esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Active Microvibration Isolation System for Precision Space Payload
###### Abstract
China's Technology and Engineering Center for Space Utilization also developed a MAIS vibration isolation system with a combination of acceleration feedback and relative position feedback, and conducted flight experiments on the Tianzhou-1 cargo spacecraft in 2017, where vibrations in the range of 0.1-10 Hz were suppressed to 10\({}^{-8}\) g [25; 26]. Considering that the microvibration on a satellite is usually a complicated and broadband input, an active vibration isolation system is suitable for the frequency band range (0.5-200 Hz) and the suppression effect (root cumulative PSD is below 1.5 \(\times\) 10\({}^{-5}\) g) of the optical reference cavity in space still needs to be further studied. In this paper, an active vibration isolation system applied to precision space payload is developed. A hybrid controller composed of feedforward and feedback is designed to achieve vibration suppression in three directions in a broadband micro-vibration noise environment through eight vibration isolation modules based on the method of decentralized control. The phase change caused by the controller and various filters is considered, and a lead-lag compensator is designed and added to ensure the stability of the system. The proposed active vibration isolation system and control algorithm are finally confirmed by experimental studies. In this study, an active vibration isolation system for microvibration is introduced. The discussion is organized into four sections. (1) The composition of the system, including the overall configuration and control principle of a single vibration isolation module, is described. (2) The design of the hybrid control law and simulation results are described in detail. (3) An experiment and result analysis of the system were carried out. (4) The conclusion is summarized in final section.
## 5 Traffic Models and Utility Functions
### Real Time Video Traffic for Mobility Independent Predictive Services  
When a real-time session is initiated, all the involved applications are extremely sensible to the packet delivery delay and they become unsuitable if the delay overcomes some fixed bounds. The needed data rate during a real-time transmission must be almost constant so, depending on data transmission and compression techniques, a minimum and a maximum level of employed bandwidth can be determined. If utility functions are associated to real-time traffic, the principle of utility max-min fairness must be respected [4,45] (when the perceived utility of a user is increased, the utility of another user, which receives already a smaller utility, is decreased. In this paper, we consider multi-layer video streaming as real-time traffic for NSIS-MIP users [4]. The satisfaction levels for a multi-layer streaming content can be effectively described by a multi-step utility function, where each step corresponds to an encoding layer, representing the achieved utility at that layer. So the general considered expression for an n-layered compressed video is [4]:  
\[U_{RT}(r_{s})=\frac{a_{s,k}}{1+\exp(b_{s,k}(r_{s}-c_{s,k}))^{d_{s,k}}}+\sum_{j= 1}^{k-1}a_{s,j} \tag{4}\]  
where \(r_{s}\) is the received rate level, \(a_{s,k}\), \(b_{s,k}\), \(c_{s,k}\), \(d_{s,k}\) are normalization factors and \(c_{s,k}-\alpha_{s,k}\leq x_{s}<c_{s,k+1}-\alpha_{s,k+1}\), for \(k=1\),..., \(n\), \(\alpha_{s,1}=c_{s,1}\), \(\alpha_{s,k}=\frac{c_{s,k}-c_{s,k-1}}{2}>0\) for \(k=2\),..., \(n\). Figure 2a shows a multi-step utility function for the considered traffic, with the same values used in [4], except for: \(a_{s,1}=1\), \(a_{s,2}=1.7\), \(a_{s,3}=0.9\), \(a_{s,4}=1\), \(c_{s,1}=512\), \(c_{s,2}=640\), \(c_{s,3}=768\), \(c_{s,4}=896\).
## 5 Results and Discussion
### Discussion  
The experimental results demonstrate that all three deep learning models--the CNN, Transformer, and Actor-Critic DRL--performed exceptionally in the wildfire prediction tasks. Across 100 experimental runs, all models consistently achieved high accuracy,  
Figure 9: SHAP feature importance analysis for wildfire prediction models. Higher absolute SHAP values indicate a greater influence on model decisions.  
precision, recall, and F1-Scores (F1 > 0.999), with perfect AUC-ROC scores, suggesting strong discriminative capabilities.  
Among the models, the Transformer architecture exhibited a slight advantage in generalization performance. This can be attributed to its multi-head self-attention mechanism, which effectively captures long-range temporal dependencies and spatial patterns across environmental variables. Such capabilities make Transformers particularly suitable for modeling wildfire dynamics and enhancing early warning systems. However, the CNN and Actor-Critic models also demonstrated competitive results and may be more practical in scenarios with limited computational resources, due to their lower complexity and faster inference times. These findings support the integration of AI-based prediction systems into operational wildfire management frameworks, especially in countries like Chile with a history of severe fire events. This approach is also applicable globally, particularly in regions increasingly affected by climate-driven wildfires such as Australia, the United States, and Southern Europe. In addition, the application of this methodology provides robust support for the delineation of occurrence zones [48], which have persisted and, in some cases, expanded over the last five years.  
Despite the promising outcomes, several challenges remain. High performance scores raise the possibility of overfitting, although mitigation techniques such as stratified sampling, synthetic data generation, and class-balanced training were applied. Additionally, the performance was evaluated over 100 randomized runs, showing minimal variance--an indication of model robustness. Nonetheless, external validation across diverse regions and ecosystems is essential for assessing generalizability.  
Future directions include the incorporation of real-time meteorological data, satellite imagery, and additional environmental variables to enhance prediction granularity and robustness. Moreover, lightweight architectures should be explored to facilitate deployment in resource-constrained environments. These enhancements will strengthen the operational viability of AI-driven wildfire early warning systems.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 3. Perform the Criticality Analysis
#### 3.3.3 Defining the Detectability Index
\begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline
**Score** & **FALSifiability: Independent Subsystem/Interface/Unit/Component That is Affected by Any Failure Producing a Specific Observable Effect** \\ \hline
1 & One subsystem only, affected by any failure, shows a specific observable effect. \\ \hline
2 & Two independent subsystems/components/interfaces affected by any failure might show the same effect, data from the subsystem itself (by analyzing its activity or changing its mode of use) or others (more than two) are useful to distinguish the event. \\ \hline
3 & More than two independent subsystems/components/interfaces affect by a failure might show the same effects on the entire system, data from the subsystem itself (by analyzing its activity or changing its mode of use) or others (more than one) are useful to distinguish the event. \\ \hline
4 & Two or more independent subsystems/components/interfaces affect by a failure show the same effects on the entire system; data from other subsystems may be useful to distinguish the event. \\ \hline
5 & More than 2 independent subsystems/components/interfaces might affect by a failure might show the same effects on the entire system. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Falsifiability coefficients. \begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline
**Score** & **OBSERVABILITY: Independent Ways of Getting Data About the Failure or the Activity/Operation/Functionality of the Subsystem that Fails** \\ \hline
5 & The only source of data about the failure is the subsystem failing. \\ \hline
4 & There is at least another source of data about the failing under exam, apart from the subsystem failing. \\ \hline
3 & There are at least two independent sources of data about the failure under exam, apart from the subsystem failing. \\ \hline
2 & There are at least three independent sources of data about the failure under exam, apart from the subsystem failing. \\ \hline
1 & There are more than three independent sources of data about the failure under exam apart from the subsystem failing. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Observability coefficients.
## 2 Few-Layer Transition Metal Dichalcogenides
### Fabrication Techniques  
Many fabrication procedures exist for obtaining few-layer TMDs, which have evolved from graphene processing strategies [11, 29]. These can be divided into exfoliation and growth techniques. Exfoliation involves cleaving monolayer or few-layer flakes from a bulk crystal, typically achieved mechanically (e.g., using Scotch tape), chemically (e.g., lithium-based intercalation) or through dispersal in solvents (known as liquid-phase exfoliation (LPE)). Alternatively, growth techniques, such as chemical vapour deposition (CVD), can produce high-quality, single layers of the material from carefully controlled chemical reactions between solid precursors [11].  
Figure 2: Transition metal dichalcogenide structure: (**a**) visualization of three-layer crystal; (**b**) simplified energy structure at crystal K point for a typical monolayer semiconducting transition metal dichalcogenide (TMD), showing the relationship between the electronic bandgap \(E_{g}\), the excitonic binding energy \(E_{b}\) and the optical bandgap \(E_{o}\), including a split valence band from spin-orbit coupling. Excitonic transitions are labelled with letters according to standard nomenclature [22].  
Both LPE and CVD approaches have the potential for large-scale production, which is desirable for developing saturable absorbers and photonic devices commercially. CVD typically has a higher yield of large-area monolayer flakes of TMDs, while LPE produces smaller area, few-layer flakes. The flake size is an important parameter in determining the optical properties of a single TMD device. In fact, this flake-size-dependence of the optical properties offers a convenient method for engineering the materials inexpensively using LPE [30]. An example of an LPE-produced MoS\({}_{2}\) dispersion (or \"ink\" [31]) is shown in Figure 3a, following the processing technique presented in [32]. The MoS\({}_{2}\) dispersion contained a distribution of flake thicknesses (measured using atomic force microscopy (AFM), as shown in Figure 3b,c), and notably, \(\sim\)50% of flakes had a 2-4-nm thickness, which corresponds to 4-5 layers, with lateral flake dimensions typically \(<\)200 nm [32].  
To develop practical saturable absorber devices, the processed few-layer nano flakes can be integrated using a variety of optically-compatible strategies [30]. For fibre lasers applications, few-layer TMDs can be directly deposited on fibre ferrules using optical deposition or transferred to the tip of a fibre as a post-processing step [33; 34; 35; 36]. Alternatively, they can be embedded in transparent polymer films to form compact and flexible SA devices [37; 32; 38; 39; 40; 41; 42; 43]. Another attractive prospect is depositing TMD nano flakes along microfibres or D-shaped (side-polished) fibres [44; 45; 46], where the evanescent field mediates the strength of the light-matter interaction. While the light intensity on the TMD material is weaker in this case, the device interaction length can be significantly increased, which can enhance the nonlinearity, affording a higher power tolerance [44].
## 3 Materials and Methods
### Selection of a Suitable ITS Architecture for the Tracking Service  
Selection of an ITS architecture for the transit vehicle tracking service is based on work presented in [13; 15]. First, the ITS architecture proposed for the service [13], based on the American ITS architecture known as ARC-IT [34], was reviewed, which was. The proposed architecture for the PT01 service (PT01 is transit vehicle tracking in ARC-IT) was also reviewed. Additionally, the review included proposals for other reference architectures at an international level (such as European Intelligent Transport Systems Framework Architecture, often now known as The FRAME Architecture [35]) regarding the transit vehicle tracking service.  
The use of a reference ITS architecture and the analysis performed to propose a specific ITS architecture for the service, which considered the special characteristics of the service (transit vehicle tracking service) in medium-sized cities in developing countries such as Colombia, is part of the novelty of the research presented in this paper.  
The analysis of the aforementioned sources allowed to determine the service architecture, as shown in Figure 1. The proposed architecture includes three modules: transit vehicle OBE (on board equipment), ITS roadway equipment, and transit management center. It also proposes a system actor called system operator, who is in charge of interacting with the transit management center module, to obtain information on the state of the transit system. The transit vehicle OBE module is responsible for capturing information for each vehicle (location, speed, etc.) and transmitting it to the transit management center module through ITS roadway equipment, which features network devices (e.g., gateways) located near the roads, or at a medium distance. The architecture also features the basic layers proposed by IoT, where the device layer includes the transit vehicle OBE, the communications layer (between the transit vehicle OBE and the transit management center) is composed of two sections with the participation of the ITS roadway equipment module, and the processing layer is in the transit management center module.  
Figure 1: Selected intelligent transport system (ITS) architecture for transit vehicle tracking service.
## 2 Model Predictive Control Methods
### Greenhouse Environment Predictive Control Model  
Temperature and humidity are important indicators to measure the greenhouse environment. The biggest obstacle for most greenhouse plants to develop diseases is caused by temperature and humidity being out of control. In order to promote the healthy growth of various vegetables in the greenhouse, establishing temperature and humidity prediction models is crucial for temperature and humidity regulation. The predictive control model for greenhouse temperature and humidity is as follows:  
\[x(k+1)=Ax(k)+Bu(k)+Dw(k) \tag{1}\]  
\[y(k)=Cx(k) \tag{2}\]  
When \(x(k)=[x_{1}(k);x_{2}(k)]\)--the state variable of the model, \(x_{1}(k)\)--indoor temperature; \(x_{2}(k)\)--indoor humidity; \(u(k)=[u_{1}(k);u_{2}(k);u_{3}(k);u_{4}(k)]\)--multiple control input variables for indoor control equipment, \(u_{1}(k)\)--heating control variable, \(u_{2}(k)\)--roof angle, \(u_{3}(k)\)--shading screen variable, \(u_{4}(k)\)--humidification control variable; \(w(k)=[w(k)_{outT};w(k)_{outH};w(k)_{outR};w(k)_{outV}]^{T}\)--disturbance variables of external environment, \(w(k)_{outT}\)--outdoor temperature, \(w(k)_{outH}\)--outdoor humidity, \(w(k)_{outR}\)--outdoorlighting, \(w(k)_{outV}\)--wind speed; \(y(k)=[y_{1}(k);y_{2}(k)]\)--measurable output variable, \(y_{1}(k)\)--indoor temperature, \(y_{2}(k)\)--indoor humidity, from Formula (1),  
\[\Delta x(k+1)=A\Delta x(k)+B\Delta u(k)+D\Delta w(k) \tag{3}\]  
Let \(e(k)\) and \(r(k)\) change according to the following Formulas (4) and (5):  
\[e(k)=y(k)-r(k) \tag{4}\]  
\[r(k)=r(k+1)=\cdots\cdots=r(k+N) \tag{5}\]  
When \(\Delta x(k)=[\Delta x_{1}(k);\Delta x_{2}(k)]\)--variation difference between time \(k+1\) and time \(k\), \(\Delta x_{1}(k)\)--indoor temperature, \(\Delta x_{2}(k)\)--indoor humidity; \(r(k)=[r_{1}(k);r_{2}(k)]\)--setting value of indoor temperature and humidity; \(e(k)=[e_{1}(k);e_{2}(k)]\)--difference between actual output and set value, \(e_{1}(k)\)--indoor temperature difference, \(e_{2}(k)\)--indoor humidity difference; \(\Delta w(k)=[\Delta w(k)_{outT};\Delta w(k)_{outH};\Delta w(k)_{outR};\Delta w( k)_{outV}]^{T}\)--variation difference of external environmental disturbance variables, according to the following Formulas (4) and (5),  
\[\begin{array}{l}e(k+1)=e(k)+y(k+1)-y(k)+r(k)-r(k+1)\\ =e(k)+C\Delta x(k+1)=e(k)+CA\Delta x(k)+CB\Delta u(k)+CD\Delta w(k)\end{array} \tag{6}\]
## 2 Problem Model
### Mathematical Model  
In order to establish the mathematical model of VRPCSC more accurately, the mathematical notations are defined as follows:  
**(1) Sets**  
\(SS\): Set of suppliers.  
\(SM\): Set of manufacturers.  
\(SP\): Set of warehouses.  
\(SL\): Set of material types.  
\(SK\): Set of manufacturer vehicles.  
**(2) Parameters**  
\(e_{sl}\): Maximum supply quantity of material \(l\) of supplier \(s\).  
\(c_{sl}\): Cost per unit weight of material \(l\) of supplier \(s\).  
\(b_{s}\): Delivery cost per unit distance of materials delivered by vehicles of supplier \(s\).  
\(n_{m}\): Number of vehicles owned by manufacturer \(m\).  
\(c_{m}\): Processing cost per unit weight of products made from materials by manufacturer \(m\).  
\(b_{mk}^{\prime}\): Delivery cost per unit distance of products delivered by vehicle \(k\) of manufacturer \(m\).  
\(u_{mk}\): Maximum loading capacity of vehicle \(k\) of manufacturer \(m\).  
\(d_{sm}\): Distance from supplier \(s\) to manufacturer \(m\).  
\(d_{mp}^{\prime}\): Distance from manufacturer \(m\) to warehouse \(p\).  
\(d_{pp^{\prime}}^{\prime\prime}\): Distance from warehouse \(p\) to warehouse \(p^{\prime}\).  
\(o_{p}\): Weight of products needed by warehouse \(p\).  
\(o_{pl}^{\prime}\): Weight of material \(l\) corresponding to the products needed by warehouse \(p\).  
**(3) Variables**  
\(y_{mkp}\): If warehouse \(p\) is the first warehouse distributed by vehicle \(k\) of manufacturer \(m\), \(y_{mkp}=1\); otherwise, \(y_{mkp}=0\).  
\(y_{mkp^{\prime}}^{\prime}\): If vehicle \(k\) of manufacturer \(m\) directly delivers products to warehouse \(p^{\prime}\) after delivery to warehouse \(p\), \(y_{mkp^{\prime}}^{\prime}=1\); otherwise, \(y_{mkp^{\prime}}^{\prime}=0\).  
\(y_{mkp}^{\prime\prime}\): If warehouse \(p\) is the last warehouse distributed by vehicle \(k\) of manufacturer \(m\), \(y_{mkp}^{\prime\prime}=1\); otherwise, \(y_{mkp}^{\prime\prime}=0\).  
\(g_{smpl}\): If the products delivered by vehicle \(k\) of manufacturer \(m\) to warehouse \(p\) are made from material \(l\) of supplier \(s\), \(g_{smklp}=1\); otherwise, \(g_{smklp}=0\).  
\(g^{\prime}_{sm}\): If supplier \(s\) uses the supplier's vehicle to deliver materials to manufacturer \(m\), \(g^{\prime}_{sm}=1\); otherwise, \(g^{\prime}_{sm}=0\).  
\(g^{\prime\prime}_{mkp}\): If vehicle \(k\) of manufacturer \(m\) is used to deliver products to warehouse \(p\), \(g^{\prime\prime}_{mkp}=1\); otherwise, \(g^{\prime\prime}_{mkp}=0\).
## 3 Wireless Sensor Networks
### WSN Applications
#### 3.3.5 Healthcare Applications  
Some of the health applications of sensor networks involve providing interfaces for the disabled, integrated patient monitoring, diagnostics, drug administration in hospitals, telemonitoring of human physiological data, and tracking and monitoring doctors and/or patients inside a hospital [34-36]. We will briefly explore some capabilities of WSN for healthcare monitoring [37] as we focus in this paper on wearable and implantable body area networks.  
\(\bullet\) **Telemonitoring of Human Physiological Data**: The physiological data collected by sensor networks may be stored for a long period of time, and can be used for medical investigations when needed. In addition, the installed sensors can also monitor and detect the behavior of elderly people. As an example, a health smart home was designed by the Faculty of Medicine in Grenoble-France to check the feasibility of such systems [38].  
\(\bullet\) **Tracking and Monitoring Doctors and Patients inside a Hospital**: Each patient has a small sensor node attached to them. Sensors vary based on their functions and each sensor node has its own specific task to perform. For example, one sensor node may be detecting the heart rate while another is detecting the blood pressure. Doctors can also carry a sensor node, which allows other doctors to locate them within the hospital.  
\(\bullet\) **Drug Administration in Hospitals**: If sensor nodes can be attached to medication, the chance of getting and prescribing the wrong medication to patients can be minimized. Thus, patients will have sensor nodes that identify their allergies and required medication. Computerized systems as described in [39] have shown that they can help minimize the side effect of drugs.
## 3 DT Model of the Electromagnetic Coupling Process of the BTS
### Mutual Inductance Calculation between the OAU and the Balise  
Balise antenna and vehicle antenna generally adopt the design of rectangular coil antenna, which belongs to electric small loop antenna. According to the research in [22], when the OAU of the BTS is more than 11 cm away from the metal plate, the influence of the eddy magnetic field generated by the metal plate on the OAU can be ignored. Therefore, we can establish a simplified model of the uplink transmission process in the BTS, as shown in Figure 4.  
Rectangular ABCD is a balise transmitting antenna, and rectangular EFGH is an onboard receiving antenna. A three-dimensional rectangular coordinate system is established, taking the center of the balise transmitting antenna as the origin. It takes the directions parallel to the AB and DA sides of the balise transmitting antenna as the OX axis and the OY axis. Let the direction perpendicular to the balise transmitting antenna be the OZ axis. The train speed vector \(\overrightarrow{V}\) is consistent with the positive direction of the OX axis.  
Let \(\mu_{0}\) be the vacuum permeability, and \(x_{0}\) and \(y_{0}\) be half of the side length of the balise transmitting antenna along the OX axis and the OY axis, respectively. Let \(x_{1}\) and \(y_{1}\) be half of the side length of the onboard receiving antenna along the OX axis and OY axis, respectively. \((\Delta x,\ \Delta y,\ \Delta z)\) is the coordinate of the center point of the onboard receiving antenna. An analytical model of mutual inductance between two parallel rectangular antennas at arbitrary relative positions can be obtained by applying the second-order vector potential method [23].  
\[M=\frac{2\mu_{0}}{\pi^{2}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}ke^{-k \Delta z+j(a\Delta x+b\Delta y)}\frac{\sin(ax_{0})}{a}\cdot\frac{\sin(by_{0})} {b}\cdot\frac{\sin(ax_{1})}{a}\cdot\frac{\sin(by_{1})}{b}dadb. \tag{1}\]  
Equation (1) is an essential basis for the quantitative evaluation of the electromagnetic coupling characteristics of the BTS. It explains the interaction between the onboard receiving antenna and the balise. The induced voltage of the onboard receiving antenna can also be calculated according to (1).