esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 4 Face Subsystem
### Video Processing
#### 4.1.2 Face Detection  
Another fundamental step in this process is the face detection because failures in this step could be crucial for the correct development in other parts of the face diarization system. In our system, the face detector employed is a system of alignment and detection based on a deep neural network (DNN) which is called multi-task cascaded convolutional networks (MTCCN) [22]. In this part, we employ this implemented system as it is a proven and effective method for face detection, which is necessary to perform before continuing with the rest of the face verification pipeline. Furthermore, using this detector, we can store the bounding boxes created by the algorithm that correspond to the coordinates where a face is detected. This information is then employed in the tracking and identity assignment process.
# Attention-Based Personalized Compatibility Learning for Fashion Matching
## 3 Methodology
### Problem Formulation  
First, assume we have a set of users \(U=\{u_{1},u_{2},\cdots,u_{M}\}\), a set of tops \(T=\{t_{1},t_{2},\cdots,\)\(t_{N_{t}}\}\), and a set of bottoms \(B=\{b_{1},b_{2},\cdots,b_{N_{t}}\}\). Each user \(u_{m}\) is associated with a set which contains historical top-bottom pairs \(O_{m}=\{(t_{i_{i}}^{m},b_{j_{i}}^{m}),(t_{i_{i}^{m}}^{m},b_{j_{i}^{m}}),\cdots, (t_{i_{N_{m}}^{m}}^{m},b_{j_{N_{m}}^{m}})\}\), where \(i_{k}^{m}\in[1,2,\cdots,N_{t}]\) and \(j_{k}^{m}\in[1,2,\cdots,N_{b}]\) refer to the index of the top and bottom. Then, for each \(t_{i}(b_{j})\), we use \(v_{i}^{t}(v_{j}^{b})\in\mathbb{R}^{D_{v}}\) and \(t_{i}^{t}(t_{j}^{b})\in\mathbb{R}^{D_{t}}\) to represent its visual and textual features from different ConvNet modules. Next, we use \(\overset{\sim}{v_{i}^{t}(v_{j}^{b})}\in\mathbb{R}^{D_{v}}\) and \(\overset{\sim}{t_{i}^{t}(t_{j}^{b})}\in\mathbb{R}^{D_{t}}\) to indicate its visual and textual embeddings through different attention branches modules. \(D_{v}\) and \(D_{t}\) denote the dimensions of the corresponding embeddings.  
In this study, we aim to develop fashion compatibility embeddings for outfit recommendations by considering user preferences and employing an attention mechanism. Consistent with previous research ([PERSON] et al., 2018), we explore the challenge of determining \"which bottom would be preferred by the user to match the given top?\". Let \(e_{ij}^{m}\) denote the preference of the user \(u_{m}\) towards the bottom \(b_{j}\) for the given top \(t_{i}\), based on a generated personalized rating score list of bottoms \(b_{j}\)'s for a given top \(t_{i}\) and hence solve the practical problem of personalized outfit matching.  
To ensure accurate measurements of \(e_{ij}^{m}\), we have designed a personalized compatibility embedding modeling network F that incorporates an attention mechanism. This network can integrate users' preferences for visual and textual aspects of items into the compatibility embedding model. The mathematical expression for this model is as follows:  
\[e_{ij}^{m}=F(t_{i},b_{j},u_{m}|\theta_{F}) \tag{1}\]  
where \(\theta_{F}\) refers to the model parameters to be learned.
## 1 Introduction
Many studies are performed to counteract the disadvantage of high torque ripple and distorted air gap magnetic field distribution for designing the optimal rotor structure [21; 22; 23]. Depending on the emplacement of the electric motor for the electric traction system, the degree of complexity can vary greatly, along with the cost of production, efficiency at the system level, and even the choice of topology of the electric motor. Powertrain architecture can be classified in two categories: central drive and wheel hub drive. The central drive architecture is employed in most EVs available on the market, using an electric motor connected to the wheels by the means of reduction gears and a mechanical differential. The wheel hub drive category is another attractive drivetrain, where the wheels are directly driven by electric motors, improving the overall efficiency by eliminating the reduction gear. The current paper focuses on the full process of designing and integrating an electric motor in a light EV. The study begins with the evaluation of the most common solutions of integrating the electric motor in the kinematic chain, considering both the performances and the costs of those topologies. The next step was to determine the most advantageous PMSM structure, considering PM surface-mounted, interior PM, or spoke-type rotors. Based on these results, a PMSM was designed, modeled in FEA-based software, and optimized; a prototype was constructed. The motor parameters required for setting up a commercial inverter were determined using FEM simulations; and were experimentally validated. The test bench developed for testing the prototype is briefly presented in Section 3, along with the experimental results obtained for an operating scenario based on the WLTP drive cycle. Finally, the fully electric drivetrain, based on the spoke-rotor PMSM prototype, was integrated into a light vehicle chassis and tested in real-life conditions, validating the entire development process.
# Thermoelastic Investigation of Carbon-Fiber-Reinforced Composites Using a Drop-Weight Impact Test
## 4 Results and Discussion
### Summary  
The DWIT and nondestructive thermographic study results illustrate the convenience of using IR thermography to monitor impact tests. Useful information about a material's behavior can be derived quickly, at almost the same time as the test, without the cumbersome need for post-impact processing.  
The temperature versus time images and plots obtained in our study showed that heat is generated in the CFRP upon impact, and then dissipates over time. The coupled wave and heat equation was solved using a numerical method to understand the mechanism for heat generation. The results showed that the thermoelastic phenomenon is high-speed, dissipating in approximately 0.015 s, while heat diffusion occurred for nearly 1.2 s. The results for the heat-diffusion equation, which matched the experimental results captured using IR thermography, validated the findings. Our study found that the thermoelasticity (elastic waves) has a negligible effect, and that heat diffusion is responsible for heat dissipation in the CFRP. This indicates that the heat-diffusion equation entirely governed the heat-conduction mechanism of the CFRP upon impact.  
Overall, temperature changes in the CFRP plate upon impact began with a rise in temperature due to friction, followed by the appearance of a hotspot and heat flow towards the edges. The flow of heat obeyed the heat-diffusion equation and mathematical model in a two-dimensional cylindrical coordinate system, as the results agreed with the experimental findings.
# LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
### Research Challenges  
Analyzing real-world endpoint logs presents persistent challenges that limit the scalability, interpretability, and autonomy of current anomaly detection systems. We summarize the core problems motivating our work as follows:  
**(1) Analyzing Heterogeneous and Unstructured Logs (Q1)**  
Real-world endpoint logs are generated from diverse systems, vendors, and security tools--resulting in inconsistent field names, data schemas, and levels of verbosity. This heterogeneity poses a significant challenge for models like LogBERT [17] and DeepLog [15], which rely on stable token vocabularies or template-based parsing. [PERSON] et al. [19] demonstrate that such models are prone to semantic loss and generalization failure when applied to logs with unstructured formats or evolving schemas. Similarly, [PERSON] et al. [22] point out that these approaches require retraining or manual normalization pipelines to maintain performance in multi-source environments, undermining their practical scalability.  
**(2) Lack of Interpretability in Log Anomaly Detection (Q2)**  
Despite progress in anomaly detection, most existing models produce opaque outputs--such as binary labels or anomaly scores--without explanation. [PERSON] et al. [20] found in a large-scale practitioner study that over 80% of analysts hesitated to act on unexplained alerts, citing a lack of trust and interpretability. Compounding this issue, [PERSON] et al. [21] further observe that most models lack temporal or contextual awareness, treating each log sequence in isolation and failing to correlate events across time or different sources. This hinders real-world triage and investigation workflows, where understanding why something is anomalous is as critical as the detection itself.  
**(3) Limits of Static and Passive Inference Models (Q3)**  
Current AI-based detection models operate in a passive and static manner, performing inference in a single pass without external retrieval, multi-step reasoning, or goal-directed planning. As [PERSON] et al. [23] note, most LLM agents lack mechanisms to query external data or refine outputs during execution. Even frameworks like AutoGPT cannot verify plansor revise behavior mid-process, limiting their autonomy in investigative workflows [24]. Furthermore, [PERSON] et al. [25] report that multi-agent orchestration suffers from poor feedback control, making it difficult to scale analysis in dynamic environments requiring iterative, goal-driven reasoning. Similarly, recent LLM-based systems such as IDS-Agent and SERC rely on predefined tool pipelines and static prompt templates, offering limited flexibility for recursive adaptation or hypothesis refinement [26; 27].
## 14 Linearised Equations of Motion  
The next few steps of the derivation are not written out completely, they are straightforward and may be checked by an interested reader (or may be found in [7]). The first step is to substitute Equation (24) into Equations (16) and (12), so that the coefficients in the equation of motion, Equation (12), are expressed in terms of the known mass ratios \(\mu\) and \(\tau\). Next, both the equations of motion, Equations (2) and (12), are linearised by writing \(s(t)=s_{e}\Big{[}1+\frac{\Delta s}{s_{e}}(t)\Big{]}\) where \(\frac{\Delta s}{s_{e}}\) is assumed to be small, so that squares and higher powers of \(\frac{\Delta s}{s_{e}}\) in series expansions may be neglected. Using convenient short notations, the equations of motion transform into the following set of equations, Equations (25)-(28):  
\[\frac{dp}{dt}=\gamma\ddot{v}_{m}+q.E_{external}\ \text{with}\ p=m.\bigg{(}1-2 \frac{\Delta s}{s_{e}}\bigg{)}.v_{m}(t) \tag{25}\]  
The symbol \(m\) here stands for the constant equilibrium mass of the electron  
\[m=\frac{1}{2}\ m_{em}(s_{e})\]  
Note that the variable \(v_{m}(t)\) has not been linearised. The linearised pulsation equation reads:  
\[\frac{\tilde{\Delta}s}{s_{e}}+\ \omega_{0}{}^{2}\Big{(}1-2k\
u_{m}{}^{2} \Big{)}\frac{\Delta s}{s_{e}}=-k\omega_{0}{}^{2}v_{m}{}^{2} \tag{26}\]  
where:  
\[\omega_{0}{}^{2}=\frac{m}{m^{*}}\frac{c^{2}}{s_{e}{}^{2}}\mu\tau\ \text{with}\ m^{*}=\frac{1}{2}m_{em}^{*}(s_{e}) \tag{27}\]  
\[k=\frac{1}{c^{2}}\frac{1}{\mu\tau} \tag{28}\]In the case of a free motion without an external potential (\(E_{external}=0\)) these equations describe damped simultaneous vibrations of \(\frac{\Delta s}{s_{e}}\) and \(v_{m}\). The coefficient \(\gamma=\frac{2}{3}\ \frac{q^{2}}{4\pi\epsilon_{0}}\ \frac{1}{c^{3}}\) of the damping term (i.e., the radiation resistance) is very small (with \(c^{3}\) in the denominator), and we can get a first insight into the character of the vibrations by neglecting the damping. Physically this means that, coupled with the ultra high frequency periodic lengthening and shortening of the droplet (i.e., the periodic pulsation), the droplet moves with small jumps around a constant average velocity: \(v_{m}(t)=v_{av}+\Delta v(t)\), where the average velocity \(v_{av}\) is constant, and \(\Delta v(t)\) is periodic with the same frequency as the pulsation. The final step is to substitute the translation equation, Equation (25), into the pulsation equation, Equation (26), and to strictly adhere to the linearisations:  
\[\frac{\bar{\Delta}s}{s_{e}}+\ \omega_{0}{}^{2}\Big{(}1+2k.{v_{av}}^{2}\Big{)} \frac{\Delta s}{s_{e}}=-k\omega_{0}{}^{2}{v_{av}}^{2} \tag{29}\]  
Note the change of sign in the second term compared with Equation (26). The frequency of the pulsation and the velocity jumps, since \(k\) is small, is:  
\[\omega=\omega_{0}\Big{(}1+k.{v_{av}}^{2}\Big{)} \tag{30}\]  
with \(\omega_{0}\) and \(k\) defined by Equations (27) and (28).
## 4 Evolutionary Algorithms for Task Scheduling Problem
### Proposed Evolutionary Algorithm
#### 4.2.4 Genetic Operators  
_a. Crossover operator_  
With chromosome encoding as an array of integers, two-point crossover operation is used to generate offspring inheriting good genes from parents. This operation is shown in Figure 3, where two crossover points are randomly selected, the first parent exchanges the middle gene segment to the second parent, and the remaining genes are unchanged, forming a new individual.  
In the crossover process over the population, parental selection influences the efficiency of the algorithm. Each individual has a crossover rate of \(\alpha\). Everyone in the population is considered to be the first parent with the probability \(\alpha\), and then the roulette wheel technique is applied to select the second parent in the population to participate in crossover process. With this selection technique,high quality individuals with a larger fitness value have higher probability of being selected as parent, thus ensuring good gene segments are more likely preserved in the next generation.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### SEU Recovery Analysis  
This section analyzes the impact of SEUs on the EWS-16T cell and evaluates its inherent self-recovery capabilities. A node is classified as sensitive if it is vulnerable to state alteration due to SEU-induced transient pulses. It is widely known that SEUs induce negative transient pulses in NMOS transistors and positive transient pulses in PMOS transistors. Specifically, when a radiation particle strikes an NMOS transistor, it can cause a transient transition of either \"1\" \(\rightarrow\) \"0\" or \"0\" \(\rightarrow\) \"0\", depending on the node's logic value. In contrast, a PMOS transistor typically experiences \"1\" \(\rightarrow\) \"1\" or \"0\" \(\rightarrow\) \"1\" transitions [13].  
Therefore, a node storing logic '1' and surrounded by NMOS transistors, or a node storing logic '0' and surrounded by PMOS transistors, is considered to be SEU-sensitive. When node Q holds logic '1' in the EWS-16T cell, its complementary node QB stores logic '0' and is exclusively connected to NMOS transistors. This configuration permits only a \"0\" to \"0\" transition at QB, making it inherently immune to state flipping and thus classified as a non-sensitive node. Likewise, the internal node S1 retains logic '1' and is surrounded by PMOS transistors, allowing only a \"1\" to \"1\" transition. S1 is therefore also considered non-sensitive. Q holds logic '1' while being connected to NMOS transistors, and S0 stores In contrast, Q holds logic '1' and is connected to NMOS transistors. S0 stores logic '0' and is connected to PMOS transistors. This configuration satisfies the condition under which SEUs can induce logic inversion, thus classifying Q and S0 as sensitive nodes. The transient responses and recovery behaviors at these sensitive nodes are illustrated in Figure 4, demonstrating the EWS-16T cell's ability to self-recover from SEU-induced perturbations.
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## Appendix A Reconstructed Image Quality Evaluation  
As shown in Sections 4.1 and 4.2, CNN-based reconstruction works well for images taken on the ground. However, for images acquired in space, CNN reconstruction needs an extra denoising step and has a lower reconstruction quality compared to the deconvolution results.  
In this appendix, we study the impact of the gain level on the CNN reconstruction results. For this on-the-ground test, we shot a series of images with a Basler daA1600-60 uc with different gain values. Figure A1a,b show images with gain levels set at 1 and 18, respectively. An increase in gain leads to increased noise of the captured images (Figure A2b). CNN-based reconstruction results are demonstrated in Figure A2c,d. We can observe that for the lowest gain of 1, the reconstructed image is artifact-free, while for the high gain, the CNN output image has strong artifacts. These results indicate the importance of setting the gain value at a low level. Experimentally, we found that the highest gain value for our imaging sensor that did not lead to artifacts in the reconstructed images is 3.  
Some of the reconstruction artifacts can be addressed by modifying the neural network architecture, as was done for lossy compression [50]. However, the camera gain remains an unsolved problem, which we will treat as an adversarial attack [51] in the future to address it.  
We apply this approach to compensate for a dynamic range problem, as shown in Figure 11,c. In these images, a building wall directly lit by the sun is strongly highlighted by neural network reconstruction. We apply histogram normalization to the input image to retain the color scaling, as was done in a linear color correction algorithm proposed in [56].  
Figure 12 shows examples of neural network reconstruction with a pre-processed image. As shown in Figure 13, we shifted the input image (Figure 11) histogram to the  
left by 30 pixels. As a result, the wall became less lit. In Figure 10(b), we show a normalized to 0-150 range histogram range. The image shown in Figure 10(b) has a lower contrast, but the wall is not as strongly highlighted as in Figure 10(a).  
Figure 11 shows several examples of images captured by the onboard camera over European regions and their neural network reconstructions. Processed images show significant visual contrast and detail enhancements.  
Figure 11: _Cont._
# Silver Nanoparticles-Loaded Exfoliated Graphite and Its Anti-Bacterial Performance
## 3 Results and Discussion
### Pore Structure by Nitrogen Adsorption and Mercury Intrusion Porosimetry Measurement  
The N\({}_{2}\) adsorption-desorption isotherms of EG and Ag-EG (Figure 4a) shows that both of the hysteresis loops of EG and Ag-EG were of similar type, indicating slit pores and laminated structure. The BET surface area of Ag-EG was calculated to be 56.9 m\({}^{2}\cdot\)g\({}^{-1}\), with an increase of 36.5% in comparison to EG. The pore size distribution measured by mercury intrusion porosimetry (Figure 4b) shows that EG has abundant macropores with diameter from 20 to 200 \(\upmu\)m. After Ag loading, the total pore volume decreased from 40.33 mL-g\({}^{-1}\) of EG to 27.77 mL-g\({}^{-1}\) of Ag-EG. The N\({}_{2}\) adsorption-desorption isotherms and mercury intrusion porosimetry results indicated that the silver nanoparticles loading significantly decreased the porosity at the micron scales, but slightly increased the surface area of EG. Thus, the AgNPs-loaded EG still retained the excellent adsorption capacity of EG, which is useful for bacteria adsorption and inactivation.  
Figure 4: (**a**) N\({}_{2}\) adsorption-desorption isotherms; (**b**) the pore size distribution measured by mercury intrusion porosimetry measurement.  
Figure 3: Transmission electron microscopy (TEM) images of Ag nanoparticles on the EG sheets, (**a,b**) Ag nanoparticles coating on the EG sheets after exfoliated through ultrasonication; (**c**) High-resolution transmission electron microscopy (HRTEM) with fringe spacing; (**d**) is an enlarged image of fringe spacing.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 4 Issues in the Evolution of the Inter-Domain Routing Control Plane
### Backward Compatibility  
A new feature for the BGP has to have a minimum integration within heterogeneous networks and operate through different administrative domains (e.g., the Internet). Those requirements frustrated new proposals to evolve the BGP and the ecosystem of inter-domain routing [34]. One of the main reasons for this is new proposals that have to be maintained with the infrastructure already built, and ASs will not rely on immature technologies that do not prove to be profitable or are not operable with other ASs on which they depend [35].  
Thus, the difficult-to-change architecture of the Internet creates a mandatory requirement for integration among different proposals and the BGP. The backward compatibility imposes a limitation to \"clean-slate\" proposals. Brand new and incompatible BGP proposals are considered unrealistic [36]. In fact, in the more than two decades since the first version of the BGP protocol [37], much scientific and industrial effort have been made to overcome the BGP limitations and failed to make it a flexible protocol. The backward compatibility with the BGP for the new approaches in the inter-domain environment continues to be a mandatory requirement.  
However, a major criticism of the BGP is that it is not suitable for the next generation of inter-domain networks [35]. The main argument to sustain that claim is any new proposal for inter-domain routing has to be part of traditional networks to operate on the Internet and those proposals will be restricted by the limitations of the BGP.  
Moreover, the BGP is a widely-adopted protocol used in the infrastructure of the major part of the Internet. It is an inflexible protocol, and it is difficult to incorporate new changes into the network infrastructure. Therefore, the control plane of the inter-domain routing must be evolved instead of receiving a revolution. Evolving the Internet control plane is not an easy task because to be practical and useful, it is almost mandatory to have backward compatibility with the current technologies [19].