esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 4 Resource Request Queue Reconstruction and Collaborative Scheduling Method
### Problem Conversion
Therefore, model (2) can be decomposed into two models: the contention model for inter-satellite link resources and the multi-satellite resource collaborative allocation optimization model.
# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 3 SPE in Food Analysis--Main Fields of Applications
### Lipid Analysis
Plant sterols exist as free or bound (esters, glycosides) species and they have proven health-beneficial properties, decreasing low-density lipoprotein (LDL) cholesterol [28]. SPE is suitable to separate covalently bound plant sterols from lipid extracts (prior and posterior hydrolysis process) in purification procedures or to enrich the phytosterols fraction from other unsaponifiable lipids. SPE can be utilized to obtain polar and non-polar fractions, which should be processed and analyzed one by one [29]. Vitamins A, D, E, and K are gathered into clusters of fat-soluble vitamins. They represent essential micronutrients with a wide spectrum of important biological influences on human health [30; 31; 32; 33]. As a result of their specific chemical structure, they are highly prone to several factors such as light sensitivity, oxygen access, pH value conditions, and temperature. The stability of vitamins makes an extraction process a critical step in further analysis. SPE with a silica column is commonly used for the analysis of fat-soluble vitamins in fruits, vegetables, meat, cereals, herbs, spices, seeds, seasoning blends, and baby food matrices [34].
## 2 Theoretical Background
### Analysis of the Possibilities of Wind Gusts Estimation (3D Problem)
Integrated measurement and navigation systems installed on commercial aircraft enable direct data acquisition in these reference frames (of course not globally, but specific variables only in selected reference systems). For example, the ADIRS system allows the determination of the aircraft's velocity relative to both the Earth and the air mass. Additionally, using data from ADIRS accelerometers enables the measurement of accelerations and, subsequently, the calculation of velocity values (or changes) in the aircraft-fixed frame. The complementary approach to the problem of three-dimensional gust estimation proposed in this paper does not require detailed modelling of the dynamic properties of the aircraft or as many simplifying assumptions as when using only state equations. However, state equations allow for a relatively accurate determination of the fast-varying gust components. The airflow movement along each axis was analyzed individually, enabling the development of methods for estimating both the slow-varying component (steady wind) and the fast-varying component (gusts). The specific methods are detailed in Sections 2.2-2.4.
## 3 Key Techniques
### FE Simulation Data Mapping Method
#### 3.3.2 Velocity Mapping Method
In summary, the velocity mapping method can be divided into the following three steps, which are schematically diagrammed in Figure 6.  
Figure 6: Flowchart of the velocity mapping method. * Step 1: Extract the FE results at time step \(t_{j}\), including velocity \(v_{j}\) and displacement \(x_{j}\). * Step 2: Obtain \(x_{\text{virtual}}\) using Equations (4) and (5), and set the displacement of the frame \(k\) to \(x_{\text{virtual}}\). * Step 3: At frame \(k\), convert the kinematic type to the animation system, and then insert the displacement and kinematic type keyframes for each object. Convert the frame from frame \(k\) to frame \(j\) after the keyframe insertion at frame \(k\). At frame \(j\), convert the kinematic type to the physics engine system, and then insert the displacement and kinematic type keyframes for each object.
## 2 Materials and Methods
### Deployment Scenarios
#### 2.2.2 Event-Driven Architecture  
The deployment uses event-driven architecture to handle the asynchronous nature of healthcare events. Rather than batch processing, the CDSS operates in near-real-time:  
* Data ingestion is continuous via Kafka; each new piece of information is an event (e.g., \"new triage note for patient X\" or \"vital sign update for patient Y\"). These events trigger downstream actions. For instance, an incoming event of an abnormal lab result could directly invoke the DSE to re-evaluate the condition of the patient.
* We deployed Node-RED flows as part of the digital twin subsystem to orchestrate IoT events. For example, a Node-RED flow is configured such that if a digital twin of the patient emits an event \"SpO2 < 90%\", the flow calls a triage re-assessment function in the DSE, which may upgrade the priority of the patient and send an alert to the UI.
* The components of the system communicate through message queues and RESTful services. In deployment, containerization (using Docker/Kubernetes) proved helpful: each layer or service (ingestion, FL aggregator, LLM service, decision engine, UI backend) runs in its own container/pod. Kubernetes event-driven autoscaling can be  
Figure 4: OITS workflow.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Function** & **Implementation Detail** & **Example** \\ \hline \multirow{2}{*}{Code normalization} & Fast hash-based lookups in a local terminology & \multirow{2}{*}{Troponin I \(\rightarrow\) LOINC 10839-9} \\  & cache (Redis [62]) seeded from the master mapping & \\  & tables of the institution & \\ \hline \multirow{2}{*}{Ontology alignment} & Rule-based crosswalks maintained in an Apache & \multirow{2}{*}{ICD-10 I2I.3 (AMI) \(\leftrightarrow\) SNOMED CT 57054005} \\  & Jena Fuseki triple store; SPARQL CONSTRUCT rules & \\  & materialize equivalence triples nightly & \\ \hline \multirow{2}{*}{Semantic enrichment} & Adds transitive “is-a” and “part-of” relationships so & \multirow{2}{*}{Severe sepsis inherits parent class Sepsis} \\  & downstream reasoners can exploit hierarchy & \\ \hline \multirow{2}{*}{Quality assurance} & Terminology-drift detector flags new local codes or & \multirow{2}{*}{Alerts when lab interface starts sending retired} \\  & deprecated concepts; discrepancies surface in the & LOINC codes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Core Functions of the OITS: Implementation Details and Illustrative Examples.  
employed--e.g., scaling out the LLM service pods if there is a surge of simultaneous user queries.
* Latency is an important deployment metric. In our tests, we aimed for end-to-end latency (from data arrival to recommendation) to be under a few seconds for urgent events. We achieved this by tuning Kafka (ensuring minimal ingestion lag) and by using efficient model serving techniques (such as quantized LLMs for faster inference). Non-urgent analyses (like periodic model retraining) are handled asynchronously to avoid interfering with time-sensitive recommendations.
# Hybrid CMP Slurry Supply System Using Ionization and Atomization
## 2 Materials and Methods  
### Hybrid Slurry Supply System  
Conventional systems supply the slurry continuously through a single-tube nozzle at the end of the slurry arm. However, a large amount of the supplied slurry does not participate in the polishing process, resulting in wastage owing to the bow wave and centrifugal force [13]. Consequently, the efficiency of slurry utilization is reduced. Figure 1a depicts the slurry supply on the pad and the bow wave on the leading edge of the carrier of the conventional slurry supply system. The bow wave generated at several millimeters high interferes with the slurry entering the wafer-pad interface [14]. Figure 1b shows that when the hybrid slurry supply system is used, a thin slurry film is formed on the pad and bow wave is hardly observed.  
Figure 1: Comparison of the slurry supply on the pad and the bow wave using dyed slurry. Images of (**a**) the conventional slurry supply system and (**b**) the hybrid slurry supply system.  
The proposed hybrid slurry supply system can enhance the polishing efficiency with minimal cost and minor modifications to the existing equipment. This system combines the slurry ionization and atomization using electrolysis and a spray slurry nozzle, respectively. Initially, the slurry is ionized using the cathode and anode connected to the alternating current (AC) power supply in the day tank. The slurry ionized by AC power supply has no pH change or aggregation. Subsequently, the slurry is supplied to the atomization system through a pump. The ionized slurry is atomized with oxygen or nitrogen gas using a spray slurry nozzle and supplied to the pad uniformly. Thus, the hybrid slurry supply system improves the MRR effectively, owing to the enhanced chemical activation based on the electrolytic ionized slurry. Additionally, the uniformity is improved owing to the atomized slurry supplied through the spray slurry nozzle. Moreover, it serves as a cooling system as the gas is supplied with the ionized slurry through the nozzle. Figure 2 illustrates the schematic of the proposed hybrid slurry supply system.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 6 Effects of Carbon-Based NPs on Medicinal and Aromatic Plants
### Carbon Dots  
Impact on Photosynthesis  
[PERSON] et al. [81] comprehensively overviewed the interactions of CDs on plant growth, internal physiological processes, and further external factors affecting plant growth. Amine functionalized CDs, effectively conjugated over the surface of the chloroplast after absorbing light or photons can transfer electrons towards chloroplasts, thereby positively affecting photosynthetic electron transport resulting in faster conversion of light energy to the electrical energy and finally to the chemical energy [302]. [PERSON] et al. [303] prepared far-red (FR) CDs that are able to efficiently convert UV-A light to 625-800 nm FR emission, i.e., wave lengths suitable to be directly absorbed by photosynthesizing organisms. In an in vitro experiment, coating of chloroplasts of _Lactuca sativa_ L. var. _longifolia_ with FR CDs resulted in a hybrid photosynthetic system, ensuring improved efficiency of electron transport between photosystem PSII and PSI, leading to increased ATP production. Moreover, in an in vivo experiment, the FR CDs-treated lettuce achieved higher electron transfer rate by 28.00% compared to the control, resulting in increased fresh and dry weights by 51.14% and 24.60%, respectively, suggesting that FR CDs could be used for improved conversion efficiency from solar energy to chemical energy.
## 5 Latency Mitigation Strategies
### Perception Latency Approaches
These approaches have been shown to give good results in terms of performance. The advanced methods for predictive display include the use of generative AI methods for pixel synthesis (e.g., GANs) [129; 46; 130], pixel transformation and time-series methods (e.g., LSTMs) [61], and probabilistic models [8]. Alternate methods that reduce the burden on computation and the interface can be considered, such as motion and content separation and extracting higher-level features in the visual feedback. Though DNNs offer accurate predictions for non-linear and long-term signals, they require large amounts of data to train for this accuracy, which can be costly in terms of computation, memory, and time [15]. Additionally, they are considered black-box models, which affects their transparency and trustworthiness, especially in safety-critical applications such as remote driving. Recent studies have also demonstrated that NNs' predictions can be altered/manipulated by pixel adversarial attacks [131; 132]. Other visual feedback enhancement techniques for enhancing teleoperators' overall situational awareness (and not for delay compensation) include exocentric view, automatic view adjustment, stereoscopic vision, virtual environment, vision-based object tracking, and predictive systems [51]. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Domain** & **Teleoperation Entity** & **Algorithm** \\ \hline
[133] & CAV teleoperation & Video stream, encoding latency & Image processing approaches, video encoder configuration \\ \hline
[122] & Telepresence & Head-mounted display (HMD) prediction & Weighted least squares, Kalman filter, weighted sum \\ \hline
[129] & Robotic teleoperation & Predictive display & CycleGAN \\ \hline
[116] & Tactile Internet-based remote robotic surgery & Predicting haptic commands & Gaussian process regression (GPR) \\ \hline
[47] & Robotic teleoperation & Video transformation enhancement & Two video transformation-based assistive visual interfaces \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mitigation strategies for perception latency.
## 2 HY-2B/2C/2D Spacecraft
### Satellite Introduction  
The HY-2B/2C/2D satellite constellation offers the capability for global continuous observation, enabling real-time data collection within the region spanning from 5\({}^{\circ}\) S to 50\({}^{\circ}\) N and 100\({}^{\circ}\) E to 150\({}^{\circ}\) E. These satellites actively transmit real-time remote sensing microwave signals and receive the reflected remote sensing signals.  
The HY-2B satellite orbits in a sun-synchronous frozen orbit, characterized by an inclination of 99.3\({}^{\circ}\) and an orbital altitude of 973 km. In contrast, the HY-2C and HY-2D satellites follow regression frozen orbits with inclinations of 66\({}^{\circ}\) and orbital altitudes of 1336 km and 973 km, respectively. Specific orbital information is summarized in Table 1.  
All HY-2 satellites are equipped with a satellite-borne DORIS receiver, a laser retroreflector arrays, and the indigenously developed \"HY2_Receiver\" satellite-borne GPS receiver. The receiver uses non-coding tracking technology, capable of tracking GPS signals on L1 and L2 frequencies, achieving centimeter-level orbit accuracy [21].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **HY-2B** & **HY-2C** & **HY-2D** \\ \hline Launch time & 25 October 2018 & 21 September 2020 & 19 May 2021 \\ Orbit type & Sun-synchronous & Regression Frozen & Regression Frozen \\  & orbit & orbit & orbit \\ Orbital altitude & 973 km & 1336 km & 973 km \\ Orbital inclination & 99.3\({}^{\circ}\) & 66\({}^{\circ}\) & 66\({}^{\circ}\) \\ Mass at launch & 1500 kg & 1500 kg & 1575 kg \\ Repeat period & 14 day & 10 day & 10 day \\ Adjacent orbit & 207.64 km & 292.52 km & 292.52 km \\ pitch: & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: HY-2B/2C/2D satellite information.  
Figure 1 depicts the distribution of these payloads in the satellite coordinate system, with the +X axis oriented in the direction of flight, the +Z axis aligned with the geocentric direction, and the +Y axis orthogonal to both the X and Z axes, forming a right-handed coordinate system [22].
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 5 Conclusions
For example, in the case of sliding mechanical equipment, the GMM method outperforms others, achieving an AUC of 99.79% and a pAUC of 98.94%. In pump equipment monitoring (such as water or oil pumps), the LOF method delivers better results, with an AUC of 96.35% and a pAUC of 90.10%. In applications where false positives are especially critical, such as in power equipment or medical devices, deploying a combination of network architectures and anomaly scoring methods, along with a voting mechanism, can further reduce the likelihood of false alarms and ensure system stability and safety. Future research will focus on three key areas. First, we aim to deploy ASDNet and SpecMFN on edge devices and develop real-time anomaly detection systems to assess their performance and stability in industrial environments. Second, we will investigate novel combinations of input features and lightweight network architectures to address diverse industrial needs and drive the broader adoption of ASD technology. Finally, while KMeans, GMM, and LOF are foundational clustering algorithms, their performance in Anomalous Sound Detection tasks may not be optimal. More advanced methods, such as federated heuristic optimization based on fuzzy clustering or fixed-centered KMeans, could potentially offer improved performance. Exploring these alternatives will be part of our future work. Conceptualization, D.K. and G.Y. ; methodology, D.K. ; validation, D.K. and H.Y. ; formal analysis, D.K. ; investigation, D.K. ; resources, D.K. ; writing--original draft preparation, D.K. ; writing--review and editing, D.K., H.Y., S.W. and B.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Publicly available datasets were analyzed in this study. The data can be found at [[https://dcase.community/challenge2020/](https://dcase.community/challenge2020/)]([https://dcase.community/challenge2020/](https://dcase.community/challenge2020/)), accessed on 25 November 2024. The authors declare no conflicts of interest.
## 3 The Proposed MPPT Algorithm
### Voltage Derivative Threshold  
The symmetrical hysteresis control threshold for the capacitance time derivative has to be chosen such that before the MPP (left side of MPP on Figure 2b); the chosen positive slope of the power requested by the inverter causes a value of \(\mathrm{d}v_{c}/\mathrm{d}t\) within the band, while after the MPP (right side of MPP on Figure 2b), \(\mathrm{d}v_{c}/\mathrm{d}t\) overcomes the band. The expected value of \(\mathrm{d}v_{c}/\mathrm{d}t\) at the left side of MPP can be easily calculated assuming a low ripple on the capacitance voltage. In this case, indeed, the slope of the inverter current is proportional to the slope of power (via the reciprocal of \(v_{mpp}\)). As the filter is sized such that the PV follows the request of the inverter, the time derivative of the PV current corresponds to the one of the inverter, and the time derivative of the capacitance voltage corresponds to that of the PV array. Thus, the expected time derivative of the capacitance voltage can be written as follows:  
\[\frac{\mathrm{d}v_{c}}{\mathrm{d}t}=-R_{2}\frac{p_{slope}}{v_{mpp}}, \tag{4}\]  
with \(R_{2}=-\mathrm{d}v_{p}/\mathrm{d}i_{p}\) belonging to the second zone of the PV characteristic of Figure 2b.  
Using a safety coefficient of 2, a reasonable value for the hysteresis band can be chosen as follows:  
\[th_{dvc}=2\cdot R_{2}\frac{p_{slope}}{v_{mpp}}, \tag{5}\]
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 3 Hierarchical Task Assignment and Path Finding
### HTAPF: Principles and Methods
```
1:procedureStep(\(a_{i}^{n}\), \(D\), \(P_{a}\), \(P_{d}\))
2:for\(\tau\) in range \(1\ldots D\)do
3:\(\gamma_{m}^{n+1},\rho_{m}^{n+1},\alpha_{i}^{n},\beta_{i,\tau}^{n},\psi_{i}^{n}\gets 0\)\(\triangleright\) Reset transition probabilities
4:\(r_{o}\leftarrow\text{SelectNeighbour}()\)\(\triangleright\) Random choice of a known robot
5:\(s_{i}\leftarrow\text{NextState}(P_{a},P_{d})\)\(\triangleright\) Selection of next transition type
6:if\(s_{i}=s_{a}\)then
7:\(\alpha_{i}^{n},\beta_{i,\tau}^{n},\psi_{i}^{n}\leftarrow\text{AscendingTransitions}(a_{i}^{n},r_{o})\)\(\triangleright\) Equations (6)-(8)
8:else
9:\(\gamma_{i}^{r},\rho_{i}^{r}\leftarrow\text{DescendingTransitions}(a_{i}^{n},r_{o})\)\(\triangleright\) Equations (4) and (5)
10:\(a_{g}\leftarrow\text{RandomTransition}(\gamma_{i}^{r},\rho_{i}^{r}\alpha_{i}^{n}, \beta_{i,\tau}^{n},\psi_{i}^{n})\)\(\triangleright\) New hierarchical area
11: UpdateTree(\(a_{g}\))\(\triangleright\) Update all ancestors
12:\(cost\), \(plan\leftarrow\text{PlanMotion}(a_{i}^{n},a_{g})\)\(\triangleright\) Collision Free Path
13: Broadcast(\(a_{g},U^{r}(a_{g}),cost,plan\))
14: UpdateUtility(\(a_{g},cost\))\(\triangleright\)\(U^{r}(a_{g})\) in Equation (3)
15: ExecuteNextAction(\(plan\))
```  
**Algorithm 1** HTAPF
## 3 Proposed Analog RF Beamforming
### Relay Analog RF Beamforming
#### 3.3.2 Relay Analog Precoder  
To derive \(\mathbf{F}_{2k}\), a similar problem is formulated that attempts to maximize SNR at the destination. Hence,  
\[h_{k}=\max_{F_{2k},k\in\{1,\ 2,\ldots,K\}}\frac{\|W_{opt}^{H}G_{k}\ F_{2k} \|_{F}^{2}}{\sigma_{d}^{2}}\]  
\[s.t.\ |F_{2k}(x,y)|=\frac{1}{\sqrt{N_{rel}}},\ \forall\ x,y. \tag{38}\]  
Just like the problem (33), the composite channel \(\mathbf{G}_{eff,k}=\mathbf{W}_{opt}^{H}G_{k}\) from the \(k\)th relay to the destination can be defined by exploiting the available information. It enables the transformation of (38) into the following form  
\[h_{k}=\max_{F_{2k},k\in\{1,\ 2,\ldots,K\}}\frac{\|\mathbf{G}_{eff,k}F_{2k} \|_{F}^{2}}{\sigma_{d}^{2}}\]  
\[s.t.\ |F_{2k}(x,y)|=\frac{1}{\sqrt{N_{rel}}},\ \forall\ x,y. \tag{39}\]  
Similarly, the problem (39) can also be transformed into another equivalent form, as that of (33), which helps in deriving the required RF beamformer \(\mathbf{F}_{2k}\). Hence,  
\[h_{k}=\max_{F_{2k}}\left\{\frac{\|\mathbf{G}_{eff,1}\mathbf{F}_{2k}\|_{F}^{2}}{\sigma_ {d}^{2}},\ldots,\frac{\|\mathbf{G}_{eff,k}\mathbf{F}_{2k}\|_{F}^{2}}{\sigma_{d}^{2}} \right\}. \tag{40}\]  
It is possible to obtain \(\mathbf{F}_{2k}\) by computing the maximum gain of \(\mathbf{G}_{eff,k}\forall k\in\{1,\ldots,K\}\). Therefore, the problem (40) can be transformed into another useful form for designing \(\mathbf{F}_{2k}\) as  
\[h_{k}=\max\Bigl{\{}\|\mathbf{G}_{eff,1}\|_{F}^{2},\ldots,\|\mathbf{G}_{eff,K}\|_{F}^{2 }\Bigr{\}}. \tag{41}\]The solution to the problem (41) gives the maximum gain of \(\mathbf{G}_{eff,k}\) with index \(k\) at which this condition satisfies. Therefore, \(\mathbf{F}_{2k}\) can be obtained by including the element-wise constant modulus constraint as  
\[\mathbf{F}_{2k}=\left(\frac{1}{\sqrt{N_{rel}}}\right)e^{jangle\mathbf{G}_{eff,k}^{H}}. \tag{42}\]
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
## 2 Modeling of Spatial Prismatic Joint Clearance
### Contact Force Calculation Under Different Contact Modes  
Due to the clearance, during the motion of the mechanism, the contact surfaces of the two elements will experience friction and wear, leading to high temperatures. Ignoring factors such as wear and lubrication, the contact forces between \(L\) and \(K\) are the normal force (\(F_{N}\)) and the tangential force (\(F_{T}\)). Depending on the type of contact between the two, the calculation of normal force is divided into the following three types: