esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 2 Theory Background  
### Space Solar Power Station Model  
SSPS [31; 32] is based on space solar-power generation technology, which can provide constant and pollution-free energy. The SSPS mainly consists of three parts: solar power generation device, energy conversion and transmission device, ground receiving and conversion device. The space above the atmosphere is not affected by the weather, nor is it absorbed or scattered by the atmosphere. The solar energy received is far more than the ground, and the solar-power station can track the sun at any time on the orbit, and keep it in working condition.  
The array of solar cells that convert energy into electricity at a SSPS consists of thousands of solar cells. Solar cells are made of pure silicon element blocks. Due to the long service cycle of the SSPS, it is easy to cause damage to parts under the condition of high radiation and large temperature variation in space. Aiming at the maintenance problem of large SSPS, this paper proposes the task planning scheme of space-robot cluster.
## 2 Simulation Model Establishment  
### Finite Element Analysis  
This article utilizes the transient analysis module of Maxwell v.2022R2 software to conduct simulation calculations on the SICL device. By coupling the circuit-field method and finite element analysis method, the electromagnetic field equations of the coil in the vertical launching device are coupled with external control circuit equations for solving, reflecting the transient motion process of the launching system.  
In the transient solver module, the \(T-\Omega\) method is employed for solving [5; 7], describing electromagnetic transients through the following three Maxwell equations.  
\[\left\{\begin{array}{l}\
abla\times H=\sigma E\\ \
abla\times E=-\frac{\partial B}{\partial t}\\ \
abla\cdot B=0\end{array}\right. \tag{1}\]  
where \(E\) is the induced electric field intensity; \(B\) is the magnetic flux density; \(H\) is the magnetic field intensity; \(\sigma\) is the material's electrical conductivity; and \(t\) is time.  
Choose function \(S\) to satisfy  
\[S=-\int\phi dt \tag{2}\]  
Furthermore, let  
\[A^{\prime}=A-\
abla S=A+\int\
abla\phi dt \tag{3}\]  
\[\phi^{\prime}=\phi+\frac{\partial S}{\partial t}=0 \tag{4}\]  
In the Coulomb gauge \(\
abla\times A^{\prime}=0\), the governing equations for the electromagnetic field are expressed as:  
\[\left\{\begin{array}{l}\sigma^{\frac{\partial(A+\
abla\phi)}{\partial t}}+ \
abla\times\left(\mu\
abla\times A\right)=J_{s}\\ \
abla\left[\sigma^{\frac{\partial(A+\
abla\phi)}{\partial t}}-J_{s}\right]=0 \end{array}\right. \tag{5}\]  
In Equation (5), \(\phi\) represents scalar electric potential; \(A\) signifies vector magnetic potential; \(J_{s}\) represents current density; and \(\mu\) is the magnetic permeability. If we neglect the eddy current in the driving coil conductor region, the relationship between total conductor current and current density \(J_{s}\) is as follows:  
\[J_{s}=\frac{N_{c}}{S_{c}}i(t) \tag{6}\]  
In Equation (6), \(S_{c}\) represents the cross-sectional area of the winding and \(N_{c}\) stands for the number of turns in the coil.  
The electromagnetic field control equation obtained from the finite element analysis serves as the foundation for software solutions. By utilizing Maxwell software, it is possible to obtain curves depicting the variation of various parameters over time during the launching process, as well as the distribution patterns of the electromagnetic field.
## 3 Results
### Riemann Approximation EM: Two Applications
#### 3.1.2 Application in Two Dimensions  
The difficulty faced by Riemann methods in general is their geometric complexity when the dimension increases. In this section, we propose a similar experiment in two dimensions to show that the method is still functional and practical in that case.  
For this 2D-model, we consider two latent independent Beta random variables \(z_{1}{\sim}Beta(\alpha_{1},1)\) and \(z_{2}{\sim}Beta(\alpha_{2},1)\), and two observed variables defined as:  
\[x_{1} =\lambda_{1}z_{1}+z_{2}+\sigma_{1}\epsilon_{1}\] \[x_{2} =z_{1}+\lambda_{2}z_{2}+\sigma_{2}\epsilon_{2}\,,\]  
with \(\epsilon_{1}{\sim}\mathcal{N}(0,1)\), \(\epsilon_{2}{\sim}\mathcal{N}(0,1)\), and \((z_{1},z_{2},e_{1},e_{2})\) independent. The 2-dimension version of the Riemann E step with \(n\) intervals on each dimension is:  
\[\tilde{p}_{\theta,n}^{(i)}\bigg{(}\frac{k_{1}}{n},\frac{k_{2}}{n}\bigg{)}= \frac{h^{(i)}(\frac{k_{1}}{n},\frac{k_{2}}{n};\theta)}{\frac{1}{n^{2}}\sum_{l_ {1},l_{2}=0}^{n-1}h^{(i)}(\frac{l_{1}}{n},\frac{l_{2}}{n};\theta)}\,.\]  
As before, we define an operator \(\Psi^{(i)}:\mathbb{R}^{[0,1]^{2}}\longrightarrow\mathbb{R}\) such that, for any \(f:[0,1]^{2}\longrightarrow\mathbb{R}\):  
\[\Psi^{(i)}\circ f=\sum_{k_{1},k_{2}=0}^{n-1}\tilde{p}_{\theta,n}^{(i)}\bigg{(} \frac{k_{1}}{n},\frac{k_{2}}{n}\bigg{)}\int_{z_{1},z_{2}=k/n}^{(k+1)/n}f(z_{1},z_{2})dz\,.\]  
Then, the M step can be expressed as:  
\[\frac{1}{\hat{\alpha}_{1}} =-\frac{1}{N}\sum_{i=1}^{N}\Psi^{(i)}\circ\ln(z_{1})\,,\] \[\hat{\lambda}_{1} =\frac{\sum_{i}\Psi^{(i)}\circ(\frac{x_{1}^{(i)}z_{1}-z_{2}z_{1}} {n})}{\sum_{i}\Psi^{(i)}\circ z_{1}^{2}}\,,\] \[\hat{\sigma}_{1} =\frac{1}{N}\sum_{i=1}^{N}\Psi^{(i)}\circ\Big{(}x_{1}^{(i)}-\hat{ \lambda}_{1}z_{1}-z_{2}\Big{)}^{2}\,,\]  
with symmetric formulas for \(\hat{\alpha}_{2},\hat{\lambda}_{2}\) and \(\hat{\sigma}_{2}\).  
For the simulations, we take \((\alpha_{1},\alpha_{2})=(1,3)\), \((\lambda_{1},\lambda_{2})=(10,-10)\) and \((\sigma_{1},\sigma_{2})=(2,3)\). From the previous experiment, we keep only the two least costly profiles: \"low\" \(\varphi_{1}(n):=n+1\) and \"medium\" \(\varphi_{2}(n):=n+100\). We also consider two new, sub-linear, profiles \"square root\" \(\varphi_{5}(n):=\lfloor\sqrt{n}\rfloor+1\) and \"5 square root\" \(\varphi_{6}(n):=5\times\lfloor\sqrt{n}\rfloor\). \(\varphi_{5}(n)\) and \(\varphi_{6}(n)\) are designed to have linear complexity even in 2-dimensions.  
The results of the EM algorithm runs are displayed on Figure 3. On the left, we follow the number of Riemann squares mapping the 2D space. The difference in computational complexity between profiles is accentuated by the higher dimension. In particular, \"medium\" performs 6.7 times more computations than \"low\" and 18.4 times more than \"5 square root\". However, on the right of Figure 3, we observe that these three profiles perform similar optimisations. This observation justifies cutting computation costs by using lower resolution profiles to compensate the higher dimension.
## 5 Results and Analysis
### Multi-Constellation Scenarios  
This section discusses the results of the multi-constellation scenarios. First, the results of scenarios #2 and #3, which comprise the narrowband interference signal, are presented. Then, the results of scenarios #5 and #6 are discussed, which involve the wideband interference signal.  
Figure 6: Average carrier-to-noise ratio of GPS constellation scenarios. (**Top figure**)—narrowband interference; (**bottom figure**)—wideband interference.  
Figure 7: **Dilution of precision** (DOP) values of GPS constellation scenarios. (**Left figure**)—narrowband interference, (**right figure**)—wideband interference.
## 3 Methodology
### Stage 2: Redaction Decision with LLM  
The second stage employs a large language model (LLM) to analyze the combined text extracted by EasyOCR and Tesseract. The LLM makes intelligent redaction decisions based on PDPA guidelines. We utilize Qwen (Qwen/Qwen2.5-72B-Instruct-AWQ) [8] via its API, accessed using the 'litellm' library [24], leveraging its strong natural language understanding and instruction-following capabilities. The LLM is prompted to classify each extracted text segment as either \"personal data\" or \"not personal data\" according to simplified PDPA guidelines.  
It is important to note that in this prototype setup, these text segments are transmitted to the external LLM API. While standard security protocols (e.g., HTTPS) are typically used for such transmissions, deploying this system in a production environment handling sensitive data under PDPA would necessitate a thorough security review of the API provider's practices and potentially alternative deployment models, as discussed further in Section 7. If a text segment is classified as \"personal data\", the system applies black masking focusing on categories like names, addresses, and phone numbers. The prompt engineering process involved iterative refinement to optimize the LLM's accuracy and reliability in identifying personal data. If a text segment is classified as \"personal data\", the system applies black masking (redaction) to the corresponding bounding box in the original label image, effectively concealing the sensitive information. This LLM-driven approach allows for context-aware redaction, going beyond simple keyword matching and enabling more accurate and nuanced personal data protection. The deterministic nature of the redaction decision, driven by the LLM's classification, ensures consistent and predictable system behavior, crucial for compliance-focused applications.
## 3 Methodological Framework
### Embedding Layer
#### 3.2.2 FastText  
FastText is a conceptual framework (see Figure 2) in which word embeddings are regarded as the sum of their character-level embeddings rather than just the word itself, and may thus be applied to a variety of text problems. FastText is a Facebook-developed open-source framework that allows experts to learn text representations and classifiers for efficient text classification. This strategy is particularly adapted to distributing power through words made up of common roots encountered in training. Two terms, such as 'counter' and'revolution', can be combined to explain the less-common word 'counterrevolution', which is otherwise be too unusual to be learnt from a dictionary. Character-level embedding also supports learning to misspell jargon, such as 'pls', 'bcz', and so on, which may help to exchange the linkages between languages with a shared origin.  
FastText creates a sampling table to eliminate frequent words. The theoretical background behind this work is that frequently repeated words contain less information than uncommon words, and that their representation will not alter significantly after multiple instances of the exact phrase. Equation (1) is the mathematical representation of calculating the probability of a word using a frequency word:  
\[P(w)=\sqrt{\frac{t}{f(w)}}+\frac{t}{f(w)},\quad f(w)=\frac{\text{count}_{w}}{ \text{Total no of Tokens}} \tag{1}\]where \(t=10\times 10^{-4}\) and \(f(w)\) is the frequency of occurrence of word \(w\). The option is used to change the default threshold manually. The threshold value \(t\) has a meaning in fastText different from that in the original word2 vec, and should be adjusted accordingly. A word is eliminated if the chance of discard is more significant than a random selection from a uniform distribution between 0 and 1 during the training stage. This mechanism is only applied to unsupervised models; in a supervised model, the words are not discarded.
## 2 Materials and Methods  
### Study Area  
The study area, the Yangtze River Delta Urban Agglomeration (YRDUA), spans 214 county-level administrative units, including both municipal districts and those outside the districts, across 27 central cities in Shanghai, Jiangsu, Zhejiang, and Anhui. The two kinds of districts were distinguished based on the administrative type of the county-level units according to the government. Municipal districts refer to county-level administrative units that are directly governed by the municipal-level administrative authority, and outside districts denote all other county-level administrative units within the study area except municipal districts. Specifically, it comprises 16 counties in Shanghai, 68 in Jiangsu, 75 in Zhejiang, and 55 in Anhui, respectively (Figure 1). The study area covers approximately 222,400 km\({}^{2}\), with 114 municipal districts accounting for 35.22% of this area (brown in Figure 1b). Although it represents only 2.1% of China's total land area, the YRDUA is a significant economic hub, contributing about a quarter of the nation's total economic output and more than a quarter of its industrial added value. The economic vitality comes with high carbon emission intensity and the complex pressure of rapid urbanization on the ecosystem.  
To address urgent CS demands, the YRDUA is committed to establishing a National Forest Urban Group. This initiative, along with ongoing efforts in afforestation, greening, ecological protection, and restoration, aims to continuously enhance the urban ecological spatial arrangement. Marking a significant stride since the 13 th Five-Year Plan, the central government has dedicated substantial resources to these efforts. An investment of RMB 453 million was allocated to afforestation activities, culminating in the greening of approximately 876 km\({}^{2}\) in the YRDUA. Moreover, the Yangtze River and coastal protection forest project have been bolstered with an allocation of over RMB 442 million, facilitating the establishment of around 1305 km\({}^{2}\) of protective forests.  
It should be noted that 17 counties were excluded from the final modeling analysis due to significant changes in their administrative boundaries between 2001 and 2020; for the remaining data, the analysis included as many counties as possible.
# A New Type Bionic Foldable Wing Design for High Maneuverable Unmanned Aerial Vehicle
## 2 Preliminary Design of the Folding Wing Structure
### Dynamics Model of Fold-Wing Aircraft  
#### 2.1.1 Coordinate System Establishment  
In the analysis of aircraft dynamics, traditional 6-DOF aircraft dynamics modeling is commonly used. However, foldable wing aircraft is a multi-body system with spatial changes, and the dynamics equation needs to be derived again [20]. When the commonly prescribed \(\mathrm{O}x_{\mathrm{i}}y_{\mathrm{i}}x_{\mathrm{i}}\) coordinate system was used as the ground coordinate system, the \(\mathrm{z}_{\mathrm{i}}\) axis was perpendicular to the center of the Earth, and the \(\mathrm{x}_{\mathrm{i}}\) and \(\mathrm{y}_{\mathrm{i}}\) axes were in line with the righthand coordinate system in the horizontal plane. \(\mathrm{O}x_{\mathrm{b}}y_{\mathrm{b}}x_{\mathrm{b}}\) is taken as the body coordinate system, in which \(x_{\mathrm{b}}\) axis is toward the nose, \(y_{\mathrm{b}}\) axis is toward the right of the body, and \(x_{\mathrm{b}}\) axis is toward the bottom of the body. As the wing folding angle changes and moves in space, the system will generate additional forces and additional torques.  
In this paper, the origin of the fuselage coordinate system is chosen as the center of mass of the fuselage, which can avoid the extra additional moment term in the equation and the constant change in the coordinate system caused by the change in the center of mass in the process of wing folding, making the modeling more intuitive and concise. The mass of the foldable wing is concentrated at its center of mass. As shown in Figure 2, \(\mathbf{r}_{o}\) is the vector from the ground coordinate system to the body's center of mass. \(\mathbf{\rho}_{i}\) is the vector from the origin of the body's center of mass to the center of mass of the foldable wing. Subscript f indicates the fuselage, subscript 1 indicates the left foldable wing, and subscript 2 indicates the right foldable wing. Ri is the vector from the origin of the ground coordinate system to the center of mass of the folded wing.  
Figure 2: Coordinate system diagram.  
The vector relationship between the fuselage and the wing is seen in Equation (1).  
\[\left\{\begin{array}{c}\mathbf{v_{o}}=\mathbf{v_{f}}=\frac{\mathrm{d}\mathbf{r_{ o}}}{\mathrm{d}\mathbf{t}}\\ \mathbf{v_{\rho_{i}}}=\frac{\mathrm{d}\mathbf{\rho_{i}}}{\mathrm{d}\mathbf{t}}= \frac{\mathrm{d}\mathbf{r_{i}}}{\mathrm{d}\mathbf{t}}-\frac{\mathrm{d}\mathbf{r _{o}}}{\mathrm{d}\mathbf{t}}=\mathbf{v_{i}}-\mathbf{v_{o}}\end{array}\right. \tag{1}\]
# Comparison of Single Control Loop Performance Monitoring Methods
## 1 Introduction
In addition, a method for control performance monitoring is presented, namely, adopting the ideas from the framework of Overall Equipment Efficiency (OEE) to this new context, the OCE (Overall Controller Efficiency) method. OEE is one well-known utilization-based metric to measure productivity and efficiency. Other acknowledged metrics include total preventive maintenance, lean, SS, and the virtual factory [9]. In monitoring, OEE can be efficiently used to identify the underlying production losses to systemically establish process performance improvements [10]. It has also been applied as one possible indicator for measuring the impact of maintenance practices on sustainability performance (overall sustainability score) in [11]. The comparative study in this paper is conducted with a validated simulator representing a sub-process in a supercritical fluid extraction system. A single control loop is isolated and single-input single-output control performance is evaluated dynamically with the demonstrated methods. Several simulation scenarios are created to deteriorate the system behavior from the nominal control performance and thus illustrate the performance of the CPM methods. With the simulated process data, a comparison of the ability to identify faults and the robustness of the CPM methods is performed. The structure of this article is as follows: Section 2 describes the considered CPM methods, the simulated process, and the simulation scenarios of the faulty control. Section 3 presents the obtained results from the CPM methods' application on the simulated dataset. In Section 4, the results are further discussed. Finally, Section 5 concludes this study.
###### Abstract
In 2021, [PERSON] et al. reported a Fabry-Perot sensor coated multimode fibre as a smaller alternative to fibre bundles for endoscopic PACT [18]. The interrogation laser was raster-scanned via wavefront shaping through the multimode fibre for the detection of ultrasound. Optical micro-resonator sensors were also integrated onto the tip of a single-mode fibre as sensitive and broadband miniature ultrasound sensors [25; 26] and combined with multimode fibres for developing ultrathin PAE probes. Light transmission through the multimode fibre was controlled via the wavefront shaping technology [22; 24; 27], which enabled raster-scanning of a tightly focused laser beam in front of the fibre tip for an optical-resolution photoacoustic microscopy (OR-PAM) implementation. In a previous study, the authors group have reported a high-speed, high-fidelity photoacoustic endomicroscopy probe that was fitted into a 20-gauge needle for guiding minimally invasive surgery [28]. The probe comprised two optical fibres: a multimode fibre for delivering the excitation light and a single-mode fibre with a plano-concave micro-resonator for ultrasound detection. A tightly focused laser beam was raster-scanned through the multimode fibre with wavefront shaping using a digital micromirror device and a real-valued intensity transmission matrix-based algorithm [29; 30], which enabled sub-cellular imaging of biological tissues at \(\sim\)3 frames per second (fps). Recently, transparent piezoelectric transducers have shown promise in PAI. Compared to optical ultrasound sensors, transparent piezoelectric transducers benefit from simple manufacturing and operational procedures, high stabilities, and low costs (compared to expensive interrogation lasers for optical ultrasound sensors). In 2005, [PERSON] et al. reported a transparent ITO-coated PVDF sensor for PAI [31]. In 2019, [PERSON] et al. reported a transparent capacitive micromachined ultrasonic transducers (CMUTs) using a glass substrate and ITO electrodes, which was demonstrated to detect optically excited ultrasound [32]. [PERSON] et al. fabricated transparent transducers by coating ITO onto lithium niobate (LiNbO\({}_{3}\)) thin films and achieved PAI of artificial absorbers [33]. Most recently, several groups have demonstrated transparent transducers for photoacoustic microscopy of phantoms [34; 35], animal vasculatures in skin [36; 37; 38], eyes [39], and brains [40]. In this work, for the first time to the best of our knowledge, we developed a forward-viewing PAE probe based on a transparent ultrasound sensor integrated at the tip of a multimode fibre. A focused laser beam was raster-scanned through the multimode fibre via wavefront shaping whilst the generated ultrasound was detected with the transparent sensor. Singular value decomposition (SVD) was used to eliminate a prominent trigger-induced noise. High-fidelity OR-PAM images of carbon fibres were achieved at varying distances to the probe distal end.
## 2 Materials and Methods
### Sample Collection and Preparation  
Herbs and plant by-products were obtained from Lemnos Island, in the North Aegean, Greece, between November of 2021 and February of 2022. The samples included rose-mary (_Rosmarinus officinalis_, L.), basil (_Ocimum basililicum_, L.), sage (_Salvia officinalis_, L.), lemon balm (_Melissa officinalis_, L.), St. John's wort (_Hypericum perforatum_, L.), pink savory (_Satureja Thymura_, L.), dittany (_Origanum dictamus_, L.), and by-products such as pomace from olives, olive leaves (_Olea europaea_, L.), orange peel (_Citrus aurantium_, L.), lemon peel (_Citrus limon_, L.), pomegranate peel (_Punica granatum_, L.), and mandarin peel (_Citrus reticulata_). All collected samples were dried in a heating oven (Binder GmbH, Tuttlingen, Germany) at 60 \({}^{\circ}\)C for 48 h and stored in airtight bags at dark conditions till further analysis [23].  
As for the fortification procedure, refined olive oil was purchased from a local certified producer from Lemnos, Greece (Sousalis, Lemnos, Greece). Extra virgin and virgin olive oil that was used for the phenolic content analysis was purchased also from the same producer from Lesvos, Greece (AES Stypsis, Lesvos, Greece).  
Three different methods, as described in Table 1, were applied for the fortification of the refined olive oil: conventional maceration (CM), incubator shaking maceration (ISM), and ultrasound-assisted maceration (UAM).