esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
Determination of the Spatial Resolution in the Case of Imaging Magnetic Fields by Polarized Neutrons  
[PERSON] 1,\({}^{\circ}\) and [PERSON] 1,2  
1 Department of Mathematics, Physics & Chemistry, University of Applied Sciences,  
Berliner Hochschule fur Technik Berlin, 13353 Berlin, Germany; [EMAIL_ADDRESS]  
2 Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFE2), a Leibniz Institute,  
Chariteplatz 1, 10117 Berlin, Germany  
* Correspondence: [EMAIL_ADDRESS]  
* **Abstract** One of the most important parameters characterizing imaging systems (neutrons, X-rays, etc.) is their spatial resolution. In magnetic field imaging, the spatial resolution depends on the (magnetic) resolution of the depolarization of spin-polarized neutrons. This should be realized by different methods, but they all have in common that a spin-polarizing and spin-analyzing system is part of the resolution function. First a simple and useful method for determining the spatial resolution for unpolarized neutrons is presented, and then methods in the case of imaging with polarized neutrons. Spatial resolution in the case of polarized neutron imaging is fundamentally different from 'classical' spatial resolution. Because of \(\pi\)-periodicity, the shortest path along which a spin-flip can occur is a measure of'magnetic' spatial resolution. Conversely, the largest detectable magnetic field (B-field) within a given path length is also a measure of magnetic spatial resolution. This refers to the spatial resolution in the flight direction of the neutrons (\(\Delta x\)). The \(\Delta x\) and \(\Delta z\) refers to the spatial resolution in x- or z-direction; however, in these cases a different method must be used. Therefore, two independent methods are used to distinguish longitudinal and lateral spatial resolution, one method to determine the modulation transfer function (MTF) by recording the frequency-dependent fringe contrast of magnetic field images of a coil (longitudinal spatial resolution), and the second method, to observe the fringe displacement at the detector as a function of magnetic motion, provided that the accuracy of the motion is much better than the pixel size (at least half the pixel size) of the detector (lateral spatial resolution). The second method is a convolution of the fringe pattern with the pixel array of the detector.  
neutron imaging; polarized neutrons; magnetic fields; spatial resolution
## 2 Methodology
### Emotion Recognition Method Based on Sparse Auto-Encoder and Support Vector Machine
#### 2.2.2 Emotion Classification Method Based on Support Vector Machine  
We employed an SVM as the classifier to balance the performance and power consumption of the proposed method. SVMs are nonlinear models that have been widely used in various fields. Because SVMs use support vectors to determine the optimal hyperplane that maximizes the margin, the complexity of finding the hyperplane function is reduced and the generalization ability of the classifier is enhanced, especially with smaller datasets. SVMs have been proven to be effective models for detecting comfort levels and emotions [42,43].  
The SVM calculation steps are as follows:  
Input: \((\mathrm{x_{i}},\mathrm{y_{i}}),\mathrm{i}=1,2,\ldots,\mathrm{n}\)  
Given the nonlinearity of the RSP dataset samples, we introduced the radial basis function as a kernel function \(K\). This kernel function transforms nonlinear data samples in the dataset into linearly separable data samples as follows:  
\[\mathrm{K(x,x_{i})}=\exp\left(-\frac{\left\|\mathrm{x-x_{i}}\right\|^{2}}{2 \sigma^{2}}\right)\ (\sigma>0). \tag{8}\]  
Range of linear sample data: \(x\in R^{d},y\in\{+1,-1\}\). The results of the linear SVM are determined by the parameters \(\omega\) and \(b\), and the sign function \(sgn\), where \(\omega\) is the weight vector and \(b\) is the bias. This process was conducted as follows.  
1. An optimal solution problem for the objective function is constructed: \[\left\{\begin{matrix}\min\phi(\omega)=\frac{1}{2}\left\|\omega\right\|^{2}\\ \mathrm{s.t.:y_{i}}\left(\omega^{\mathrm{T}}\cdot\mathrm{x_{i}}+\mathrm{b} \right)\geq 1\ (\mathrm{i}=1,2,\ldots,\mathrm{n})\end{matrix}.\right.\] (9)
2. The function is optimized by adding the Lagrange multiplier parameter \(\alpha_{i}\), taking the form of: \[\left\{\begin{matrix}min\ L(\alpha)=\frac{1}{2}\sum_{i,j=1}^{N}\alpha_{i} \alpha_{j}y_{i}y_{j}\big{(}x_{i}\cdot x_{j}\big{)}-\sum_{i=1}^{N}\alpha_{i}\\ s.t.:\alpha_{i}\geq 0\ (i=1,2,\ldots,n)\\ \sum_{i=1}^{N}y_{i}\alpha_{i}=0\end{matrix}\right.\] (10)
3. The formula is simplified to obtain the SVM classifier: \[f(x)=sgn\Big{[}\sum_{i=1}^{N}\alpha_{i}y_{i}(x\cdot x_{i})+b\Big{]}.\] (11)
4. The final classifier is obtained by combining Equations (8) and (11): \[f(x)=sgn\Bigg{[}\sum_{i=1}^{N}\alpha_{i}y_{i}K(x\cdot x_{i})+b\Bigg{]}.\] (12)  
Because an SVM is a binary classifier, to validate our method and compare it with previous results, we used two SVMs to classify arousal and valence into two binary classes based on the assigned values. We chose a threshold value of 5, making the task a binary classification problem; that is, high/low valence and high/low arousal. In this study, to achieve the best classification results, we set the initial value of C (the regularization parameter) to 1 to avoid overfitting. The Gamma value was set to \(1/(number\ of\ features\times data\ variance)\).
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
## 4 Results and Discussion
### Dimensionless Temperature Contours at x/D = 5  
The overprediction of the centerline film cooling effectiveness by RANS was attributed to the lower level of mixing predicted between the main flow and the jet compared to the experimental data. Figure 4 illustrates the time-averaged dimensionless temperature contours at _x/D_ = 5 obtained experimentally [12], as well as by means of LES and RANS. In Figure 4c, the RANS results indicate that the region around the jet core barely mixes with the main flow, and the dimensionless temperature around point 0 representing the centerline is higher than the experimental results. This explains why the adiabatic centerline effectiveness was overpredicted by the RANS approach. In contrast, in Figure 4b, the dimensionless temperature of the core part obtained by LES is lower than that obtained in RANS, indicating greater mixing with the main flow. However, the mixing intensity in the LES contour is still lower than the experimental result.  
Figure 5 illustrates the phase-averaged dimensionless temperature contours at _x/D_ = 5 obtained experimentally [12], as well as by means of LES and RANS, under 32 Hz pulsation. The phase-averaged dimensionless temperature contours shown in Figure 5b were obtained by averaging 50 instantaneous LES contours to achieve bilateral symmetry. The change in the distributions was attributed to periodic changes in the blowing ratio, as given by Equation (2). The dimensionless temperature contours obtained by RANS were different from the experimental results, whereas the LES contours were quite similar to the experimental contours because the jet lift-off and various other vortexes, such as CRVP, generated due to jet injection were considerably better predicted by LES than RANS. At t/\(\tau\) = 0.4, the lift-off of the injectant observed in the experiment was reproduced by LES but was not predicted by RANS. At t/\(\tau\) = 0.6, a kidney-shaped temperature distribution was observed by the CRVP in the experiment. In LES, the kidney-shaped temperature distribution similar to that in the experiment was observed, but in RANS, a temperature peak occurred at _z/D_ = 0.
## 7 Performance Evaluations
### Results and Discussion
#### 7.2.3 Robustness against Free-Riding Attacks  
Our proposed time-dependent incentive mechanism aims to track the incremental contribution of each smartphone. Note that Algorithm 2 takes data valuation as the factor in model aggregation. With our design, contributors with low or zero data valuations will have minimal impact on the aggregated global model. We further illustrate the incremental valuation of honest contributors and attackers to show the superiority of our decentralized data valuation.  
Figure 10 shows that the QoS of classifying CIFAR-10 images is not changed with or without free-riding attacks. The resilience against free-riders is a desired security feature in collaborative edge intelligence, where fairness must be guaranteed.  
Figure 8: Similar performance with SOTA Curricula: Algorithm 1 and Curricula reach 90% test accuracy faster than Anti-Curricula.  
Figure 9: Reduced per-round training latency: our adaptive moving window is applied to both Algorithm 1 and Anti-Curricula to reduce overall training latency by about 30% during DML.  
Furthermore, Figure 11 shows the cumulative data valuation of honest contributors without an attack and with free-riders conducting an attack. As attackers do not contribute to the validation accuracy of honest contributors, the data valuation of attackers remains zero. In Figure 11, free-riders try to attack in every round. As the cumulative data valuation of honest contributors increases, it becomes increasingly difficult to mimic an honest contributor and achieve a successful attack. The observation and analysis jointly explain why the QoS of our system does not change under free-riding attacks.
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 2 WiMAX Physical Layer Model  
The Orthogonal Frequency Division Multiplexing (OFDM) constructs the WiMAX physical layers (OFDM) [3]. It is the primary goal of the physical layer to transform data frames from the top layers into a wireless communication format [4]. In order to increase capacity and performance, channel coding is utilized in the WiMAX transmitter [5].
## 1 Introduction
In this paper, to quantify the frequency regulation capability of the DFIGs integrated through the VSC-HVDC, the DPF model is improved with the cascaded droop control from the system frequency to the DC voltage and then to the active output of the DFIGs. For a more accurate frequency response, the governor delay is considered in the output of the SGs, the virtual inertia of the VSC-HVDC is set based on the estimation to the frequency nadir and constraint of the DC voltage. The inertia of the DFIG is corrected according to the changing kinetic energy. The numerical results show the feasibility of the proposed model, and validate the regulation effect and accuracy of the modified inertia of the DFIGs and the maximized virtual inertia of the VSC-HVDC.
## 2 Related Work  
### Subject-Specific Modeling Approach  
To address the inter-subject variability in ECG signals, researchers have proposed subject-specific model approaches that fine-tune a general model for each individual patient.  
Some studies utilize a constant portion of the ECG recording and its manual label from the test patient [11; 12; 13; 14; 15; 16]. For example, researchers use the first 5 min of the test patient's ECG data to fine-tune the general model [11; 12; 13]. [PERSON] et al. [11] proposed a subject-specific model focusing on the usage of a wearable device. A preliminary generic CNN was initially trained using a large database in an off-device training phase and then fine-tuned using 5 min ECG signals obtained from individual patients in an on-device training phase. [PERSON] et al. [12] transformed the initial 5 min ECG recording of the test patient into an identity vector, which was then injected into the intermediate layer of the general DNN model and fine-tuned by backpropagation. Instead of using the initial several minutes of the ECG recording, several studies select representative beats of the test patient, by employing the clustering technique [14] and active learning [15; 16]. Despite some improvements in performance, these approaches require additional manual labeling, which is cost-expensive and impractical in real-world scenarios.  
To avoid the need for expert labeling, recent studies have proposed automatic labeling methods [17; 18; 19; 20]. [PERSON] et al. [17] introduced the Personalized ECG Generative Adversarial Network (PGAN), which synthesizes ECG signals from an individual test patient. [PERSON] et al. [18; 19] utilized estimated patient-specific normal beats by calculating the correlation coefficient between the spectrograms of the adjacent beats. The high-correlation beats are estimated as normal beats and are included in the fine-tuning dataset. [PERSON] et al. [20] proposed a fusion model that combines a general model and a subject-specific model, where the subject-specific model utilizes multi-view learning to obtain high-confidence heartbeats from the subject's unlabeled initial 5 min ECG recording.  
Despite the improvements in performance achieved by these subject-specific models, there are still some limitations that need to be addressed. Training a separate model for each individual can be time-consuming, and the automatic labeling methods used to generate pseudo-labels may not be entirely reliable, with the performance varying depending on the selected signals.
## 5 Methodology for Anomaly Detection and Mitigation
### Mitigation
#### 5.2.3. AGV under Cyber-Attack  
This section discusses a set of high-level mitigations that would either directly address or help to prevent each of the previously described cyber-attack phases. The mitigation of _Delivery_ attempts to an AGV can be performed with strict enforcement of defined network traffic using a firewall, where any pattern outside a baseline is automatically blocked. However, if the attacker uses legitimate channels to deliver a malicious payload, e.g., over the air firmware updates, this could not be detected or mitigated based solely on packet filtering. Instead, the firewall would need to have application layer capabilities to inspect and validate all data going through such legitimate channels. In addition, proper input validation within the software of an AGV would block code injection attempts towards any exposed service, including unsigned firmware delivery. With regard to _Exploitation_,least-privilege should be enforced within the software and OS of AGVs, hard-coded user accounts should be disabled, and all default passwords must be changed. This would directly prevent the exploitation of user accounts for malicious purposes and contain the damage in case they are compromised. Application whitelisting would further help to contain damage by preventing the unauthorized _Installation_ and execution of software. However, the mitigation of malicious _Command and Control_ can prove difficult once the attacker has established a foothold in the AGV system. As a last defense, the control software of an AGV should be protected by strong authentication and access control. Depending on its sophistication, this software could further detect malicious instructions that could lead to an undesired or dangerous AGV state and block their execution. In summary, both the detection and mitigation possibilities outlined above can be automated to a certain degree, depending on the complexity of the actual systems as well as the availability of capable tools. Automation would further allow detection capacities to be paired with adequate response actions, enabling the prevention of AGVs being compromised in the first place. However, for an effective defense-in-depth approach, the AGV firmware is of critical importance and must constitute a solid and trusted computing base. Otherwise, all mitigations built on top of it would be severely affected in the case of being compromised.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 4 Scene Processing  
The core feature of our assistive maintenance framework consists of a scene understanding software that: (i) makes use of depth sensors to acquire 3D data from the environment; (ii) processes such data on an edge computing platform to detect and localize potentially dangerous objects; (iii) highlights these objects in real-time to the operator by projecting 3D holograms onto the HMD.  
The HMD adopted for deploying the system is Microsoft HoloLens. These AR glasses are equipped with a Time-of-Flight (ToF) sensor that constantly acquires depth information from its surroundings and progressively builds and updates a 3D representation of the environment, in the form of a triangular mesh. The on-board computational power is, however, not sufficient to perform real-time analysis on the recorded 3D data; therefore, the environmental mesh is transmitted over a 5G network to an MEC, where the whole processing task is carried out by a set of specifically designed algorithms (implemented in Unity). The output consists of a set of obstacle coordinates, which are sent back to the AR glasses and visualized on the integrated holographic display.  
Providing a formal definition for obstacles is no trivial task. Generally speaking, we may associate the concept of an obstacle to an object impeding or limiting the execution of a given action. In the specific case of on-field assisted maintenance, we chose to narrow our focus to isolated objects on the ground (or hanging from the ceiling) that stand in the operator's way and, if unnoticed, may hamper its movements or result in potential security threats. Given a 3D mesh of the environment, we call _obstacle_ any part of the mesh within a predefined size range that results disconnected from the scene once removed the floor (or ceiling) plane. The following paragraphs provide a detailed description of the algorithms developed to preprocess the environmental mesh, extract the ground-plane and localize the isolated components.
# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 2 Nutritional and Health Challenges of Edible Oils
### Tomato Seed Oil: Nutritional and Functional Profile  
The oil extracted from tomato seeds has a high content of unsaturated fatty acids and a wide range of antioxidant, anti-inflammatory, and cardioprotective molecules [67].  
Depending on the extraction technique, which ranges from cold and hot pressing to solvent-assisted methods, the oil yield can vary between 20% and 36%. From a thermodynamic and kinetic perspective, the principles of mass transfer govern the efficiency of oil recovery, including solute-solvent diffusion and equilibrium conditions that follow [PERSON]'s laws and partition coefficients.  
Tomato seed oil has a highly unsaturated lipid profile, with linoleic acid (omega-6) being the predominant component, followed by oleic acid (omega-9), while saturated fatty acids such as palmitic acid and stearic acid are present in smaller amounts. Equation (5) determines the Unsaturation Index (UI) for tomato seed oil:  
\[UI=\sum_{i=1}^{n}D_{i}\cdot X_{i} \tag{5}\]  
where \(\mathrm{D_{i}}\) represents the number of double bonds and \(X_{i}\) indicates the molar fraction of fatty acid \(i\). The typically high value of the molar fraction indicates that tomato seed oil contains excellent nutritional potential and a higher sensitivity to oxidative degradation.  
Moreover, tomato seed oil is a source of functional micronutrients and contains a significant amount of vitamin E (mainly in the form of \(\alpha\)-tocopherol), phytosterols (including \(\beta\)-sitosterol, campesterol, and stigmasterol), and carotenoids, such as lycopene and \(\beta\)-carotene.  
Table 3 reports both the absolute amounts and the corresponding contribution of tomato seed oil to the recommended daily intake (RDA or Adequate Intake, AI) for an average adult based on international nutritional guidelines.  
The presence of omega-3 fatty acids contributes to neural and visual function and may reduce the risk of chronic inflammatory diseases.  
Vitamin E preserves the integrity of cell membranes and modulates immune responses. The properties inherent in tomato seed oil allow its use in biomedical and industrial fields, especially in dermatology and cosmetics. Its balanced lipid profile, combined with anti-inflammatory and antioxidant compounds, makes it a suitable ingredient for skincare products aimed at improving hydration, reducing irritation, and promoting tissue repair.
# 3D Reconstruction of Cultural Heritage Sites as an Educational Approach. The Sanctuary of Delphi
## 4 Materials and Methods  
### Design and Implementation Process  
The creation of a virtual environment of an archaeological site for educational purposes presents some unique challenges. On one hand, the presentation of the landscape and environment should be as accurate as possible to reflect the feeling of being in the actual place. On the other hand, the environment might need to be enhanced with virtual reconstructions that are historically valid and accurate and have a strong educational role in the application. Inevitably, in most cases, the historical monuments have been partially or totally damaged, others have been stolen, broken, and others have been moved from their original location. Many of the important findings are found in museums, while natural and other disasters as well as human interventions with additions over time alter the image of the site. One must carefully balance between enhancing the site with representations of the past to strengthen the educational value of the application and avoiding adding imaginary content that is not historically proven. The latter case is commonly found in reconstructions of the past in games and movies that provide inaccurate and misleading information.  
Additionally, the design of an educational application must take into account the needs and preferences of the target group of learners. First of all, one must ensure that the learning content is appropriate for the students and their background knowledge. However, besides the content itself, its presentation and means of delivery is equally important. Therefore, the types of media that will be used, the order in which the various information will be presented, the association between the educational elements and the reconstructed space, and any challenges or playful elements that will enhance the environment need to be decided.  
To ensure that the above-mentioned considerations will be discussed and incorporated to the design of the application, we adopted a participatory design approach that included archeologists, educators, developers, and representative students. The methodology that we followed for the creation of the application is the following (Figure 1).  
First, we set up a multidisciplinary team with IT specialists, teachers, and representative students to determine the educational needs of the students, decide on the course material, and prepare the educational scenarios that will be supported by the application.  
Then, we set up the environment using digitized models and reconstructions of parts of the site, we produced the educational material and we created a first prototype (demo) of the application.  
After that, we set up an initial usability evaluation of the application with the involvement of the students, with the aim of improving and enriching the educational software.  
The final step was the deep use and the final (summative) evaluation of the educational software.  
Each step provided feedback on the previous one, improving and refining the design of the application.