esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Integrated Modeling
### Optical Subsystem  
The decrease in the imaging resolution of optical remote sensing satellites is the ultimate effect of micro-vibration, so it is more intuitive to estimate the impact of micro-vibration by taking the imaging resolution index as the final output result. Micro-vibration will cause the optical element to jitter and deviate from its initial position. When the position of any optical element changes, the position of the image point also changes, resulting in a decrease in resolution.  
The optical model used for micro-vibration analyses can be described as a Taylor expansion of the optical path lengths of a uniformly incident beam through the optical subsystem. Since the magnitude of micro-vibration disturbance is very small, high-order terms can be ignored. Image point movement can be decomposed as a function of the displacement and rotation angle of each optical element:  
\[\Omega=\sum_{i=1}^{N}\frac{\partial L}{\partial T_{i}}\Delta T_{i}+\sum_{i=1} ^{N}\frac{\partial L}{\partial R_{i}}\Delta R_{i} \tag{8}\]  
where \(\Delta T_{i}\) and \(\Delta R_{i}\) are the displacement and rotation angle vectors of each optical component, and \(\frac{\partial L}{\partial T_{i}}\) and \(\frac{\partial L}{\partial R_{i}}\) are the displacement and rotation angle transfer coefficients of each optical component, which can be obtained by optical calculation software. The response \(y_{jit}\) caused by an optical element in physical coordinates can be converted into image point movement \(\Omega\) by Equation (8).  
The optical subsystem in a satellite is a Cassegrain coaxial two-mirror optical system, and the corresponding schematic diagram is given in Figure 6. The optical subsystem is mainly composed of two reflecting mirrors and one focal plane. For such an optical system, micro-vibration affects imaging mainly by the micro-vibration response of the primary mirror, secondary mirror and focal plane. Therefore, in Section 2.3, the three nodes' information is selected as a micro-vibration response output.  
Figure 5: Block diagram of the control subsystem.
## 5 Results and Discussion
### Total Transmitted Traffic  
Network message collision/failure is a key drawback for a network with restricted bandwidth, especially when transmitted traffic is increased. Total transmitted traffic (TTR) includes network and data messages which are forwarded during MA route planning and data collection.  
According to Figure 8, MINDS reduces TTR as compared with the benchmarks. This is because of utilising NDN routing, which avoids transmitting redundant and/or meaningless messages. NDN routing utilises a data-centric technique which only allows forwarding network traffic if it matches a routing forwarding strategy. That way, LDSs avoid forwarding/broadcasting the network traffic blindly. Moreover, this utilises a clustering technique which limits the network communication to intra-cluster messages. This means that network traffic is reduced, as only a few LDSs participate in route planning. Indeed, CHs are the only nodes which forward the route planning messages, and CMs do not individually participate in route planning. The Hamming distance technique is a lightweight clustering technique which utilises a reduced number of network messages to form the clusters. It is performed in a distributed manner and avoids widely transmitting network messages for clustering.  
TBID forwards network messages with no restrictions to establish address-centric routes. This increases transmitted network traffic if the network deployed is crowded and several LDSs participate in the route planning procedure.  
Figure 7: Data collection path hop-count.
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
## 4 Numerical Simulation for Sub-Nanoseconds  
The passively Q-switched rate equations were first derived [17] by [PERSON] and [PERSON], and [PERSON] developed a method for optimizing the model of passively Q-switched rate equations [18]. [PERSON] and [PERSON] presented a generalized model [19] of a passively Q-switched laser that can describe both ground and excited state absorption (ESA) at the laser wavelength of a saturable absorber. The rate equations are as follows:  
\[\frac{d\phi}{dt}=\frac{\phi}{t_{\mathrm{r}}}\bigg{\{}2\sigma nl-2\sigma_{\mathrm{ gs}}n_{\mathrm{gs}}l_{\mathrm{s}}-2\sigma_{\mathrm{es}}n_{\mathrm{es}}l_{ \mathrm{s}}-\left[\ln\left(\frac{1}{R}\right)+L\right]\bigg{\}} \tag{1}\]  
\[\frac{dn}{dt}=-\gamma\sigma c\phi n \tag{2}\]  
\[\frac{dn_{\mathrm{gs}}}{dt}=-\sigma_{\mathrm{gs}}c\phi n_{\mathrm{gs}} \tag{3}\]  
\[\frac{dn_{\mathrm{es}}}{dt}=-\sigma_{\mathrm{es}}c\phi n_{\mathrm{es}} \tag{4}\]  
\[n_{\mathrm{gs}}+n_{\mathrm{es}}=n_{0} \tag{5}\]  
\[T_{0}=e^{-n_{0}\sigma_{\mathrm{gs}}l_{\mathrm{s}}} \tag{6}\]  
where \(\phi\) is the density of the photon, \(t_{\mathrm{r}}\) is the round trip time, and \(\sigma\) is the radiation cross section of Nd\({}^{3+}\): YAG. \(\sigma_{\mathrm{gs}}\) and \(\sigma_{\mathrm{es}}\) are the ground and excited state absorption cross sections, respectively. \(n\) is the inverted population density in Nd\({}^{3+}\): YAG, while \(n_{0}\), \(n_{\mathrm{gs}}\), and \(n_{\mathrm{es}}\) are the total doping particle density, and ground and excited state particle densities in Cr\({}^{4+}\): YAG, respectively. \(l\) and \(l_{\mathrm{s}}\) are the lengths of Nd\({}^{3+}\): YAG and Cr\({}^{4+}\): YAG, respectively. \(T_{0}\) and \(R\) refer to an initial transmittance of Cr\({}^{4+}\): YAG and the reflectivity of the output coupler, respectively. \(\gamma\) refers to the level degeneracy factor, while \(L\) is the round trip loss in the cavity.  
Based on the above equations, we processed a numerical simulation on output pulse widths with respect to the key parameters as an initial transmittance of Cr\({}^{4+}\): YAG (\(T_{0}\)), the reflectivity of output coupler (\(R\)), and the cavity length (CL). The results are given in Figure 9.  
Figure 8: Quality factor \(M^{2}\) of the 532 nm beam.  
For obtaining sub-ns pulse widths, there are wider range options for \(T_{0}\) and \(R\) as CL of 7 mm than CL of 9 mm, as shown in Figure 9. Some key parameters are shown in Table 2. As \(T_{0}\) = 45%, \(R\) = 40%, and CL = 7 mm, the pulse width of the seed was expected to be about 850 ps. This resulted in a good agreement between the experiments and modeling.
## 2 Theoretical Background
### Life Assessment Method Based on IG Process for Accelerated Degradation Test
Therefore, the log-likelihood function can be expressed as follows:  
\[\ln L(\mathbf{\theta})=\sum_{j=1}^{n}\sum_{i=1}^{k}\sum_{l=1}^{m_{i}}\left(\frac{ \ln\lambda}{2}+\ln t_{ij,l}-\frac{\lambda\Big{(}y_{ij,l}e^{-\alpha_{0}-\alpha _{1}x_{i}}-t_{ij,l}\Big{)}^{2}}{2y_{ij,l}}\right) \tag{6}\]  
where \(\mathbf{\theta}=(\alpha_{0},\alpha_{1},\lambda)^{\prime}\) can be solved by the MLE method. In reliability degradation that follows the IG process, the product life \(T_{D_{f}}\) can be defined as the time when the degradation \(Y(t)\) first reaches the failure threshold \(D_{f}\):  
\[T_{D_{f}}=\inf\Bigl{\{}t\Big{|}Y(t)\geq D_{f}\,\Bigr{\}} \tag{7}\]  
The cumulative distribution function (CDF) of product life is the probability that the degradation \(Y(t)\) exceeds the failure threshold \(D_{f}\). Therefore, the CDF of the product life \(T_{D_{f}}\) is as follows:  
\[F_{T_{D_{f}}}(t)=P\Big{(}T_{D_{f}}\leq t\Big{)}=1-\Phi\left[\sqrt{\frac{ \hat{\lambda}}{D_{f}}\bigg{(}\frac{D_{f}}{e^{(\hat{\alpha}_{0}+\hat{\lambda} _{1}x)}}-t\bigg{)}}\right]-\exp\left(\frac{2\hat{\lambda}t}{e^{(\hat{\alpha}_{ 0}+\hat{\lambda}_{1}x)}}\right)\Phi\left[-\sqrt{\frac{\hat{\lambda}}{D_{f}} \bigg{(}\frac{D_{f}}{e^{(\hat{\alpha}_{0}+\hat{\lambda}_{1}x)}}+t\bigg{)}}\right] \tag{8}\]  
where \(\Phi[\cdot]\) is the CDF of the standard normal distribution. The CDF \(F(t;\lambda,\mu)\) of the general IG distribution can be expressed as \(F(t;\lambda,\mu)=\Phi(z)=\Phi\left(\sqrt{\frac{\hat{\lambda}}{t}}\Big{(}\frac {t}{\mu}-1\Big{)}\right)\). To simplify the calculation of the CDF of the IG distribution, the properties and calculation methods of the standard normal distribution can be used to analyze and assess the storage life of products. Formula (8) uses the mathematical connection between the IG distribution and the normal distribution to simplify the calculation. Therefore, the reliability function is as follows:  
\[ \begin{array}{ll}R(t)&=P(T_{D}>t)=P(Y(t)\leq D)=1-F_{T_{D}}(t)\\ &=\Phi\bigg{[}\sqrt{\frac{\lambda}{D}\Big{(}\frac{D}{e^{\delta_{0}+\delta_{1} \gamma}}-t\Big{)}}\bigg{]}+\exp\Big{(}\frac{2\lambda t}{e^{\delta_{0}+\delta_{ 1}\gamma}}\Big{)}\Phi\bigg{[}-\sqrt{\frac{\lambda}{D}\Big{(}\frac{D}{e^{ \delta_{0}+\delta_{1}\gamma}}+t\Big{)}}+t\Big{)}\bigg{]}\end{array}  \tag{9}\]
# Road-Side Unit Anomaly Detection
## 2 State of the Art
### Anomaly Detection
EVAD is a method proposed in [17] to detect anomalies in vehicles using edge computing, wherein a correlation between sensor variables is drawn and used for anomaly detection where, for an observation, if two supposedly correlated variables do not correlate, an anomaly is detected. In [18], a CNN is used to extract the spatial, temporal, and spatio-temporal traffic features then used for anomaly detection by the means of employing thresholds. In [19], an anomaly detection approach that takes into account the spatio-temporal features of VANET traffic is proposed. The approach consists of two phases; first, deep learning based on a CNN architecture is used for network traffic estimation; second, a decision-making approach based on reinforcement learning is used to identify the normal and anomalous traffic entries. When it comes to security anomalies, DAMASCO [20] is a security-based system that aims to detect DoS attacks. A statistical approach was used to detect anomalies in vehicular communications where the MAC layer was addressed and assessed to identify potentially malicious nodes by the number of sent packets and block their activity. In [21], a certification-less authentication method was adopted, wherein the RSUs are only trusted partially and are granted the vehicle's information only partially. Moreover, anomaly detection was performed through clustering; precisely, the agglomerative clustering was used on the traffic data represented as time series using the dynamic time wrap distance [22]. In [23], a methodology was proposed to analyze data collected from agriculture areas during many years and verify the anomalies on the productions and their relationship with weathers conditions. In [24] a Mobile phone Network Data (MNF) based framework to detect anomalies in real time was proposed. The framework is based on two steps, the first one is an offline unsupervised learning done on the MND, the second step is the online real time detection of anomalies. To the best of our knowledge, only a few works have focused on V2I fault detection and we are not aware of any work that proposes an online method for the fault detection of RSUs.
# Exploiting the Value of Active and Multifunctional Facade Technology through the IoT and AI
## 3 The Multifunctional Facade Concept
Within the framework of RenoZEB and MEZeroE projects, a MF with integrated heating, cooling, and ventilation system technology has been investigated and developed [25; 33; 39]. If, on the one hand, these projects have shown how technical issues can be managed for real applications [11], on the other, it is evident how far the commercialization of such products has come. Indeed, achieving a fully integrated MF product means redesigning both technologies and the entire supply chain. All these experiences have demonstrated that MF technology represents a niche of the facade industry. Indeed, despite ongoing efforts toward developing fully integrated systems, practical application experiences remain limited to prototypes and case study demonstrations [11]. As highlighted in the white paper [17], there still is no market for \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**EU Project** & **Project Year** & **MF Technology Description** & **TLR** & **Website Ref.** \\ \hline Plug-N-Harvest & 2017–2022 & Plug-N-Harvest develops a plug-and-play module for building retrofit including a PV module, battery, thermal collector, electric heat, and automatic ventilation. & TLR5 & [31] \\ \hline Energy Matching & 2017–2022 & Energy Matching develops a BIPV module with high aesthetical value and flexibility. & TLR9 & [32] \\ \hline RenoZEB & 2017–2022 & RenoZEB will provide a cost-effective plug-and-play facade integrating ventilation systems, solar and PV, automated blinds, and sensors. & TLR9 & [33] \\ \hline PowerSkin Plus & 2019–2024 & PowerSkin Plus combines enhanced insulation and renewable energy technology based on photovoltaics modular solutions. & TLR8 & [34] \\ \hline ENsnare & 2021–2025 & The ENSNARE project will strive to increase the implementation of renovation through digitalization. A MF with BIPV, PV, a roll-bond solar collector, thermal batteries, and windows will be developed. & n.a. & [35] \\ \hline MEZeroE & 2021–2026 & MEZeroE will focus on developing nZEBs. A MF is developed integrating mechanical ventilation, heating and cooling systems, automated blinds, and window sensors. & TLR8 & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the widespread deployment of active multifunctional systems, as clients and designers do not recognize their value. This implies that the benefits of MF systems in terms of energy savings and comfort provided need to be demonstrated to attract greater market interest. In the proposed projects, the focus on sensor integration mainly involves monitoring the performance of integrated technologies. Only in RenoZEB and MEZeroE is strict reference made to the use of such sensors for the regulation of active systems. From this perspective, the use of IoT sensors in combination with AI algorithmic control systems remains an open issue with great room for improvement. Although the topic is relevant in the research landscape for building energy optimization and IEQ, this topic has not yet been analyzed in relation to complex envelope systems.
###### Abstract
However, there is only comparable evidence of spatial perception in young healthy adults from a single study which used a 3D motion analysis system [8], considering participants standing blindfolded on a rotating platform which moved randomly to different angles in clockwise and anti-clockwise directions. The results showed that subjects who were at risk consistently overestimated the amplitude of rotation which they typically, consistently duplicated with their own estimation. The authors concluded that their findings highlighted the importance of dynamic motion in the appropriate perception of spatial orientation. More recent studies have suggested monitoring of turning behavior using Inertial Measurement Units (IMUs) as a solution to assess gait, turning and balance in older adults [7, 9, 10], as these can be used outside of the laboratory setting. This current study used IMUs to assess the movements of different body segments which provided linear accelerations and Euler segment orientations during turning, which allows the exploration of the modulation of turn amplitude on turning characteristics and the spatiotemporal relationships between the body segments. However, there is a paucity of research that has examined and reported the effect of the amplitudes of turning on whole-body coordination in healthy individuals. Therefore, the aim of this present study was to clarify the effects of turning amplitude on turning characteristics in older adults in comparison to younger adults, which may be related to the risk of falling, which have the potential to be utilized to guide fall prevention strategies, particularly for older adults. Our hypotheses were that changes in turning amplitude affect coordination of the whole-body and stepping characteristics in healthy individuals, and that older adults would adapt their turning strategy during amplitude perturbation, which causes systematic changes in turning behavior. In addition, this protocol should be used when determining the impact of turning on overall coordination, specifically with regard to the diagnosis of turning deficits in older adults. When the proficiency of turning is examined with a focus on segmental independence specific exercises may be recommended on an individual basis to prevent falls.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 6 Conclusions  
This study presents ELP-Net, a novel location prediction model for wargaming that integrates graph learning and transformer-based temporal modeling to improve the accuracy of opponent location forecasting. By leveraging a spatial-temporal GNN module and a transformer-based sequence learning framework, ELP-Net effectively mitigates the challenges posed by data sparsity, high redundancy, and dynamic opponent strategies.  
To promote further research and practical deployment, we have publicly released the source code of ELP-Net on GitHub (URL provided in Appendix B). Future research directions could include the following: expanding ELP-Net to real-world military and security applications, optimizing the model for real-time inference, and exploring reinforcement learning for adaptive decision making.  
By addressing these challenges and expanding its scope, ELP-Net contributes to intelligent decision making in complex adversarial environments, bridging the gap between AI-driven strategic reasoning and real-world wargaming applications.  
Conceptualization, D.L. and J.Y.; methodology, D.L. and J.L.; software, J.L.; validation, J.L.; formal analysis, D.L.; investigation, D.L. and J.L.; resources, J.Y.; data curation, J.L.; writing--original draft preparation, D.L. and J.L.; writing--review and editing, D.L. and J.Y.; visualization, J.L.; supervision, J.Y.; project administration, D.L.; funding acquisition, D.L. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Postdoctoral Researcher Support Program of China (grant number GZC20242191); the Major Program of National Natural Science Foundation of China (grant numbers 12292980, 12292984, and 12031016); Tianyuan Fund of the National Natural Science Foundation of China (grant number 12426529); the National Key R&D Program of China (grant numbers 2023 YFA1009000, 2023 YFA1009004, 2020 YFA0712203, and 2020 YFA0712201); the Beijing Natural Science Foundation (grant number BNSF-Z210003); and the Department of Science, Technology and Information of the Ministry of Education (grant number 8091B042240).  
Not applicable.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
## 3 Proposed Solution
### Distributed Anchor Design
However, solving this problem is challenging; as the network size grows, the number of possible Anchor placements increases exponentially, meaning that finding an optimal solution in polynomial time is unlikely, making it an NP-hard problem. As a result, most researchers have focused on developing heuristic algorithms [29] and simulation tools [30] that can find near-optimal solutions in a reasonable amount of time. To determine the optimal Anchor placements that minimize the path stretch, we propose a method that involves partitioning the network into clusters based on network topology and selecting a router or Access point as an Anchor for each cluster based on its proximity to all routers and Access points within the cluster and its closeness to Anchors in other clusters to minimize intra-cluster and inter-cluster communication distances. There are numerous clustering algorithms that can be applied to partitioning the network. Each algorithm has its own strengths and weaknesses, and some do not guarantee convergence to the optimal solution. A comprehensive survey of clustering algorithms can be found in [31]. Our proposed method uses an iterative approach based on a simple and efficient clustering algorithm, namely the K-Medoids algorithm [32]. It starts by selecting a k router as an initial cluster center router (medoids). The quality of this initial cluster is iteratively improved through a local search process based on minimizing both the distance between any cluster's router and medoid and the distance between medoids. During each iteration, center routers are replaced by non-center ones, a process that continues until the intra-cluster and inter-cluster distances are reduced. However, the quality of the final clustering solution of our method may not usually be optimal since it is sensitive to the initial selection of the center cluster. Therefore, our method is executed multiple times with different random initialization. We then choose the optimal solution as Anchor placements. It is worth noting that determining the optimal number of Anchors for a given network is a complex problem that is out of the scope of this paper.
# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 2 Our Approach
### 3D Convolutional Neural Networks  
Deep neural networks, like CNN, have shown significant performance in a variety of applications, including audio and image. However, it has not been examined for the multi-label earthquake classification task. A CNN comprises layers with filters, frequently referred to as kernels, seeking specific characteristics in the earthquake signals. 3D-CNN [30], having the above filters represented in a three-dimensional matrix, extract spatial and temporal information and identify patterns in the input. During periods of in  
Figure 2: The proposed architecture of 3D-CNN-RNN for earthquake magnitudes categorization. The above design employs a composition of 3 convolutional layers and 16 distinct GRUs for every filter in the subsequent layers. Each feature map from the previous layer is given to 32 GRU cells in the 16 recurrent layers. The sigmoid output layer serves as a final fully connected (FC) layer for categorizing earthquakes. The input consists of a series of 10-s seismic activity samples.  
tense seismic activity, it is highly probable that the changing behavior of the signal is likely to be understood in the temporal domain, which inspired us to implement 3D-CNN by convolving a kernel having three dimensions. We produce these convolutions in this work by stacking numerous neighboring spectrograms of a 60-second ground motion divided into a 10-s frame. In a conventional setting of convolutional layer, operations are carried out in three stages: firstly, we perform convolution operation using 3D kernels to create matrix transformations linearly; secondly, each linear matrix convolution passes through spatial and temporal features in the spectrogram by employing a non-linear activation (ReLU) function; and finally, the feature maps are downsampled using a pooling operation. Various combinations of these essential basic components may be used to create the CNN.  
The value of any element at position \((x,y,z)\) in the \(j\)th feature space of the \(i\)th layer is represented by \(u_{ij}^{x,y,z}\), and defined as,  
\[u_{ij}^{x,y,z}=g\Bigg{(}b_{ij}+\sum_{m}\sum_{p=0}^{P_{1}-1}\sum_{q=0}^{Q_{i}-1 }\sum_{r=0}^{R_{i}-1}w_{ijm}^{p,q,r}u_{(i-1)m}^{(x+p)(y+q)(z+r)}\Bigg{)}, \tag{1}\]  
where \(g\) is the activation function, \(b_{ij}\) is a bias for the feature space, \(R_{i}\) is the size of the 3-dimensional filter along the time axis, \(w_{ijm}^{p,q,r}\) is the \((p,q,r)\)th value of the filter associated to \(m\)th feature in the former layer.