esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance-Constraint Fault Tolerant Control to Aircraft in Presence of Actuator Deviation
## 4 FTC-FT Controller Applied on the Unmanned Helicopter
\begin{split}&\dot{V}_{2}(\tilde{\mathbf{x}}_{2},\mathbf{\
abla}(\tilde{ \mathbf{x}}_{2}))\\ &=\tilde{\mathbf{x}}_{2}^{T}\mathbf{R}(\tilde{\mathbf{x}}_{2})\big{(}f_{2}+( \mathbf{g}_{2}+\Delta\mathbf{g}_{2})\delta+\tilde{\mathbf{\xi}}_{c}(t)-\tilde{\mathbf{\pi}}_{1} \big{)}+\mathbf{\
abla}(\tilde{\mathbf{x}}_{2})^{T}\mathbf{\
abla}(\tilde{\mathbf{x}}_{2})\\ &\leqslant-k_{2,w}\frac{\tilde{w}^{2}}{k_{w}^{2}-\tilde{w}^{2}}- k_{2,r}\frac{\tilde{r}^{2}}{k_{r}^{2}-\tilde{r}^{2}}-\left(\frac{\tilde{w}^{2}}{k_{w }^{2}-\tilde{w}^{2}}\right)^{\mu}-\left(\frac{\tilde{r}^{2}}{k_{r}^{2}-\tilde{ r}^{2}}\right)^{\mu}\\ &\quad-\eta\|\mathbf{\
abla}\|^{2}-\|\mathbf{\Psi}\|^{2\mu}-\frac{1}{2}\| \mathbf{g}_{1}\tilde{\mathbf{x}}_{2}\|^{2}+\frac{1}{2\gamma^{2}}\|\tilde{\mathbf{\xi}}_{c} (t)\|^{2}\\ &\leqslant-a_{2}V_{2}-2^{\mu}V_{2}^{\mu}-\frac{1}{2}\|\mathbf{g}_{1} \tilde{\mathbf{x}}_{2}\|^{2}+\frac{1}{2\gamma^{2}}\|\tilde{\mathbf{\xi}}_{c}(t)\|^{2} \end{split}  \tag{38}\]  
where \(a_{2}=2\min\{k_{2,w},k_{2,r},\eta\}\), system BL function is given as  
\[V(\tilde{\mathbf{x}}_{1},\tilde{\mathbf{x}}_{2},\mathbf{\
abla}(\tilde{\mathbf{x}}_{2}))=V_{1 }+V_{2} \tag{39}\]  
easily, we get  
\[\dot{V}\leqslant-\sigma V-2^{\mu}V^{\mu}+\varepsilon \tag{40}\]  
where \(\sigma=2\min\{k_{1,\mu},k_{1,\varphi},k_{2,w},k_{2,r},\eta\}\), \(\gamma>0\), \(\varepsilon=\frac{1}{2\gamma^{2}}\|\xi_{c}(t)\|^{2}\). Obviously, this closed-loop system is practical finite-time-convergence stability based on Theorem 1, and tracking errors are bounded \(|\tilde{h}|<k_{\tilde{h}}\), \(|\tilde{w}|<k_{\tilde{h}}\), so \(|h|<k_{\tilde{h}}+h_{d}\), \(|w|<k_{\tilde{h}}+|\kappa_{1}(1)|,|r|<k_{r}+|\kappa_{1}(2)|\). Thus, the yaw-vertical channel closed-loop system with actuator output deviation is regulated by FTC-FT controller. In next section, detailed simulation will be carried out to verify the validity and superiority of proposed method.
## 5 Conclusions  
This study focuses on the large mining areas of the Dongguanshan copper deposit, using the geological conditions and mechanical parameters of the ore body as a basis. A three-dimensional model of the mining area was established using FLAC3D numerical simulation software, and a detailed analysis of the stress distribution and displacement changes under different mining schemes was conducted. The following conclusions were drawn:  
1. Stress conditions: The filling body can improve the distribution of stress to some extent; however, it cannot alter the overall trend of stress transfer. The filling body itself cannot bear most of the transferred stress, which remains supported by the surrounding rock with a higher strength. In the second-step mining area, the adjacent filling body cannot bear the transferred stress, resulting in significantly higher maximum values of tensile, compressive, and shear stresses compared to the first-step mining area.
2. Displacement and plastic zone distribution: The displacement values and the distribution of plastic zones in the second-step mining area are notably higher than those in the first-step mining area. The displacement distribution of the roof in the second-step mining area shows relatively gentle numerical changes and uniform subsidence. Although there are plastic zones in the roof, they do not penetrate the entire roof. Considering the dynamic load caused by blasting vibrations, it is essential to control the scale of the maximum amount of explosives during blasting in the second-step mining area to minimize the impact of blasting on the mining area and the surrounding filling body.
3. Conventional mining methods are prone to stress concentration in the rock and increased roof displacement, making them unsuitable for large mining areas in complex geological conditions. Various optimized mining schemes were proposed and validated. The scheme of \"reducing the intermediate dimensions of the first-step mining area\" significantly reduces the displacement of the rock on both sides of the void and alleviates the stress concentration, effectively enhancing the stability of the void. The second-step mining should adopt a gradual approach, progressively mining from one side to the other, to minimize the stress concentration and displacement changes in the mining area and ensure the stability of the void.  
Conceptualization, Q.Z. and M.H.; methodology, Q.Z.; resources, M.H.; writing--original draft preparation, M.H.; writing--review and editing, Q.Z. and J.G.; visualization, M.H.; and funding acquisition, J.G. All authors have read and agreed to the published version of this manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 52174140).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
Author [PERSON] was employed by the company Hunan Lianshao Construction Engineering (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Results
### Water Absorption  
Concrete with high permeability can assist chemicals that are harmful to pierce and react with cement-based material ingredients to alter its properties. Water absorption of concrete samples was measured at 28 and 90 days of curing, as shown in Figure 8.  
Figure 7: Concrete Mass Loss Due to Sulfuric Acid (%).  
It is observed that water absorption is affected negatively by increasing the doses of recycled concrete aggregate. Water absorption is increased by 8%, at 50% recycled concrete aggregate and 22% at 100% recycled concrete aggregate. This is attributed to the existence of adhered mortar which had low density and a lot of ITZs in the recycled concrete aggregate which assists easy water penetration. Adding silica fume assists in decreasing the concrete water absorption for a given dose of recycled concrete aggregate. Water decreases by 10% when silica fume is utilized as a partial substitute for cement. This can be attributed to the filler effect of the silica fume particles. In addition, silica fume consumes C-H in pozzolanic reaction to form C-S-H gel, which could also reduce the microchannels connectivity within the binder matrix. Silica fume also assists in decreasing the water absorption of a concrete sample utilizing recycled concrete aggregate--such as RCA50-SF20 exhibits water absorption similar to the reference sample at 28 and 90 days of curing. However, a considerable decrease is also observed in the water absorption of concrete with 100% recycled concrete aggregate.  
The utilization of nylon fibers increases water absorption by 3-6%. Nylon fiber reinforced concrete samples have marginally more water absorption as compared to plain concrete samples. This could be attributed to enhanced microchannels connectivity within the binder matrix and concrete outer surface. Researches have also revealed that concrete [11] sorptivity, and water absorption [32] increases with adding fibers because of the increase in microchannel length in the microstructure. Adding silica fume not only decreases plain concrete water absorption but also reduces nylon fiber-reinforced concrete water absorption. Such as, RCA0-SF20-NFRC has low water absorption as compared to RCA0-SF0-PC and RCA0-SF0-NFRC mixes. Fibers enhanced bond because of silica fume adding could also assist in decreasing the pore volume connectivity which decreases concrete water absorption capacity. It could be settled that adding silica fume can recompense for durability loss because of the utilization of both nylon fibers and recycled concrete aggregate.
# TCNformer Model for Photovoltaic Power Prediction
## 3 Methodology
### Variable Selection (VS) Module  
Combined with the information shown in Figures 1 and 2, the historical data of photovoltaic power not only have timing features in the short term, but they also have certain timing features over the long term. Considering the length of the long-term cycle (as shown in Figure 2, the cycle is close to 365 days) and the subsequent optimization problems, it is difficult for the traditional model to capture these timing features at the same time. So, we designed a VS module to divide the input sequence into three dimensions through preliminary analysis and selection of the historical data. Then, the results from the VS module are transferred to the LSTFE module for feature fusion.  
\[d_{t},d_{s},d_{t}=VariableSelection(data,input) \tag{5}\]  
In the formula, \(data\in\mathbb{R}^{I\times n}\), \(d_{l}\in\mathbb{R}^{I_{l}\times n_{l}}\), \(d_{s}\in\mathbb{R}^{I_{s}\times n_{s}}\), \(d_{t}\in\mathbb{R}^{I\times n_{t}}\) respectively represent preprocessed raw data, month-level time series data, week-level time series data, and day-level time series data. \(n\), \(n_{l}\), \(n_{s}\) and \(n_{t}\) respectively represent the number of influencing factors. \(VariableSelection(\cdot)\) represents the VS module, and the specific calculation method is as follows.  
Photovoltaic power data often show strong time series features. Although the volatility is strong, they still have a certain periodicity over a longer time range. In this paper, the Fourier transform decomposition curve of photovoltaic power data and its influencing factor data are selected for periodicity analysis [32] in order to obtain the fluctuation periods of different periodic curves and to provide a certain degree of reference for the analysis of photovoltaic power prediction. The formula of the Fourier transform is as follows:  
\[X(k)=\sum_{n=0}^{N-1}x(n)W_{N}^{nK},k=0,1,\ldots,N-1 \tag{6}\]  
\[x(n)=(1/N)\sum_{n=0}^{N-1}X(k)W_{N}^{-nK},k=0,\ldots,N-1 \tag{7}\]  
\[W_{N}^{nK}=e^{-j(2\pi/N)kn} \tag{8}\]  
\(X(k)\) represents the Fourier series, \(x(n)\) represents the Fourier coefficient, \(W_{N}^{nK}\) represents the complex function, \(k\) represents the \(x\) coordinate in the frequency domain, and \(N\) represents the period.  
Photovoltaic power is correlated with a large number of weather factors, especially the strong correlation between solar radiation intensity and photovoltaic power. In this study,  
Figure 3: The structure of TCNformer model.  
the Pearson correlation coefficient was selected for correlation analysis, and the calculation formula is as follows:  
\[P_{x,y}=\frac{cov(x,y)}{\sigma_{x}\sigma_{y}}=\frac{E[(x_{i}-\overline{x})(y_{i} -\overline{y})]}{\sigma_{x}\sigma_{y}} \tag{9}\]  
The VS module processes the month-level time series data, week-level time series data, and day-level time series data according to the analytical results of correlation and periodicity.
## 4 Illustrated Case
### Availability Analysis of the FMS  
The availability of an FMS can be defined as the ability of the manufacturing system to meet production demands in accordance with specified conditions and time interval [16]. Therefore, the availability of FMS depends on the relation between the output performance and the desired level of this performance-demand, which is determined outside the system. In a general case, demand \(W(t)\) is also a random process that can take discrete values from the set \(\mathbf{w}=\{w_{1},\dots,w_{M}\}\). The desired relation between the system performance and the demand at any time instant \(t\) can be expressed by the acceptability function \(\Phi(G(t),W(t))\). The acceptable system states correspond to \(\Phi(G(t),W(t))\geq 0\) and the unacceptable states correspond to \(\Phi(G(t),W(t))<0\). The last inequality defines the MSS failure criterion. In many practical cases, the MSS performance should be equal to or exceed the demand. Therefore, in such cases, the acceptability function takes the following form:  
\[\Phi(G(t),W(t))=G(t)-W(t), \tag{29}\]  
and the criterion of state acceptability can be expressed as  
\[\Phi(G(t),W(t))=G(t)-W(t)\geq 0. \tag{30}\]  
In the FMS, without loss of generality, we assume that the required demand level is constant \(W(t)\equiv w\) and all system states with performance greater than or equal to \(w\) corresponds to the set  
Figure 7: Productivities of product manufactured by the whole FMS.  
of acceptable states, and all system states with performance lower than \(w\) correspond to the set of unacceptable states.  
Then combined with Equation (4), the instantaneous availability \(A(t)\) of the FMS is the probability that the FMS at instant \(t>0\) is in one of the acceptable states.  
\[A(t)=\sum_{\mathbb{g}\geq w}p_{k}(t). \tag{31}\]  
Consequently, the system of equations of the instantaneous availability of the FMS in different demand is expressed as follows:  
\[\left\{\begin{array}{l}A_{1}(t)=P_{1}^{\text{FMS}}(t),w_{1}=18\\ A_{2}(t)=\sum_{i=1}^{3}P_{i}^{\text{FMS}}(t),w_{2}=16\\ A_{3}(t)=\sum_{i=1}^{4}P_{i}^{\text{FMS}}(t),w_{3}=12\\ A_{4}(t)=\sum_{i=1}^{4}P_{i}^{\text{FMS}}(t),w_{4}=10\end{array}\right., \tag{32}\]  
and the instantaneous availability of the FMS in different demand shown as follows:  
As shown in Figure 8, the instantaneous availability of the FMS for different demands can be obtained according to the Lz-transform method, which helps the manufacturers to design the best production plan in line with customer's order requirements. It should be noted that the availability results are calculated under the constraints that setup changes occur randomly and the machine synchronization rule. Consequently, the Lz-transform can not only be used to evaluate the output of products manufactured by FMS but also to evaluate the system availability effectively in accordance with the changes in product demand.
## 3 Results and Discussion
### Diurnal Variation Characteristics
Additionally, the strong-echo signal above 3 km disappears, and the DBS detection range is reduced to about 3 km. From 14:00 to 18:00, with the enhancement of thermal effects, active vertical airflows lead to better dispersion of particles. Additionally, boundary layer lifting may allow the DBS detection height to increase, eventually reaching the maximum detection range continuously. Near-surface echo signals intensify, increasing the PPI detection range. After 19:00, strong and persistent upward airflows, combined with surface winds, rapidly transport near-surface particles to higher altitudes, which are more evenly distributed over a larger vertical range. The strong-echo region gradually ascends to higher altitudes, significantly enhancing vertical particle diffusion. This provides favorable conditions for the DBS detection range to remain at the maximum of 4.6 km while also improving the detection range of the PPI mode.
## 2 Related Work
### Feature Maps and Feature Spaces  
Feature maps and feature spaces share some similarities: CNN-based FSR methods utilize convolution layers to project LR images into inner \"feature maps\" and then into HR ones. Meanwhile, traditional manifold learning-based FSR methods project LR images into \"feature spaces\" and then into HR ones, assuming that both LR and HR spaces share the same local geometry [46]. Many traditional FSR methods have been introduced based on the manifold learning assumption [47; 48; 49], aiming to enhance the LR and HR space relationship. However, the manifold learning technique has gained less attention with the rise of CNNs because complicated CNN structures are challenging to deliberate. To bridge the gap between CNNs and manifold learning, several deep learning-based FSR methods have been proposed. [PERSON] et al. [50] introduced a manifold localized deep external compensation (MALDEC) network that provides accurate localization and mapping to the HR manifold by referring to the big data online. [PERSON] et al. [51] searched the HR manifold space to find images that match the original LR image and then applied a generative model for image reconstruction by feeding it the downscaling loss. [PERSON] et al. [52] introduced an LR and HR space homogenization projection to formulate FSR in a multi-stage framework. [PERSON] et al. [16] developed a closed-loop dual regression network (DRN) with an additional constraint, claiming that limiting mapping spaces would be advantageous for image super-resolution. The methods mentioned above tried to combine the CNN-based method with the manifold learning technique. However, they overlook the vital role of inner feature maps, which restricts the image super-resolution performance. Therefore, determining how to effectively handling inner feature maps, such as a guiding block that increases feature map correlations, is vital for a high-quality image reconstruction process.
# Multimodal Natural Disaster Scene Recognition with Integrated Large Model and Mamba
## 2 Related Work  
### Large-Scale Pre-Trained Models  
Prior to the rise of large-scale pre-trained models, natural language processing (NLP) and computer vision tasks primarily relied on feature engineering and traditional machine learning methods. These approaches required the manual design of features by experts, such as Bag of Words (BoW) [16] and TF-IDF [17] for text processing and SIFT [18] and HOG [19] for image processing. While these techniques achieved some early success, they were heavily dependent on hand-crafted features, limiting their scalability to more complex tasks. The advent of recurrent neural networks (RNNs) and their enhanced variants, long short-term memory (LSTM) networks, enabled the automatic learning of temporal relationships and contextual information in sequences, advancing the capability to handle complex tasks. However, these models still faced limitations related to long-term dependencies and training efficiency [20].  
In 2017, Google introduced the Transformer architecture, which leveraged self-attention mechanisms to overcome the sequential processing constraints of RNNs [21]. This breakthrough enabled the efficient parallel processing of input data, laying the foundation for the development of large-scale pre-trained models. Today, pre-trained models can be broadly classified into large language models (LLMs) [6] and vision language models (VLMs) [22].  
LLMs are designed specifically for NLP tasks and are pre-trained on vast amounts of text data to generate high-quality language representations, which can then be fine-tuned for a wide range of downstream tasks. Common examples of LLMs include the GPT series [23] and the Text-To-Text Transfer Transformer (T5) [24]. VLMs, on the other hand, are capable of processing both visual and linguistic information, making them ideal for cross-modal tasks such as image captioning and visual question answering (VQA). Notable VLMs include DALL-E [25], BLIP (Bootstrapping Language-Image Pretraining) [26], and Florence [27].  
Both LLMs and VLMs have demonstrated remarkable success in pre-trained tasks, being widely applied in text generation, image synthesis, and VQA. In this paper, we leverage the widely used large model Gemini 1.5 in a question-answering framework to generate detailed descriptive text and category probabilities for natural disaster images, thereby obtaining bimodal features.
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 3 State Space Iterating (SSI) Scheme
### Basic Ideas of SSI  
As explained in Section 3.1.2, one can find an LCO solution for a system by finding the fixed point of a mapping \(F\). Suppose that the system has \(n_{L}\) LCOs. Since each LCO \(\mathbf{x}_{L,j}\) is an attractor and is associated with a basin of attraction \(U_{L,j}\), according to Equation (6), we could define a set \(X_{L}\) that collects all LCOs and a set \(\overline{U}_{L}\) that contains all basins of attraction--that is, \(X_{L}=\{\mathbf{x}_{L}|\ \mathbf{x}_{L}-F(\mathbf{x}_{L})=0\}\) and \(\overline{U}_{L}=\cup_{j=1}^{n_{L}}U_{L,j}\). Our final goal is to find \(X_{L}\) and distinguish each separate point \(\mathbf{x}_{L}\) in \(X_{L}\).  
Two basic conceptions thus emerge: (1) If we use the Poincare map \(P\) as \(F\), there is no method for finding fixed points easily; if we use a Lorenz map \(L\) as \(F\), although it is easy to find fixed points via a visual inspection of Figure 4, the results highly depend on the accuracy of the local maxima. Since we have already used the Henon-RK45 method to move system states accurately into discontinuous boundaries, we expect to find a way to use the states that we already have instead of spending additional and considerable time to find local maxima. Therefore, we need to construct a new mapping \(F\) instead of using \(P\) or \(L\) directly.  
(2) If the set \(X_{L}\) is extremely difficult to obtain, we can initially find the basins of attraction \(\overline{U}_{L}\) because we know that \(X_{L}\subseteq\overline{U}_{L}\) according to Equation (6). Moreover, if \(\overline{U}_{L}\) is still too difficult obtain, then we can try to find an iteration in state space:  
\[\overline{U}_{L}\supseteq\mathbf{X}_{0}^{\{1\}}\supseteq\mathbf{X}_{0}^{\{2 \}}\supseteq\cdots\supseteq\mathbf{X}_{0}^{\{n\}}\supseteq X_{L} \tag{8}\]  
We can start with any \(\mathbf{X}_{0}^{\{j\}}\) that could possibly be obtained, confirm that \(\overline{U}_{L}\supseteq\mathbf{X}_{0}^{\{j\}}\supseteq X_{L}\), and then try to contract \(\mathbf{X}_{0}^{\{j\}}\) by finding another set \(\mathbf{X}_{0}^{\{j+1\}}\) which would satisfy \(\overline{U}_{L}\supseteq\mathbf{X}_{0}^{\{j\}}\supseteq\mathbf{X}_{0}^{\{j+ 1\}}\supseteq X_{L}\). Thus, an iteration \(\mathbf{X}_{0}^{\{j\}}\rightarrow\mathbf{X}_{0}^{\{j+1\}}\) in state space needs to be constructed, which may need some additional results from time integration. Finally, when we find \(X_{L}\) and the system patterns are clear enough, we can read all \(\mathbf{x}_{L}\) values easily from a figure which is similar to Figure 4.  
Thus, we have two goals: (1) design a mapping \(F\) and (2) construct the iteration procedure shown in Equation (8), which will be implemented in Sections 3.3 and 3.4, respectively.
## 3 Experiment
### Communication Delay Measurement and Teleoperation Setups  
Figure 7 shows a teleoperation site for the communication delay measurement. The total area of the site is 3000 m\({}^{2}\), and the one-way length of the course is 193.5 m. One-way communication delay samples were collected continuously from a remote vehicle for 30 min, and the collection interval was set to a maximum of 40 Hz. The vehicle was safely driven at a constant speed of 10 km/h or less considering stopping distance and delay time and traveled a total of 25 laps.  
Figures 8 and 9 show the remote vehicle configuration and experimental setup scene. The remote vehicle uses a CAN (Controller Area Network) to connect lower-level controller terminals and has a communication speed of 250 kbps. In teleoperation, video transmission and reception performance are critical and conducted in an environment that can receive video at the control center using transmission and reception terminals that support video compression and low-latency camera terminals.  
Figure 6: Experimental setups between the remote vehicle and the control center using wireless internet.  
Figure 7: Teleoperation course of the test site (Seonam division, Gwangju, Republic of Korea). The total area of the site is 3000 m\({}^{2}\), and the one-way length of the course is 193.5 m.