esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
The background of classic reinforcement learning is a Markov decision process (MDP), defined by a tuple \((\mathbf{S},\mathbf{A},\pi,r,P,\gamma)\), where \(\mathbf{S}\) is a state set, \(\mathbf{A}\) is an action set, \(\pi:\mathbf{S}\rightarrow\mathbb{R}\) is a policy, \(r:\mathbf{S}\times\mathbf{A}\rightarrow\mathbb{R}\) is a reward function, \(P:\mathbf{S}\times\mathbf{A}\times\mathbf{S}\rightarrow\mathbb{R}\) is a transition dynamic, and \(\gamma\in(0,1)\) is a discount factor. When the agent interacts with the environment using policy \(\pi\), a trajectory sequence \(\tau:\{s_{0},a_{0},r_{0},s_{1},a_{1},r_{1},\cdots,s_{T},a_{T},r_{T}\}\) is rolled out, where \(T\) is the length of \(\tau\). Discounted accumulated rewards \(R(\tau)\) can be written as:  
\[R(\tau)=\sum_{t=0}^{T}\gamma^{t}r_{t}(s_{t},a_{t}),0<\gamma<1, \tag{1}\]  
where \(a_{t}\sim\pi(a_{t}|s_{t})\), \(s_{t+1}\sim P(s_{t+1}|s_{t},a_{t})\), and \"\(|\)\" is a symbol for conditional probability. Value function \(V^{\pi}(s_{t})\), state-action value function \(Q^{\pi}(s_{t},a_{t})\), and advantage function \(A^{\pi}(s_{t},a_{t})\) are defined as:  
\[V^{\pi}(s_{t})=\mathrm{E}[\sum_{k=t}^{T}\gamma^{k-t}r_{t}(s_{t},a_{t})|s=s_{t}; \pi], \tag{2}\]  
\[Q^{\pi}(s_{t},a_{t})=\mathrm{E}[\sum_{k=t}^{T}\gamma^{k-t}r_{t}(s_{t},a_{t})|S= s_{t},A=a_{t};\pi], \tag{3}\]  
\[A^{\pi}(s_{t},a_{t})=Q^{\pi}(s_{t},a_{t})-V^{\pi}(s_{t}). \tag{4}\]  
The policy is optimized by maximizing the accumulated rewards \(R(\tau)\).  
Most common robot control tasks such as manipulation or navigation tasks require a robot to reach a desired state from an initial state. As a result, in this paper, we focus robot control policy learning on different forms of reaching tasks in relatively complicated environments with obstacles, which are still challenging and also the stepping stones to more complex tasks. Figure 1 shows the learning pipeline of our proposed method, which includes a real-to-sim training phase and a sim-to-real inference phase, as shown in Figure 1a,b, respectively. To make the content of this paper more concise and compact, by default, we mainly take the manipulation task as an example to illustrate our method. At the real-to-sim training phase, firstly, a semantic image is segmented from an RGB image of a real-world working scenario. Coordinate transformation maps each pixel position from the image coordinate system to the robot coordinate system. Then, a task-relevant simulated environment is generated based on the semantic information and coordinate transformation. Finally, a control policy is learned with the DRL method in the constructed simulated environment. At the sim-to-real inference phase, simulated-like synthetic images are generated based on the semantic images of the real world. The trained policy takes the synthetic images as input and outputs actions that thus directly control the real-world robot to perform the desired task.
## 3 Results  
### Enhanced \(\upmu\)CT Imaging and Deblurring  
Varying currents (all other parameters fixed, 70 kVp, no frame averaging) demonstrated that lower currents improved the visualization of trabecular edges. Increasing frame averaging using a fixed low current improved the SNR. Increased voltage improved the contrast between _BaSO4_ and bone while maintaining low current and high frame averaging. Tuned \(\upmu\)CT parameters (90 kVp, 44 \(\upmu\)A, 0.5 mm Al filter, 200 ms integration time, 8 frame averaging, 4.9 \(\upmu\)m voxel spacing) enhanced visualization of the damaged regions compared to the baseline \(\upmu\)CT scan parameters. Table 1 shows a summary of the baseline and enhanced \(\upmu\)CT acquisition parameters. The current was kept low enough for the tube to operate with a small focal spot size to yield sharp images. Due to the limitations with respect to the current, frame averaging was increased to improve damage visualization. Each scan took 2.1 h collecting images for a volume of \(\sim\)500 \(\upmu\)m in height. The enhanced \(\upmu\)CT images show microdamage that is obscured by reduced resolution and low contrast in the baseline \(\upmu\)CT images (Figure 3). The deblurring algorithm visually sharpened the regions of the _BaSO4_ contrast agent seen in the enhanced \(\upmu\)CT images and identified smaller areas of _BaSO4_ not picked up in the enhanced image. The mean registration error between paired BSE and \(\upmu\)CT images (baseline, enhanced, deblurred) was 0.01 \(\pm\) 0.008 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Acquisition Protocol**} & \multirow{2}{*}{**Voltage (kVp)**} & \multicolumn{2}{c}{**Tube**} & \multirow{2}{*}{**Filter**} & \multirow{2}{*}{**Integration Time (ms)**} & \multirow{2}{*}{**Frame Averaging**} & **Voxel Spacing (\(\upmu\)m)** \\ \hline Baseline & 55 & 200 & 0.5 mm Al & 250 & N/A & 11.4 \\ \hline Enhanced & 90 & 44 & 0.5 mm Al & 200 & 8 & 4.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of \(\upmu\)CT acquisition parameters for baseline and enhanced protocols for visualization of _BaSO4_-labeled microdamage in rat vertebral bone.
## 3 Preliminaries
### Strapdown Seeker Model  
The strapdown seeker does not have a physical platform to achieve object tracking. As a result, it cannot provide the measurement information with respect to the inertial frame, such as LOS angles (\(q_{z}\) and \(q_{y}\)) and their time derivatives. Instead, it can only provide the body-LOS angles, \(q_{Bz}\) and \(q_{By}\), as defined in Figure 3.  
Body-LOS angles, \(q_{Bz}\) and \(q_{By}\), describe the reorientation between the body-LOS frame and the body frame. Without loss of generality, it is assumed that the optical axis of the strapdown seeker is aligned along the \(x\)-axis of the body frame. With the definition of \(q_{Bz}\) and \(q_{By}\), the optical axis can be described in the body frame as [8; 30]  
\[\mathbf{r}_{LOS}^{b}=\mathbf{L}_{y}^{T}(q_{By})\mathbf{L}_{z}^{T}(q_{Bz})\begin{bmatrix}1 \\ 0\\ 0\end{bmatrix}=\begin{bmatrix}\cos q_{By}\cos q_{Bz}\\ \sin q_{Bz}\\ -\sin q_{By}\cos q_{Bz}\end{bmatrix} \tag{9}\]Similarly, in the inertial frame, the vector of the LOS can also be given as  
\[\mathbf{r}_{LOS}^{g}=\mathbf{L}_{y}^{T}(q_{y})\mathbf{L}_{z}^{T}(q_{z})\begin{bmatrix}1\\ 0\\ 0\end{bmatrix}=\begin{bmatrix}\cos q_{y}\cos q_{z}\\ \sin q_{z}\\ -\sin q_{y}\cos q_{z}\end{bmatrix} \tag{10}\]  
Therefore, with the help of the relationship that \(\mathbf{r}_{LOS}^{g}=L_{gb}\mathbf{r}_{LOS}^{b}\), this gives  
\[\begin{bmatrix}\cos q_{y}\cos q_{z}\\ \sin q_{z}\\ -\sin q_{y}\cos q_{z}\end{bmatrix}=\begin{bmatrix}L_{gb}(1,1)&L_{gb}(1,2)&L_{gb }(1,3)\\ L_{gb}(2,1)&L_{gb}(2,2)&L_{gb}(2,3)\\ L_{gb}(3,1)&L_{gb}(3,2)&L_{gb}(3,3)\end{bmatrix}\begin{bmatrix}\cos q_{By}\cos q _{Bz}\\ \sin q_{Bz}\\ -\sin q_{By}\cos q_{Bz}\end{bmatrix} \tag{11}\]  
where \(\mathbf{L}_{gb}\) is the rotation matrix of the inertial frame with respect to the body reference frame, which can be calculated by Euler angles obtained from the navigation system.
## 3 Latin America and Colombia
### Defense and Security  
In future smart cities [46], many applications of UASs can be implemented, but security operations will continue to be a priority in all South American regions due to the conflict present. It will help security forces in intelligence missions [17] through projects such as ART Quimbaya, bringing surveillance and reconnaissance critical infrastructure, border control, and photography [47]. Additionally, Brazil has deployed drones to monitor the Amazon rainforest and prevent environmental crimes [34].  
Latin America represents a potential in the UAS market due to its special climatic and topographic characteristics [25]. Hence, applications of this technology are endless, e.g., precision agriculture by remotely sensing vegetation health [34], in future smart cities [46], to help security forces in intelligence missions [17] through projects such as ART Quimbaya, bringing surveillance and reconnaissance critical infrastructure, border control, and photography [47]. In recent years, population growth in the region has also increased the energy power demand, requiring an increase in power system expansion and transmission line inspection [28]. Some applications in this growing market are not regulated, such as in Mexico, where the use of drones in the community has become necessary due to its ability to accurately monitor and map the territory [32].  
The real-time monitoring and characterization of Pululahua volcanic activity with software and hardware have been carried out, with ambient information and georeferentiation [33]. In Colombia, some projects study PA to improve crops [44] and mitigate the impacts of climate change in the country, and its use to monitor environmental variables and lower layers of the atmosphere to prevent disasters with the help of a network such as the air-ground integrated mobile edge network (AGMEN) [45]. Although the literature shows some advances in research and projects, the use of UAS technology in the electrical sector remains low for Colombia and the Latin American region.
## 2 Methodology
### Field Data
#### 2.2.3 In situ Reflectances  
SeaBASS searches for field radiometry targeted \(R_{rs}\) (or equivalently \(L_{w}\) and \(E_{s}\), where \(R_{rs}=L_{w}/E_{s}\), or \(L_{wm}\), where \(R_{rs}\) is \(L_{wm}\) divided by the top of atmosphere solar irradiance [54]). A total of 135,966 independent field observations of \(R_{rs}\) were binned as described in 2.2.4, quality controlled as described in 2.2.5, and then matched to the CDOM database using the same spatial, temporal, and outlier elimination criteria used for DOC (2.2.2).
## 2 Materials and Methods
### Evaluation of Phosphate Ion Consumption by F-NBG and P-NBG in SBF and BSA-SBF  
PTFE tubes filled with NBG samples were prepared as described in Section 2.2 above and immersed in 1 mL of SBF or 1-, 5-, or 10-g/L BSA-SBF (\(n\) = 6 each). At 1, 3, and 7 days (24, 72, and 168 h), 100 \(\upmu\)L samples were collected from the media and the phosphate ion concentration was measured using a kit (DPM2-PO4-C; Kyoritsu Chemical-Check Lab Corp., Tokyo, Japan) based on the molybdenum blue method [17]. The baseline phosphate ion concentration of the media was also evaluated. A standard curve was prepared using a phosphate ion standard solution (Wako Pure Chemical Industries, Ltd.), the correlation coefficient of which was 0.997. The phosphate ion concentrations of F-NBG and P-NBG soaking in media were statistically compared at each time point using an unpaired \(t\)-test. Differences with \(p\) values \(<\) 0.05 were considered statistically significant.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
The proposed mobile application will provide a warning system with crucial weather information (humidity, UV status, and temperature) and details about destination services that mitigate risks like dehydration, heart attack, and heatstroke. It will also assess the risk of heatstroke based on smartwatch data, generate timely reports on abnormal conditions, and establish seamless communication with nearby rescue organizations to ensure immediate assistance for tourists in need. Furthermore, hospitals will have real-time access to patients' locations within their service area. By addressing the limitations of existing tourist information and providing a comprehensive solution that meets the needs and concerns of tourists in warm and humid destinations, our research aims to make a significant contribution to the field of tourism and tourist safety. Based on the comprehensive literature review conducted, the ensuing topics constitute the primary focal points of our research contributions. 1. The development of a comprehensive mobile application that integrates weather and safety information, health recommendations, and a life rescue system to enhance tourist safety in warm and humid destinations. 2. The incorporation of three distinct input datasets from various sources, including internet-based data, data obtained from a custom-designed application linked to tourists' smartwatches, and data directly collected from tourist attractions, to facilitate communication between tourists and emergency service providers in the relevant areas. 3. The proposal of a research methodology consisting of two main components: data collection and the design of the tourist safety system, which can be used as a framework for future research in this area. 4. The potential to improve the management and policy of tourism and the development and implementation of highly effective weather and safety information systems for tourists through the proposed solution. The article follows a structured organization outlined below; Section 2 presents a comprehensive literature review. Section 3 details the research methodology, while Section 4 encompasses the computational results and framework. Section 5 offers an insightful discussion, drawing comparisons between the current research and existing methodologies. Finally, Section 6 concludes the study and suggests potential directions for future research.
# Mean Square Consensus of Nonlinear Multi-Agent Systems under Markovian Impulsive Attacks
## 1 Introduction
However, most existing methods such as the works in [26; 27] for a consensus with attacks only considered cases where topologies or systems contained Markovian properties rather than attacks. Though some authors did assess attacks in a Markovian form such as the works in [9; 28], most of these works were concerned with DoS attacks; the type of variable chosen, such as gains; or assumptions about the effects of a combination of attacks instead of deception attacks. As a result, the works above do not fully consider the characteristics of the attack, and thus, a general work on both gains and instants of deception attacks still has not yet been conducted. With the discussion above, this paper aims to establish a more universal model for deception attacks and provides some sufficient conditions for the consensus of multi-agent systems. The contributions of this paper can be summed up as follows:  
* Taking destabilizing impulse into account, and based on the Markovian properties, a general model for impulsive deception attacks is established, where the gains do not obey any specific distribution and instants are assumed to obey the Markovian chain. * In addition, some special cases including both the characteristics of attacks and Markovian properties are considered. The remainder of this paper is organized as follows. The main results about the consensus for system (1) are derived in Section 3 based on the novel model for the impulsive deception attacks introduced in Section 2, and a corresponding numerical example is provided in Section 4. The conclusion and some interesting topics of future work are given in Section 5. **Notation 1**. : _Let \(\mathbb{R}\) be a set of real numbers and \(\mathbb{N}\) be a set of positive integers. Let \(\mathbb{R}^{n}\) be the n-dimensional Euclidean space; furthermore, \(I_{n}\) is the n-dimensional identity matrix and \(1_{n}\) is the n-dimensional column vector with one element. Let \(M\) be a matrix or vector, for which the transposition is represented by \(M^{\mathsf{T}}\), and its deduced norm or norm is represented by \(|M|\). Let \(\lambda_{\mathsf{max}}(A)\) and \(\lambda_{\mathsf{min}}(A)\) denote the largest and nonzero smallest eigenvalue of the matrix \(A\), respectively. A matrix \(B\) is said to be a symmetric positive definite matrix if \(B=B^{\mathsf{T}}\) and \(x^{\mathsf{T}}Bx>0\) for any nonzero vector \(x\). For any function \(h:\mathbb{R}\to\mathbb{R}\), we define \(h(t^{+})=\lim_{\Delta t\to 0^{+}}h(t+\Delta t),h(t^{-})=\lim_{\Delta t \to 0^{-}}h(t+\Delta t)\). In addition, let \((\Omega,\mathcal{F},\mathcal{F}_{t\geq 0},\mathcal{P})\) be a complete probability space with respect to the filtration \(\mathcal{F}_{t\geq 0}\), which satisfies the common conditions, i.e., right continuous and \(\mathcal{F}_{0}\) contains all \(\mathcal{P}\)-null sets. Let \(\mathbb{E}[\cdot]\) (\(\mathbb{E}[\cdot|\cdot]\)) denote the (conditional) expectation of a stochastic process._
## 4 Experimental Results and Analysis
### Experimental Setup
#### 4.1.3 Parameter Setting  
After many experiments and comparisons, the experimental parameters are set as follows: population size \(N=20\), iteration times \(T=30\), crossover probability \(Pc=0.9\), mutation probability \(Pmt=0.15\), gene replacement probability \(Pr=0.95\), and candidate set size of replacement genes \(K=4\).
## 4 Conclusions  
In conclusion, we have experimentally demonstrated the 515 nm femtosecond laser generated from a MgO:PPLN crystal based SHG module and a monolithic Yb-doped MOPA laser system. The SHG average power reaches 820 mW, and the pulse duration is \(\sim\)540 fs. The 515 nm femtosecond laser exhibits power stability with an RMS value of 0.18% for an eight hour measurement and a pulse-to-pulse intensity standard deviation of 0.007. Thanks to the all-PM fiber-based laser configuration, the SHG laser system enjoys compact, robust, and low-cost characteristics.  
Conceptualization, B.Y. and Q.H.; methodology, B.Y. and Q.H.; validation, B.Y., Q.H. and C.T.; formal analysis, B.Y.; resources, Q.H., H.C. and W.L.; writing--original draft preparation, B.Y.; writing--review and editing, B.Y., Q.H. and K.Y.; supervision, Q.H.; project administration, Q.H., K.Y. and H.Z.; funding acquisition, Q.H., K.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China, grant number 2018 YFB0407100 and the National Natural Science Foundation of China, grant number 11974248.  
Figure 5: (**a**) Microscope image of the MgO: PPLN surface damage site with the magnification of 20\(\times\). Inset: Microscope image of that with the magnification of 40\(\times\). (**b**) Power curve of the 515 nm light for a duration of more than four hours. Inset: Magnified scale. (**c**) Histogram of 515 nm pulse intensity distribution. (**d**) Power curve of the 515 nm light for a duration of eight hours in this case. Inset: Outside view of the home-made aluminum alloy case.
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
## 2 Formulation
### Bulk Damage Problem Description
#### 2.1.2 Damage Evolution Law  
The damage value can be taken to be equal to the damage force. However, this local definition of \(D\) has several shortcomings, as will be discussed below and in Section 2.3.2. We employ the time-delay model in [3; 25] for damage evolution. The rate of damage evolution, \(\dot{D}\), is given by,  
\[\dot{D}=\left\{\begin{array}{ll}D_{\mathrm{src}}(D,D_{f})=\frac{1}{\tau_{c}} (1-e^{-a(D_{f}-D)})&D<1\\ 0&D=1\end{array}\right. \tag{6}\]  
where \(D_{\mathrm{src}}(D,D_{f})\) is a general source term for the evolution equation \(\dot{D}=D_{\mathrm{src}}(D,D_{f})\). This function can be calibrated from experimental strain-stress results, for example, for uniaxial tensile/compressive loading. The specific form of \(D_{\mathrm{src}}(D,D_{f})\) is taken from [3; 47] as it is claimed to accurately model materials' rate effect; cf. Section 2.3.2. In addition, \(\tau_{c}\) is the _relaxation time_, \(a\) is the _brittleness exponent_, and \(\langle.\rangle\) is the Macaulay positive operator.  
Albeit its simplicity, this evolution model incorporates several essential characteristics of real materials. First, we observe that the damage evolution is governed by the difference of damage \(D\) and damage force \(D_{f}\). The higher the difference, the higher the damage rate. Moreover, when \(D=D_{f}\), damage evolution terminates. That is, \(D_{f}\) is the _target damage_ value; if \(D\) is smaller than the target value, it evolves until it reaches \(D_{f}\). Second, damage cannot instantaneously reach \(D_{f}\) given that \(D\) is bound by the maximum damage rate \(1/\tau_{c}\). As will be discussed in Section 2.3.2, this results in the rate-sensitivity of strain-stress response. Third, the positive operator ensures that damage is a nondecreasing function in time (no material healing processes). Finally, Figure 3 shows the effect of \(a\); for higher values of \(a\), even small differences between \(D_{f}\) and \(D\), quickly jumps up the damage rate close to its maximum value of \(1/\tau_{c}\); implying a more brittle response.
## 4 Cryptographic Unit System
### Cryptographic Unit  
The cryptographic unit is composed of three parts: an XOR operator (a simple operator to hide the communication from simple eavesdropping), a Table LookUp unit and a Cipher block unit (described in Sections 4.3.1 and 4.3.2). Additionally, it has two inputs incoming from the chaotic generator: the X Key and the Y key. The full scheme of both of the encryption and decryption systems is shown in Figure 6. The original message generated by the device starts by passing through the Table LookUp unit that shuffles the bits. Then, the result is operated with an XOR operation with the X Key. Next, the signal passes through the Cipher block unit. The resulted message re-enter again through the same units in the same order, only this time the XOR operation is achieved with the Y Key. Finally, the encrypted message is generated. The two times reinstantiation of the blocks is the minimal scheme to guarantee a robust cryptographic result. For medical systems with robust computational resources, a loop can be introduced into the system to increase the complexity of the encrypted message.  
For the decryption unit, the received encrypted message will get through the same units in a reverse order: the Cipher block is now used in its decryption mode, the Table LookUp unit will reorder the bits in their original positions according to the same function it has in memory (which is synchronized with the order stored in the sending device), and for the XOR operations with the encryption keys, we can get the original message by reapplying the same encryption key as a second operator. This is where the necessity of having the same encryption key on both sides is critical. If there is a looping function in the original encryption unit, the decryption unit should be synchronized with the same number of loops in its algorithm. Therefore, we can regain the original message sent by the first device.