusion algorithm is therefore an important direction for multi-source remote sensing data studies.
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 2 Related Works  
### Classical Localization Methods  
In TSA, at least three BSs were detected for estimating the MS location because of the constraint of hearbility. By linearizing the TOA equations based on the Taylor series algorithm, only the first two terms were retained to obtain the matrix relations [22]. The LLOP algorithm resolved the MS location by utilizing the linear equations derived from subtracting any two original range nonlinear equations [23]. In the literature [24], RSA, which was a very typical location technique and could estimate the true range in NLOS environments, was proposed. Many developments of the positioning algorithms focus on mitigating NLOS error, and RSA was a method with excellent performance. To decrease the NLOS error, this positioning problem is designed as a constrained nonlinear optimization problem according to the geometry relationship of the cell layout.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 2 Materials and Methods
### Loss Function  
To effectively guide the Differentiable SLIC during training, the FFN introduces three loss components: semantic-and-instance consistency loss, facet-centroid consistency loss, and facet regularity loss. They are combined as:  
\[L=\lambda_{s}L_{\text{sem-inst}}+\lambda_{c}L_{\text{centroid}}+\lambda_{r}L _{\text{regular}} \tag{13}\]  
where, \(\lambda_{s}\), \(\lambda_{c}\), and \(\lambda_{r}\) are weights balancing each term's effect on training.  
(1) Semantic-and-Instance Consistency Loss (\(L_{sem-inst}\)): Let the soft-association matrix from differentiable SLIC be \(\mathbf{A}\in\mathbb{R}^{N\times K}\), and let the ground-truth point-level one-hot labels be \(\mathbf{Y}\in\{0,1\}^{C\times N}\). First, facet-level predicted labels are obtained as follows:  
\[\mathbf{\hat{Y}}=\mathbf{A}^{T}\mathbf{Y}\in\mathbb{R}^{K\times C} \tag{14}\]  
Map facet-level predictions back to point-level predictions. The semantic/instance consistency loss is the cross-entropy between the predicted and ground-truth labels:  
\[L_{sem-inst}=-\sum_{i=1}^{N}\sum_{c=1}^{C}Y_{c,i}\ln(\widetilde{Y}_{c,i}) \tag{15}\]  
This term encourages points within the same facet to share the same semantic or instance labels, thereby improving facet purity.  
(2) facet-Centroid Consistency Loss (\(L_{centroid}\)): This term ensures that points in the same facet cluster around their geometric centroid. Let point \(i\)'s 3D coordinate be \(\mathbf{x}_{i}\in\mathbb{R}^{3}\), and the ground-truth centroid of facet \(j\) be \(\mathbf{m}_{j}\in\mathbb{R}^{3}\). Define:  
\[L_{centroid}=\sum_{j=1}^{K}\sum_{i=1}^{N}a_{ij}\left\|\mathbf{x}_{i}-\mathbf{ m}_{j}\right\|_{2}^{2} \tag{16}\]  
where, \(a_{ij}\) is the normalized weight from Differentiable SLIC. This loss penalizes large average distances between points and their facet centroids, promoting geometric compactness.  
(3) facet Regularity Loss (\(L_{regular}\)): This constraint encourages the soft labels from Differentiable SLIC to approach the polygonal shapes produced by classic (hard) SLIC. Let the hard-label matrix from classic SLIC be \(H\in\{0,1\}^{K\times N}\), where \(H_{j,i}=1\) if point \(i\) belongs to facet \(j\), otherwise 0. Define:  
\[L_{regular}=\sum_{j=1}^{K}\sum_{i=1}^{N}(a_{ij}-H_{j,i})^{2} \tag{17}\]This term drives the soft associations _aij_ toward the hard labels _aij_, making facet boundaries and overall shapes more regular--thereby improving approximation to true geometric segmentation.  
In summary, during training, the FFN uses Differentiable SLIC plus these loss constraints for end-to-end learning of a fixed number of facets. During testing, SPSLIC reproduces the learned segmentation, ensuring each facet contains at least one real point and the count remains \(K\). This design lays the foundation for subsequent applications--such as facet-abstracted downsampling and crop-organ segmentation--based on FFN outputs.