esa-sceva/satcom-chunk-collection huggingface dataset:
# Per-Pixel Manifold-Based Color Calibration Technique
## 3 Derivation of the Solution
\begin{bmatrix}RL^{1}\\ RL^{2}\\ \vdots\\ RL^{p}\\ \vdots\\ RL^{p}\end{bmatrix}  \tag{31}\]  
And \(\mathcal{L}\mathcal{L}\) can be defined as a block matrix composed of \(LL^{p}\)  
\[\mathcal{L}\mathcal{L}= \begin{bmatrix}LL^{1}&0&\cdots&0&\cdots&0\\ 0&LL^{2}&\cdots&0&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&LL^{p}&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&0&\cdots&LL^{p}\end{bmatrix}  \tag{32}\]  
Similarly, in order to estimate manifold \(A^{p}\) based on \(S_{2}\), we create a system of Equations (19) and (24) for pixel \(p\) and \(q\)  
\[ \begin{split}\frac{\partial S_{2}}{\partial a^{p}_{ij}}& =2\cdot\sum\limits_{k}a^{p}_{ik}\cdot l^{q}_{k}\cdot l^{q}_{j}-2\cdot \sum\limits_{k}a^{q}_{ik}\cdot l^{q}_{k}\cdot l^{q}_{j}=0\\ \frac{\partial S_{2}}{\partial a^{q}_{ij}}&=2\cdot\sum \limits_{k}a^{q}_{ik}\cdot l^{q}_{k}\cdot l^{q}_{j}-2\cdot\sum\limits_{k}a^{p}_ {ik}\cdot l^{q}_{k}\cdot l^{q}_{j}=0\end{split}  \tag{33}\]  
Using a definition of \(LL^{p}\) given in (29), we can rewrite Equation (33) as  
\[ \begin{bmatrix}LL^{o}&-LL^{o}\\ -LL^{o}&LL^{o}\end{bmatrix} \cdot \begin{bmatrix}AA^{p}\\ AA^{q}\end{bmatrix} = \begin{bmatrix}0\\ 0\end{bmatrix}  \tag{34}\]  
The exact formulation of \(\mathcal{L}\mathcal{L}\) matrix for all of the \(P\) pixels depends strongly on the relationship between the position of pixels \(p\) and \(q\).
# Impact of Environmental Factors on Stilbene Biosynthesis
## 2 Biosynthesis of Stilbenes and Stilbenoids
### Oligomerization  
Stilbenes are often accumulated in plants as oligomers (oligostilbenes) resulting from the oxidative coupling of stilbene monomers [157; 158]. Oligostilbenes have been isolated from species belonging to different plant families including Vitaceae [159], Fabaceae [160], Cyperaceae [161], Dipterocarpaceae [162], Gnetaceae [163], Paconiaceae [164], Iridaceae [165], and Moraceae [166]. The interest addressed to oligostilbenes in recent decades is linked to their important biological role in the plant as phytoalaxins [167], to their chemical diversity with more than 200 different molecules known to date [158], and to the wide spectrum of biological activities such as \(\beta\)-secretase inhibitory, anti-influenza virus, and anti-herpes simplex virus activities [168].  
The largest group of oligostilbenes is represented by resveratrol oligomers, arising from the polymerization of two to eight resveratrol units [169; 170] (Figure 8). Among these, the  
Figure 7: Major prenylated stilbenoids contained in peanuts.  
most investigated are viniferins, which accumulate in _V. vinifera_ upon abiotic stress (e.g., UV irradiation) or fungal infection (e.g., _Botrytis cinerea_ and _Plasmopara viticola_) [8, 120, 171].  
Resveratrol oligomerization has been achieved in vitro by enzymatic oxidation utilizing horseradish peroxidase [172, 173] or lacase-like stilbene oxidase from _B. cinerea_[174, 175]. It has been proposed that peroxididases (POD or POX EC 1.11.1.7) located in the plant cell wall and vacuole are responsible for oxidative polymerization of resveratrol to form its natural oligomers [157, 176]. POD enzymes use hydrogen peroxide to catalyze oxidative reactions and have already been exploited for the in vitro synthesis of oligostilbenes [173, 177, 178]. However, it should be noted that in vitro experiments with these enzymes did not lead to the formation of oligomers with the natural configuration found in plants, and to date, there is no direct evidence of the involvement of specific peroxididases in the formation of oligomeric stilbenes _in planta_[5].  
In recent years, considerable efforts have also been made to develop non-enzymatic methods to produce oligostilbenes [179, 180].
# Towards a Real-Time Description of the Ionosphere:
## 3 Measured and Modeled Data Used for Validation
### Observations from Space-Based COSMIC/FORMOSAT-3 Satellites  
Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC/FORMOSAT-3, hereafter COSMIC) was a six low-Earth-orbit microsatellites constellation launched on 15 April 2006. The mission was a collaborative project between the National Space Organization in Taiwan and the University Corporation for Atmospheric Research in the United States. COSMIC satellites were deployed into a circular orbit, with 72\({}^{\circ}\) of inclination, at about 800 km of altitude and a separation angle of 30\({}^{\circ}\) in longitude between neighboring satellites [61]. Each satellite carries a GPS RO receiver capable of measuring the phase delay of radio waves from GPS satellites as they are occulted by the Earth's atmosphere, thus providing an accurate determination of the ionospheric vertical electron density profile up to the COSMIC satellite altitude. COSMIC RO data were downloaded from the COSMIC Data Analysis and Archive Center (CDAAC, [[https://data.cosmic.ucar.edu/gnss-ro/cosmic1/](https://data.cosmic.ucar.edu/gnss-ro/cosmic1/)]([https://data.cosmic.ucar.edu/gnss-ro/cosmic1/](https://data.cosmic.ucar.edu/gnss-ro/cosmic1/)), accessed on 30 July 2021).  
Specifically, COSMIC retrieved _fo_F2 and _hm_F2 ionospheric characteristics from 22 April 2006 to 31 December 2018 were considered in this study. For this time range, a total of 3,626,729 COSMIC electron density profiles were available, and the most reliable ones were selected by applying the filtering procedure described in the \"Methods\" section of [PERSON] et al. [62]. As a consequence of such a filtering procedure, the COSMIC dataset that was used in this study for validation purposes was reduced to 1,791,602 profiles.
## 3 Results and Analysis
### DPRS Load Distribution Analysis
#### 3.2.4 Influence of the Pitch at Different Ambient Temperatures  
Figure 16 illustrates the influence of the pitch on the DPRS load distribution at different ambient temperatures. It can be seen that as the pitch increases, the load on the first few threaded teeth of the roller gradually increases, whereas that on the last few threaded teeth gradually decreases. Comparing the different temperature graphs, it can be seen that the influences of the pitch and ambient temperature on the DPRS load distribution are similar; however, as the ambient temperature increases, the difference in the load for the same threaded tooth decreases for different pitch cases.  
The results illustrate that the greater the pitch, the more uneven the load distribution of the DPRS, the higher the ambient temperature, and the smaller the variation in the thread tooth load due to different pitches.  
Figure 14: Load distribution of the DPRS at different ambient temperatures. (a) Screw–roller side; (b) Nut–roller side.  
Figure 16: _Cont._
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 2 Materials and Methods  
### Patients Selection  
This study was approved by the Ethics Committee of our institution, and the subjects gave their signed consent to photography and data processing for scientific purposes. Patients included in the study were adults without severe comorbidities admitted to the Plastic Surgery Clinic with a cutaneous defect greater than 3 cm\({}^{2}\) requiring closure with a flap, either local, pedicled, or free. Exclusion criteria were patients under the age of 18 and those whose cutaneous defect was closed either by direct suturing or skin grafting. Thermographic monitoring of the flap necessitates the presence of a skin island, which serves as an indicator of the flap's vascular status. Consequently, patients who had undergone a muscle or fascial flap procedure without a cutaneous surface island were also excluded from the study.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Localisation Performance  
The positioning error estimate is calculated as the distance between the estimated position and the actual position of the mobile station as obtained from the ray-tracer setup:  
\[e_{i}=\sqrt{[\left(x_{i}-\ \hat{x_{i}}\right)^{2}+\ \left(y_{i}-\ \hat{y_{i}} \right)^{2}]}, \tag{5}\]  
where \((x_{i},y_{i})\) are actual coordinates for the \(i^{th}\) MS taken from the ray-tracing tool, and \((\hat{x_{i}},\hat{y_{i}})\) are the corresponding LSSVM estimated MS coordinates. Performance for different scenarios, is compared using the location error cumulative distribution functions (CDF) plots.  
Figure 5: Outlier removal.
## 2 Review of Model Construction and Parameter Determination
### Range of Motion Constraints  
To prevent joint angles exceeding anatomical limits, passive restorative torques have commonly been applied at the extremes of the range of motion [120; 121; 10; 13]. The specific approach used has included passive elastic joint moments via an exponential model [120] in triple-jumping [10]. For passive restorative torques limiting shoulder flexion in the hand-spring somersault vault, [PERSON] et al. fit a similar function [122] to wrist and hip extension isovelocity data [121]. Finally, a viscoelastic spring has been used to incorporate elbow hyperextension in cricket fast bowling, with stiffness and damping parameters determined via an optimization algorithm to match recorded performances [13; 123]. Scaling parameters during model evaluation may be the most appropriate way to attempt parameter individualization, given the potentially injurious nature of experimentally recording passive elastic restorative torques. Nonetheless, range of motion constraints must be incorporated to avoid unrealistic optimal movement solutions. Methods in which restorative constraints ramp up (rather than employing hard limits) allow integration continuity within the iterative methods used to run the simulation models [124].
## 6 Discussion
The single application instance handling scenario does not require new capability on the device side, so it provides backward compatibility and fits for legacy/brownfield deployments. On the other hand, this option requires tight interworking of the TSN FRER and edge computing domain in order to hide the multiple application instances from the device. For the most efficient interworking, the TSN FRER functionality should be virtualized and moved into the cloud domain. Furthermore, the coordination of the selection of the application and TSN FRER instance that currently serves the end device, as well as the capability of seamless application instance change (e.g., in case of failure), is a must.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 5 Bicep Modules' Design
### Security Practices in Infrastructure Modules  
Security is a first-class concern in modern cloud deployments, and this Bicep design explicitly incorporates security considerations at multiple levels. Infrastructure as Code presents both opportunities and risks. While it enables the consistent and repeatable deployment of secure configurations, it can also codify insecure defaults if not carefully designed and implemented.  
A critical security consideration in this design is the management of secrets. The Bicep modules are designed to avoid storing secrets directly in code. Instead, parameters such as passwords are injected at deployment time, typically sourced from secure locations such as Azure Key Vault or pipeline secrets in GitHub Actions. This design ensures that sensitive values are never committed to source control, significantly reducing the risk of credential leakage.  
Role-based access control (RBAC) is another foundational security principle embodied in the design. By provisioning User-Assigned Managed Identities, the system enables Azure services to authenticate without secrets while supporting fine-grained access control through RBAC assignments. Managed Identities can be granted only the specific permissions they require; one example is access to Key Vault secrets or Cognitive Search indexes, enforcing the principle of least privilege. This modular RBAC configuration can be audited and modified independently, supporting continuous improvement of security posture [26].  
Networking security is also tightly integrated into the design using Private Endpoints and subnet isolation. By ensuring that critical services such as PostgreSQL and Cognitive Search are reachable only over Azure's private network, the architecture reduces the attack surface exposed to the public internet. The use of delegated subnets for Container Apps ensures that only authorized services can deploy into specific network segments, providing an additional layer of control and governance.  
Lastly, the modular structure itself supports secure development practices. By isolating concerns, security-sensitive modules such as identity provisioning or networking can be reviewed, approved, and maintained independently of application-level deployments. This separation enables organizations to enforce rigorous security standards without impeding development velocity for other teams.  
In summary, the Bicep modules' design exemplifies a structured, modular, and secure approach to programmable infrastructure. By adopting well-defined modules for networking, identity, database, application hosting, and search services, the solution promotes maintainability, reusability, and strong security guarantees. The design aligns with industry best practices for cloud architecture, supporting the deployment of production-ready,scalable, and secure infrastructure on Azure while enabling teams to adapt deployments to the needs of development, QA, and production environments [13; 27].
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)  
### Collecting the SNR and Weather Conditions  
We collected data on the SNR and weather conditions in the first stage. Figure 3 depicts an example of the SNR station in December 2019.
# Performance of 5G Trials for Industrial Automation
## 2 Industrial Use Cases and Requirements
### Reutlingen Trial Site  
There are two different 5G use-cases trialed at the Bosch semiconductor factory in Reutlingen, see Figure 1, as part of the 5G-SMART project. The cloud-based mobile robotics  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Cases** & \
\begin{tabular}{c} **Message Size** \\ **(Bytes)** \\ \end{tabular} &
\begin{tabular}{c} **Cycle Time and** \\ **Latency Bound (ms)** \\ \end{tabular} & **Comm. Service Availability (\# of Nines)** \\ \hline Robotics motion planning & 500 & 5–50 & 99.99\% \\ (Kista) & 500 & 4–10 & 99.9–99.999\% \\ Controller-to-controller (Reutlingen) & 500 & 10–100 & 99.999\% \\ Cloud-based mobile robotics (Reutlingen) & 1024 & 10 & 99.999\% \\ (Aachen) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Industrial use cases and their requirements.  
use case focuses on investigating the feasibility, flexibility, and performance of wirelessly controlled Automated Guided Vehicles (AGVs) that are connected to the 5G network. Decoupling the closed-loop control of the AGV from its embedded system and placing it into a factory cloud enables the physical platform and control intelligence to evolve separately, which contributes to the simpler hardware architecture of the robot device and longer operation time with a single battery charge. Moreover, it creates several operational benefits such as enabling enhanced route selection in real-time through a common map. This use case employs two types of AGVs: a commercial AGV and a prototype AGV realizing partial and full cloud-based control, respectively. In this context, the timely exchange of control messages between the AGVs and their control stacks over 5G becomes of great importance for the desired operation of the AGVs.  
The TSN/Industrial LAN over 5G use case, on the other hand, centers around investigating and validating the applicability of 5G for transporting the traffic for Time-Sensitive Networking (TSN)/industrial LAN (I-LAN) applications. Limited flexibility for setting up new production lines or for restructuring an existing production line as well as complex and costly maintenance are the major drawbacks of wired I-LAN realizations. This use case aims at evaluating the feasibility of partially replacing fixed interconnections between TSN/I-LAN nodes with 5G mobile communications. This will reduce wear and tear of the cables and connectors for the mobile machines/controllers, resulting in reduced maintenance costs. Furthermore, replacing the cables for communication between controllers and machines with 5G also means greater flexibility for the implementation and adaptation of the industrial manufacturing infrastructure, which can improve manufacturing productivity. The requirements of the use cases are shown in Table 1. Further details describing these use cases and their implementation status can be found in [8; 11].
## 4 FEM Verification and Analysis
### Validation Analysis of Electromagnetic Torque and Magnetic Levitation Force Models
#### 4.2.2 FEM Validation Analysis of Controlled Magnetic Levitation Force Models  
With zero radial displacement of the rotor and levitation current applied to the six-phase winding, the dynamic waveforms of the radial magnetic levitation force were observed. The amplitude of the levitation current was set to vary within the range of 0 A to 2 A with a step size of 0.5 A. Figure 8 shows the dynamic waveform of the controlled  
Figure 6: Torque waveform when the torque current changes.  
Figure 7: Curve of torque variation with torque current.  
radial magnetic levitation force with time (or rotor position angle), and Figure 9 shows the comparison between the FEM analysis results of the controllable radial magnetic levitation force and its model calculation results. Based on Figures 8 and 9, the following research results were obtained:  
(1) Under the unsaturated magnetic circuit condition, the controllable magnetic levitation force varies proportionally with the levitation current, and the FEM analysis results of the controllable magnetic levitation force are almost in perfect agreement with the model calculation value, with the maximum error between them not exceeding 0.25%. For example, when the levitation current is 1 A, the model-calculated value of the controllable magnetic levitation force is 28.865 N, while its FEM-analyzed value is 28.8302 N, with an error of about 0.12%.  
(2) The dynamic waveform of the controllable magnetic levitation force with time (or rotor position angle) shows certain fluctuation characteristics. However, with the increase in levitation current, the peak-to-peak fluctuation rate of the controllable magnetic levitation force is essentially unchanged. For example, when the levitation current is 1 A or 2 A, the maximum peak-to-peak fluctuation rate of the controllable magnetic levitation force is  
Figure 8: Dynamic waveform of radial magnetic levitation force with variation in the levitation current.  
Figure 9: Variation curve of radial magnetic levitation force with levitation current.  
almost the same (about 14.62%). The main reasons for the fluctuation of the controllable magnetic levitation force are twofold: the first is the influence of the harmonic magnetic field, and the second is that the structural parameters of the six-phase DSW-12/2-BPMSM still need to be optimized.  
(3) The dynamic fluctuation frequency of the controllable magnetic levitation force is relatively low. When the rotor rotates every mechanical cycle, the controllable magnetic levitation force fluctuates two times; the number of fluctuations is equal to the number of magnetic poles of the torque system. The actual radial displacement is controlled by a closed loop. Therefore, the slow fluctuation of the radial magnetic levitation force will not have a significant impact on the radial displacement control of the rotor.
# Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
## 3 EMR Public Sentiment Analysis System Design
### Data Collection Based on Topic Similarity
#### 3.1.1 Implementation of the Filtering Module  
Training a high-quality topic model is costly, and researchers in academia have proposed a variety of topic models to adapt to different scenarios. However, most of this work focuses on the modeling level, that is, designing reasonable models to fit various types of data. Resources and studies are scarce, guiding the implementation of topic models in industrial scenarios. Since online public sentiment analysis is an offline task and internet buzzwords emerge rapidly, frequent updates to the training dataset are essential to maintain the quality of semantic matching. This would also slow down the processing speed of the entire public sentiment analysis system. Therefore, it is not necessary to use a complex model that requires training. This study chooses to use the Baidu's Familia [26] open-source project to implement this module. This project directly trains and provides high-quality models to the community for direct use, which is quite convenient.  
The project includes a document topic inference tool, a semantic matching calculation tool, and three industrial-grade corpus-trained topic models: Latent Dirichlet Allocation (LDA), Sentence LDA, and Topical Word Embedding (TWE) [26]. The filtering module uses the semantic matching calculation tool to implement it. Within the project's provided APIs, semantic matching can be divided into three categories based on text length: short text-short text, short text-long text, and long text-long text [26] semantic matching. To calculate the similarity between keywords and the content of a post, short-long text matching is chosen. The model has currently been released in three versions, and this study uses the latest version--1.0.2.  
The goal of the filtering module is to calculate the semantic similarity between a specified keyword (query) and the main text of a post (content). Unlike other semantic models that directly map short texts to topics, the chosen model for the filtering module calculates similarity by determining the probability that a topic distribution of long text generates the short text [27]. The calculation is as follows. Here, \(q\) represents the query, \(c\) represents the content, \(w\) represents the words in \(c\), and \(z_{k}\) represents the \(k\)-th topic.  
\[Similarity(q,c)=\Pi_{weq}z_{k=1}^{K}=p(w\mid z_{k})p(z_{k}\mid c) \tag{1}\]  
After completing the semantic similarity calculation between the keyword and the text, the results are normalized. A threshold is set to filter out data with low similarity to the keyword. The size of the threshold can be adjusted according to the filtering requirements.
# Complex-Order Models: A System Identification Point of View
## 2 Operators of Complex Order
### Complex-Order Real-Valued Differintegral
#### 2.2.4 CO-RV Differintegral  
It would be useful to have a single flexible operator parameterized in such a way that its logarithmic slope in amplitude and phase may be adjusted independently. We can define such an operator easily by combining the integral and differential operators of the first and second type in an appropriate manner.  
**Definition 4**.: _Given \(\alpha,\beta\in\mathbb{R}\), the complex-order real-valued differintegral is defined as:_  
\[\mathfrak{D}^{\alpha,\beta}(s):=\begin{cases}^{1}\mathfrak{D}^{\alpha,\beta}( s)&\alpha\geq 0,\beta\geq 0\,\\ ^{2}\mathfrak{D}^{\alpha,-\beta}(s)&\alpha\geq 0,\beta<0\,\\ ^{1}\mathfrak{I}^{-\alpha,\beta}(s)&\alpha<0,\beta\geq 0\,\\ ^{2}\mathfrak{I}^{-\alpha,-\beta}(s)&\alpha<0,\beta<0\.\end{cases} \tag{20}\]  
The amplitude and phase frequency characteristics of this element are illustrated in Figure 3.  
Figure 3: Amplitude (**left**) and phase (**right**) frequency response of a complex-order differentiator with \(\alpha=0.5\) and \(\beta=\pm 0.1\).