esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 3 Face Enrollment Models
### Training Process of Enrollment Models  
Motivated by the demonstrated effectiveness in training DNNs using aDCF loss function, in [13], we developed a straightforward and powerful back-end approach based on a network optimization with this loss function. This approach tries to mimic the target/nontarget process performed in verification tasks. In addition, this back-end takes advantage of the data learned during a previous training step of a general embedding network. Thus, this approach avoids the need for a careful selection of input data to train the models as required by other complex back-ends such as triplet neural network with triplet loss [11] or triplet neural network combined with aAUC [16; 17].  
Figure 1 shows the process to carry out this training, where a learnable vector is obtained to represent each enrollment identity. This process is based on comparing the positive or target examples with themselves (\(s_{tar}\)), and also with the negative or nontarget examples (\(s_{nontar}\)), using the aDCF loss function as training objective loss. To optimize aDCF loss, the scores used are obtained with cosine similarity as  
\[s_{\theta}(x_{i})=\frac{x_{i}\cdot w^{T}}{\|x_{i}\|\cdot\|w^{T}\|}, \tag{5}\]  
where \(\|x_{i}\|\) is the normalized input to the enrollment model, and \(\|w^{T}\|\) is the normalized layer parameters of the embedding obtained from the enrollment utterance.  
The philosophy of the approach followed in this work has been the same as the original approach used for speaker verification, but in this work, there are several differences. First, in our previous work, the final verification scores were obtained directly in the last step of the training process by comparing all target and nontarget samples with the trained vector, whereas in this work, the learnable vector is stored as an enrollment model to be used in the final step of the face subsystem to assign the identity to each segment. On the other hand, the nontarget examples employed in this system are directly the embeddings extracted  
Figure 1: Training face enrollment models using target and nontarget embeddings for each enroll or target identity.  
from the pretrained model. These nontarget examples belong to identities different to the enrollment identities, so we used them to train the enrollment models instead of using the weight matrix of the last layer of the trained neural network to obtain them as we did in [13]. Hence, in this work, the process for training the enrollment model for each identity is based on the following steps:  
1. The target and nontarget embeddings extracted from the pretrained model are employed as positive and negative examples.
2. Each enrollment model is trained using the aDCF loss function with all nontarget embeddings and only the target embeddings of the corresponding enrollment identity.
3. The trained models are stored to use them in the assignment process.
# Face Keypoint Detection Method Based on Blaze_ghost Network
## 4 Experiment and Result Analysis
### Result and Discussion
#### 4.3.3 Detection Performance Validation  
In order to visually check the effect of keypoint localization, the detection results of some facial images were output and displayed. The detection results under normal conditions are shown in Figure 3, which can intuitively show that the labeled keypoints can accurately depict the contour of the key facial area, demonstrating good performance.  
Due to the complexity and variability of the real driving environment, drivers may wear face coverings, such as glasses or masks, and there may be an uneven facial lighting distribution due to lighting environment effects. In addition, image blurring may occur due to issues with the acquisition equipment such as infrared cameras. These factors can all affect the effect of keypoint localization, as shown in Figures 4-6, which show the localization results of the improved model under different influencing factors. It can be seen intuitively from the detection result figures that the above factors did not cause too much interference in the detection results, indicating that the improved algorithm has good robustness.  
To verify the performance of our keypoint detection model in extracting fatigue features, we conducted experiments in a simple system that recognizes blinking and yawning behavior through mouth aspect ratio (MAR) and eye aspect ratio (EAR) calculation using face keypoints. We first used the retinaface [20] face detection network to locate and refine the target area of the portrait in the video image frames from coarse to fine to improve the localization efficiency and reduce the size of subsequent input images. Then, we used our proposed keypoint detection algorithm to extract keypoints and finally extract the fatigue features. Different faces were tested in the experiment, and the results  
Figure 4: Detection effect when the acquisition image is blurred.  
Figure 3: Effect of detection under normal conditions.  
are shown in Figure 7. It can be seen that under oblique facial conditions the model can fit the eye contour well and accurately reflect the opening and closing status of the eyes. When the driver yawns, the mouth keypoints can also fit well and accurately reflect the driver's mouth state. In summary, based on the results of different facial fatigue features, our proposed face keypoint detection algorithm can accurately detect and identify the face in the image, extract 68 keypoints of the face, and accurately extract fatigue features, such as blinking and yawning, indicating that our algorithm has high effectiveness and stability and can meet the requirements of fatigue feature extraction.
# Synergistic Effect and Phase Behavior of SCG-CAPB-H\({}_{2}\)O
## 3 Results and Discussion
### The Phase Behavior of SCG/CAPB System
#### 3.2.2 Phase Diagram of SCG/CAPB/H\({}_{2}\)O Ternary Compound System  
Based on the previous research results of this study, a phase diagram of the SCG/CAPB/H\({}_{2}\)O ternary system at 25 \({}^{\circ}\)C was constructed, as depicted in Figure 6. In Figure 6, S represents the solid phase, L\({}_{1}\) represents the micellar phase, L\({}_{\alpha}\) represents the lamellar phase, and H represents the hexagonal phase. It can be observed that the SCG/CAPB/H\({}_{2}\)O ternary system exhibits rich phase behavior, featuring one miscible phase, one micellar phase, two liquid crystal phases, and one solid phase.  
Due to the low temperature (25 \({}^{\circ}\)C), significantly below the Krafft point of SCG (36 \({}^{\circ}\)C), SCG exhibits low solubility and cannot completely dissolve in water. Therefore, undisolved surfactants swell in water, existing in the form of hydrated crystals in the aqueous solution. On the other hand, CAPB has higher solubility and can fully dissolve in water to form a micellar solution at low concentrations. A single SCG aqueous solution tends to form a micelle-solid phase, resembling a suspension macroscopically. In contrast, a single CAPB aqueous solution readily forms a micellar phase, and due to the anisotropic physical properties of micellar solutions, its clear and transparent solution state is easily observed.  
However, when the two solutions are mixed, diverse results are observed due to the varying relative content of SCG and CAPB. With increasing CAPB content in the samples, the less soluble solid surfactant gradually dissolves in the solution until a homogeneous micellar phase solution is formed. This is likely due to the electrostatic interaction between  
Figure 5: The SAXS results of different samples: (**a**) sample 2; (**b**) sample 3; (**c**) sample 4.  
Figure 6: Phase diagram of SCG/CAPB/H\({}_{2}\)O ternary system.  
the cationic part of CAPB and the anionic part of SCG, leading to mutual attraction between the head groups of the two molecules and facilitating the entry of SCG molecules into the gaps formed by CAPB micelles, thus increasing the solubility of SCG in water. Furthermore, the total mass fraction of surfactants also significantly affects the phase behavior of the solution. As the total mass fraction of surfactants gradually increases, the solution successively exhibits lamellar and hexagonal phases.
## Appendix A  
The rate equations for population densities are as follows:  
\[\frac{dN_{6}(z,t)}{dt}=-\sum_{i=0}^{5}r_{6i}N_{6}+R_{ESA_{1a}}+R_{ESA_{1b}}+R_ {ESA_{2}}+R_{2206}, \tag{1}\]  
\[\frac{dN_{5}(z,t)}{dt}=r_{65}N_{6}-\sum_{i=0}^{4}r_{5i}N_{5}+R_{4251}-R_{5031}, \tag{2}\]  
\[\frac{dN_{4}(z,t)}{dt}=\sum_{i=5}^{6}r_{i4}N_{i}-\sum_{i=0}^{3}r_{4i}N_{4}-R_ {ESA_{2}}+R_{VSA}-R_{SE_{2}}-R_{4251}, \tag{3}\]  
\[\frac{dN_{3}(z,t)}{dt}=\sum_{i=4}^{6}r_{i3}N_{i}-\sum_{i=0}^{2}r_{3i}N_{3}-R_ {ESA_{1b}}+R_{SE_{2}}+R_{1103}+R_{5301}, \tag{4}\]  
\[\frac{dN_{2}(z,t)}{dt}=\sum_{i=3}^{6}r_{i2}N_{i}-\sum_{i=0}^{1}r_{2i}N_{2}+R_ {GSA}-R_{ESA_{1a}}-R_{VSA}-R_{SE1}-2R_{2206}-R_{4251}, \tag{5}\]  
\[\frac{dN_{1}(z,t)}{dt}=\sum_{i=2}^{6}r_{i1}N_{i}-r_{10}N_{1}+R_{SE1}-2R_{1103 }+R_{4251}+R_{5031}, \tag{6}\]  
\[\frac{dN_{0}(z,t)}{dt}=\sum_{i=1}^{6}-R_{GSA}+R_{1103}+R_{2206}-R_{5031}, \tag{7}\]\[N(z,t)=\sum_{i=0}^{6}N_{i}(z,t), \tag{10}\]  
The photonic rate equations are given by:  
\[\pm\frac{dP_{976}^{\pm}(z,t)}{dz}=\pm\Gamma_{976}(G_{ESA1a}-G_{GSA} \ -\ G_{ESA1b})P_{976}^{\pm}(z,t)\pm R_{loss976}P_{976}^{\pm}(z,t), \tag{11}\]  
\[\pm\frac{dP_{1976}^{\pm}(z,t)}{dz}=\pm\Gamma_{1976}(G_{VSA}-G_{ESA2})\ P_{1976}^{\pm}(z,t)\ \pm R_{loss1976}P_{1976}^{\pm}(z,t), \tag{12}\]  
\[\pm\frac{dP_{I\!\!1}^{\pm}(z,t)}{dz}=\pm\Gamma_{I\!1}G_{SE1}P_{I\!\!1}^{\pm}(z,t)\pm R_{loss1}P_{I\!\!1}^{\pm}(z,t), \tag{13}\]  
\[\pm\frac{dP_{I\!\!2}^{\pm}(z,t)}{dz}=\pm\Gamma_{I\!2}G_{SE2}P_{I\!\!2}^{\pm}(z,t)\pm R_{loss2}P_{I\!\!2}^{\pm}(z,t), \tag{14}\]  
where \(P_{976}\) and \(P_{1976}\) are the powers of the 976 nm and 1976 nm pump. \(P_{I\!1}\) and \(P_{I\!2}\) are the powers of the 2800 nm and 3500 nm laser light. \(R_{loss}\) is the background loss coefficient. The boundary conditions are given by:  
\[P_{976}^{+}(0)=P_{976}^{-}(0)R_{976}+P_{976}, \tag{15}\]  
\[P_{1976}^{+}(0)=P_{1976}^{-}(0)R_{1976}+P_{1976}, \tag{16}\]  
\[P_{I\!1}^{+}(0)=P_{I\!1}^{-}(0)R_{I\!1}, \tag{17}\]  
\[P_{I\!2}^{+}(0)=P_{I\!2}^{-}(0)R_{I\!2}, \tag{18}\]  
\[P_{976}^{-}(L)=P_{976}^{+}(L)R^{\prime}{}_{976}, \tag{19}\]  
\[P_{1976}^{-}(L)=P_{1976}^{+}(L)R^{\prime}{}_{1976}, \tag{20}\]  
\[P_{I\!1}^{-}(L)=P_{I\!1}^{+}(L)R^{\prime}{}_{I\!1}, \tag{21}\]  
\[P_{I\!2}^{-}(L)=P_{I\!2}^{+}(L)R^{\prime}{}_{I\!2}, \tag{22}\]  
where \(R\) and \(R^{\prime}\) are the reflectivities of the laser cavity at the \(z=0\) and \(z=L\) positions. \(L\) is the length of the fiber.
## 4 Results of Experiment and Simulation
### Description of De-Coupling Effect  
In order to validate the decoupling ability of the proposed method, the azimuth gimbal is assigned to perform a sinusoidal motion with amplitude 0 in the first 48.8 s (Stage A); a sinusoidal motion \(1.0\times\sin(0.164\times\pi\times t)\) is applied in the following period (Stage B), as shown in Figure 8. The elevation gimbal is still assigned to perform a sinusoidal motion as in Section 4.1.
## 4 Experiments
### Experimental Results  
Experiments on LTA Anomaly were implemented on a CPU-equipped Windows machine with PyTorch. Calculation speed and results become better on a GPU with specific hardware. The window size of the log sequence and the batch_size were set to 15 and 128, respectively. With two layers of LSTM and two layers of Transformer, we set the initial learning rate to 0.0001 and trained the LTA Anomaly model for 20 epochs. LTA Anomaly was compared with six baseline methods with unsupervised learning: LogCluster [44], DeepLog [8], LogRobust [9], HitAnomaly [12], CL2 MLog [45], and LogLS [46].
## 5 An SOA-Based Platform Example and Case Study Results
### A High-Level Architecture Overview of an Example SOA-Based Platform  
Below, there is an example of how an SOA-based platform, also based on microservices, can be embodied in terms of overall architecture and its main components, and relations in between these. Figure 1 outlines the overall architecture of the Arrowhead Framework [8]. The overall architecture encompasses a small number of necessary core systems and services, including an authorization system (for users and services), a service registry (where services can be registered and looked up), and an orchestration system (in order to maintain system connections). Further, there is an application systems and services part, which comprises the actual business logic and provisioning, which differ for each context. In addition, the SOA principle is based on the foundation of the three Ls: Lookup (discover/set presence), Loosely coupled (autonomies and distributed components), and Late binding (dynamic system of system compositions) supported by strictly defined service contracts and architectural methods.  
A service can be a provider, consumer or both a provider and consumer. The service registry enables selection of the desired service from a range of many similar services. However, firstly, the orchestrator manages the system connection and ensures that there is an authorization to use the service wanted. If wanted, a minimum cybersecurity requirement baseline, such as authentication level and encryption algorithm with key length, can be part of the authorization scheme or else disqualify the service request connection.
## 5 Experiments
### Performance Comparsion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Models** & **Recall@5** & **Recall@10** & **NDCG@5** & **NDCG@10** & **MRR** \\ \hline NeuNext & 0.2114 & 0.2591 & 0.1494 & 0.1599 & 0.1501 \\ LSTPM & 0.2219 & 0.2709 & 0.1561 & 0.1749 & 0.1597 \\ LightGCN & 0.2226 & 0.2589 & 0.1969 & 0.2087 & 0.2006 \\ EEDN & 0.2548 & 0.3227 & 0.2219 & 0.2459 & 0.2277 \\ STAN & 0.2628 & 0.3324 & 0.2089 & 0.2195 & 0.2135 \\ GETNext & 0.2691 & 0.3263 & 0.2225 & 0.2253 & 0.2341 \\ CTRNext & 0.2699 & 0.3268 & 0.2331 & 0.2438 & 0.2456 \\ MSTHN & 0.2702 & 0.3324 & 0.2471 & 0.2542 & 0.2580 \\ SLS-REC & 0.3391 & 0.3638 & 0.2571 & 0.2726 & 0.2663 \\ STHGCN & 0.3398 & 0.3659 & 0.2588 & 0.2687 & 0.2671 \\
**MPHCL** & **0.3630** & **0.3925** & **0.2912** & **0.3028** & **0.2950** \\ \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with baselines on TKY dataset. Figure 6: Performance comparison of different methods on NYC dataset. such as spatial movement or local sequential patterns, and lack the capacity to coordinate multiple behavioral views. Our proposed MPHCL framework addresses these gaps by simultaneously modeling three distinct perspectives of user behavior through separate hypergraphs, thereby capturing a more comprehensive understanding of user intent. More importantly, MPHCL introduces a contrastive learning objective across these views, encouraging the alignment of user embeddings derived from different perspectives. This facilitates the learning of robust and coherent representations that are more adaptable to dynamic user behaviors. The final representations are integrated through a learnable aggregation mechanism that dynamically adjusts the contribution of each view, further enhancing personalization. Empirical results on two benchmark datasets, NYC and TKY, clearly demonstrate the superiority of MPHCL. On the NYC dataset, MPHCL achieves Recall@10 of 0.4786, NDCG@10 of 0.3965, and MRR of 0.3865, significantly outperforming the strongest baseline STHGCN, which achieves 0.4377, 0.3732, and 0.3709, respectively. A similar trend is observed on the TKY dataset, where MPHCL reaches Recall@10 of 0.3925 and MRR of 0.2950, again achieving state-of-the-art results across all metrics. Notably, while earlier methods stagnate around Recall@10 values in the mid-30s, MPHCL breaks through this ceiling, reaching values near 48 on the NYC dataset, illustrating a substantial leap in recommendation accuracy. Figure 8 shows the CPU usage comparison of different models. Specifically, our proposed MPHCL model demonstrates a relatively low CPU usage of 60% on the NYC dataset and 75% on the TKY dataset. These results indicate that MPHCL not only performs competitively in terms of prediction accuracy but also operates efficiently, making it suitable for real-time applications with mobile users who update their location data frequently.
# The Strain Heterogeneity and Microstructural Shear Bands in AZ31B Magnesium Alloy
## 3 Results  
### Macroscopic Mechanical Properties  
During the in situ tensile test, the experiment was paused at specific strain levels of 1%, 2%, 5%, and ultimately 12%, where the sample sustained damage. The true strain of the sample was loaded to 1% and then unloaded. After scanning the microstructure of the sample, the sample was re-loaded to 2% at the same rate, and then unloaded for scanning the microstructure. The previous steps were repeated successively to obtain the microstructure with true strains of 1%, 2%, 5%, and 12%. When the test was interrupted, the specimens all had plastic deformation, so there was no elastic rebound after unloading. The unloading section and elastic section of the discontinuity curve were removed, and the real stress-strain curve was obtained through data processing, as shown in Figure 5. Three points, a, b, and c, in the figure are discontinuity points, and the sample is broken at d. During the uniaxial tensile test, the strain field distribution on the RD-TD surface of the tensile specimen was obtained by using the DIC technique. The true strain of the sample was obtained via a strain field calculation. In order to directly reflect the microscopic evolution of the sample during the uniaxial tensile process, strain cloud maps in the TD direction with true strains of 1%, 2%, 5%, and 12% are also placed in Figure 5.  
The strain cloud map results show that after yielding, the strain on the specimen surface shows obvious non-uniformity, as shown at point a in Figure 5. With the increase in strain, obvious cross-shear bands were generated on the specimen surface, which showed an obvious 45\({}^{\circ}\) angle with the tensile direction, were dispersed in the direction and narrow in width, and evenly distributed on the specimen surface, as shown at point b in Figure 5. As the loading strain increased, these shear bands underwent stable expansion, effectively dispersing the strain concentration, as shown at point c in Figure 5. This dispersion significantly weakened the intensity of the strain concentration, fostering stable plastic deformation within the material [22]. However, as the strength of these shear bands intensified and shear bands from various directions intersected and traversed the specimen's surface, the material's integrity was compromised, ultimately leading to specimen failure.  
Figure 5: Macroscopic stress–strain curve and DIC test results.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 5 Experiments and Results
### Hardware-in-the-Loop Simulation  
In the above numerical simulation, the fixed-wing UAV is treated as a mass point, the flight constraints, such as the minimum turning angle and performance of the FCS, have not been considered. Therefore, we transfer the proposed algorithm to the HIL simulation on tracking ground targets in different motion states to verify the feasibility in high-fidelity experiments.  
Figure 14: Velocity estimation results. (**a**) X velocity estimation error versus time; (**b**) Y velocity estimation error versus time.
# A Comprehensive Survey on Knowledge-Defined Networking
## 3 Knowledge Plane
### Architecture of the Knowledge Plane  
The architecture of the knowledge plane containing the three knowledge sub-planes and its communication with the network [25] are graphically illustrated in Figure 3.  
As evident from Figure 3, there are three sub-planes in the knowledge plane, which are listed below:  
* Knowledge generation plane--Generates descriptive knowledge from raw data using a knowledge generation model;
* Knowledge composition plane--Identifies the relationship between different pieces of knowledge to compose knowledge using an ontology editor, which can be used to generate rules considering user intent;
* Knowledge distribution and management plane--Manages and exchanges raw data (within the knowledge plane), descriptive knowledge, rules, and control messages between the knowledge plane and other planes.  
The knowledge and rules generated from the knowledge plane can be helpful for fault diagnosis and mitigation, automatic reconfiguration, intrusion detection, etc. It is a requirement of the knowledge plane to operate successfully in the presence of limited and uncertain inputs [27]. The knowledge plane architecture given in Figure 3 is described in detail in the following sections.
# How Working Tasks Influence Biocontamination in an Animal Facility
## 2 Materials and Methods
### Inhalable Dust Sampling and Analysis of Endotoxin, (1,3)-\(\beta\)-p-glucan and Allergens  
Stationary inhalable dust samples were collected using airChek2000 pumps (SKC Inc., Eighty Four, PA, USA), at a sampling flow rate of 2 L-min\({}^{-1}\), equipped with stainless IOM sampler and glass (GF, pore size 1.6 \(\upmu\)m) and polycarbonate (PC, pore size 0.8 \(\upmu\)m) for respectively endotoxin and (1,3)-\(\beta\)-p-glucan analysis.  
Closed-face cassette with support pad and mixed cellulose ester (MCE, diameter 33 mm, 0.8 \(\upmu\)m porosity), at a flow rate of 2.5 L-min\({}^{-1}\), were used for allergen sampling. Samplers were placed in the middle of the room at 1.5 m above the floor to simulate human breathing zone. A field blank was deployed on each sampling. The sampling strategy was the same as environmental monitoring (three consecutive days in animal rooms, in the morning and in the afternoon inside the storage area). The sampling time was 3 h.  
GF extracts (5 mL of 0,05% Tween 20 in Pyrogen Free Water) were centrifuged at 1000\(\times\)_g_ rpm for 20 min. and analysed in duplicate with Kinetic LAL method (QCL-LAL assay Lonza Walkersville, MD, USA). Concentrations are reported in EU/m\({}^{3}\). The LOD was 0.005 EU/mL.  
For (1,3)-\(\beta\)-p-glucan, PC extracts (5 mL of 0.85% NaCl and 0.05% Tween 80) were vigorously shaken for 30\({}^{\prime}\) at room temperature (250 rpm) and analyzed with the (1,3)-\(\beta\)-p-glucan specific Kinetic Chromogenic LAL assay ([PERSON], Associates of Cape Cod), as previously described [(42; 43)]. (1,3)-\(\beta\)-p-glucan concentrations are presented in ng/m\({}^{3}\). The LOD in suspension was 2.53 pg/mL.  
MCE filters were extracted by submerging in 1 mL phosphate saline buffer (PBS plus 0.01% Tween 20) in a 15 mL centrifuge tube, vortexed at room temperature and shaken for 2 h. The eluate was collected by compressing the filter in a plastic syringe, 1% of human serum albumin was added for protein stabilization and stored at -20 \({}^{\circ}\)C until assay. ELISA kits for Mus m 1, Rat n 1, Can f 1, Fel d 1, Equ c 4 were performed following the protocols of analysis (Indoor Biotechnologies Ltd., Manchester, UK.) with read of the plates at 405 nm optical density. Results are expressed as ng/m\({}^{3}\). The uncertainty assessment results are reported in Table 2.  
(*) Type A standard uncertainty with a confidence level 1-\(\alpha\) > 0.95 using the Student's T to find the appropriate coverage factor based on degrees of freedom. (**) Measures are detected in the morning (without activity) and in the afternoon (during activity).