esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Terrain Model Generation
#### 2.3.3 Spaceborne Optical Data  
For satellite-based stereo DSMs, three DTM generation approaches based on morphology, geodesic dilation and steep edge detection on simulated synthetic urban scenes are evaluated in Reference [99]. A standard method based on morphological opening was extended in Reference [100]. There, percentile filters rather than morphological grayvalue erosion and dilation are employed to be more robust against DSM outliers. Another approach is based on determining the normalized volume above ground over several scanlines [101]. An automated stratified object-based approach that also usesthe spectral information of an ortho image is presented in Reference [102]. An approach based on iterative geodesic reconstruction tested on Cartosat-1 stereo images is presented in Reference [103]. DTM generation in forested regions based on Cartosat-1 stereo image is reported in Reference [104]. There, the panchromatic image is classified into semantic classes, like buildings, low forest, high forest or ground and different filtering is applied to DSM regions according to the class.
# Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems +
## 2 Proposed Dynamic Changes  
### Base Problem Formulation  
As mentioned before the TSP is used as the base problem to generate dynamic test cases. Typically, a TSP instance is modelled by a fully connected weighted graph \(G=(N,A)\), where \(N=\{1,\ldots,n\}\) is a set of \(n\) nodes and \(A=\{(i,j)\ |\ i,j\in N,i\
eq j\}\) is a set of arcs. Each arc \((i,j)\in A\) is associated with a non-negative value \(w_{ij}\in\Re^{+}\), which for the classic TSP represents the distance between nodes \(i\) and \(j\). In the case of such arc, in which it is blocked or does not exist, we set its associated value as a very large number. In the symmetric TSPs, we have \(w_{ij}=w_{ji}\) for every pair of nodes. When \(w_{ij}\
eq w_{ji}\) for at least one pair of nodes, the TSP becomes asymmetric. Every problem instance consists of a weight matrix, \(W=\{w_{ij}\}_{n\times n}\), containing all the weights associated with the arcs of the corresponding graph \(G\). A classical mathematical model for TSP was proposed by [38] and later modified and relaxed, so it was easier for the authors of [39] to solve, and it was recently used by [40]. The formulation of the TSP is an integer linear programming utilizing \(n^{2}\) binary variables \(x_{ij}\). The variable \(x_{ij}\) is defined as:  
\[x_{ij}=\begin{cases}1,\text{ if and only if arc }(i,j)(i=1,\ldots,n;j=1,\ldots,n) \text{ is in the optimal tour}\\ 0,\text{ otherwise}\end{cases} \tag{1}\]  
The objective function is to minimize the route cost, as shown in Equation (2).  
\[min\sum_{i=1}^{n}\sum_{j=1,j\
eq i}^{n}w_{ij}x_{ij} \tag{2}\]  
\[\text{subject to }\sum_{i=1,j\
eq j}^{n}x_{ij}=1,\quad j=1,\ldots,n \tag{3}\]  
\[\sum_{j=1,j\
eq i}^{n}x_{ij}=1,\quad i=1,\ldots,n \tag{4}\]  
\[\sum_{i\in S}\sum_{j\in S,j\
eq i}x_{ij}\leq|S|-1,\quad S\subset N\;:\;S\
eq\oslash \tag{5}\]  
\[x_{ij}\in\{0,1\}\quad i,j=1,\ldots,n,\;\;i\
eq j \tag{6}\]  
where constraints (3) and (4) impose that the in-degree and out-degree of each vertex, respectively, is equal to one, while constraints (5) are sub-tour elimination constraints and impose that no partial circuit exists. The variables domain are expressed by constraints (6).
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 3 Delayed Combination of Encoded Features  
Our objective is to build a new architecture based on a deep learning technique which uses the delayed combination model to improve the accuracy for two benchmark datasets, i.e., CoNLL 2003 and OntoNotes 5.0.  
The main idea of our work is the bidirectional language model (BLM), to which is given as an input, a sequence of tokens \((t_{1},t_{2},...,t_{\rm n})\) that is passed through forward and backward a language model (LM) [37]. The forward pass of the LM computes the sequence probability according to Equation (1). The backward pass is similar to the forward pass, expect it runs over the reverse sequence to predict the previous token according to the Equation (2).  
\[P(t_{1},t_{2},...,t_{N})=\prod_{k=1}^{N}P(t_{k}|t_{1},t_{2},...,t_{k-1}) \tag{1}\]  
\[P(t_{1},t_{2},...,t_{N})=\prod_{k=1}^{N}P(t_{k}|t_{k+1},t_{k+2},...,t_{N}). \tag{2}\]  
The forward and backward sequence has two separate, hidden states to capture both past and future information. Each result of forward and backward is concatenated to obtain the result vector before being passed to the CRF layer. For the CRF computation, we denote \(X\) as the matrix of the score output from the bidirectional LSTM to predict the tag sequence \(\rm Y=y_{1},y_{2},...,y_{n}\) with the probability of the ground truth for a tag of each word shown determined by Equation (3) [31]:  
\[P(y|X,\lambda)=\frac{1}{Z(X)}exp\sum_{i=1}^{N}\sum_{j}\lambda_{j}f_{i}(X,i,y_{ i-1},y_{i}). \tag{3}\]  
Here, \(\lambda\) is the feature function weight, which is learned by the corresponding algorithm, and \(Z(X)\) is the normalization factor according to \(Z(X)=\sum_{y\in Y}\sum_{i=1}^{N}\sum_{j}\lambda_{j}f_{i}(X,i,y_{i-1},y_{i})\).  
The bidirectional LSTM CRF technique was used for our experiment on the NER task. We first select the most promising features for the NER task from the previous works and also design a novel CNN-based sentence encoding using a dictionary. Then we suggest the delayed combination of the promising features by considering the characteristics of each feature.
## 2 Material and Methods
### Classification of ParAF and PerAF with the Models  
The classification of ParAF and PerAF was performed using coarse tree models together with leave-one-out cross-validation, as depicted in Figure 1. Each coarse tree model was built using one possible combination of the variables that passed the features selection process. In particular, for all segment lengths, all the possible subsets of features were used to determine the coarse tree providing the highest accuracy. Furthermore, aimed at further simplification of the classification process, a coarse tree model for each variable was created, having just one input feature. Regarding the cross-validation strategy, any recording of the patient under test was previously removed from the training set. Accuracy values obtained with the classification process were reported as percentages.
# Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment
## 3 The Proposed Two-Stage Phase Alignment Frame
### Two-Bit Feedback Algorithm
If \(b_{0}b_{1}=10\), then \(\overline{u_{i}^{best}}=\overline{u_{i}}(t-1)+\delta_{i}(t-1)+\tau_{i}(t-1)\), \(\tau_{i}(t)=\tau_{i}(t-1)\), \(\tau_{i}(t)=\pm\tau_{0}(t-1)\); B2. Else if\(b_{0}b_{1}=11\), then \(\overline{u_{i,t}^{best}}=\overline{u_{i}}(t-1)+\delta_{i}(t-1)+\tau_{i}(t-1)\), \(\tau_{i}(t)=\tau_{i}(t-1)\), \(\tau_{i}(t)=\pm\tau_{0}(t-1)\)*\(q\); B3. Else if\(b_{0}b_{1}=01\), then \(\overline{u_{i,t}^{best}}=\overline{u_{i}}(t-1)\), \(\tau_{i}(t)=-\delta_{i}(t-1)\), \(\tau_{i}(t)=\pm\tau_{0}(t-1)/q\); B4. Else\(b_{0}b_{1}=00\), then \(\overline{u_{i,t}^{best}}=\overline{u_{i}}(t-1)\), \(\tau_{i}(t)=-\delta_{i}(t-1)\), \(\tau_{i}(t)=\pm\tau_{0}(t-1)\); B5. End If Output: the best phase compensation \(\overline{u_{i,t}^{best}}\). Each concomitant satellites sends their signal towards the master satellite. Repeat in subsequent transmissions until convergence. ```  
**Algorithm 1**The RPC_TF algorithm. Consequently, the phase compensation \(\overline{u_{i}(t)}\) is obtained to eliminate the randomness when RPC_TF algorithm is converged. The beamforming weighted phase \(\phi_{i}(t)\) at each satellite can be achieve as \(\phi_{i}(t)=\overline{a_{i}(t)+b_{i}(t)+u_{i}(t)}\). Therefore, we can obtain the beamforming gain with EGC reception, and the fusion SNR at the master satellite can be approximately equal to \(\gamma_{opt}^{EGC}\). The proposed two-stage phase alignment frame diagram of SS_DSC system is summarized in Figure 2.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 2 DW Family Code Construction  
DW family code consists of DW, modified double weight (MDW), and enhanced double weight (EDW). Each code characteristic differs in terms of the amount of weight and the code sequence/structure. DW code is the oldest code within the family. MDW code structure is based on DW code with some modification, while EDW code structure is an enhancement of DW and MDW code. The improvement and modification of the code are in terms of the amount of weight it can support (odd or even); the structure of EDW differs from the previous DW family code, and EDW has a shorter code length than the MDW code.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 5 Potential Research Directions
### Real-Time Attack Detection in ML
In the application layer, a real-time anomaly detection method is proposed in [103] for autonomous driving systems, where the ML model classifies a normal image inputs versus malicious inputs causing unsafe conditions in a short time. In [87], a real-time software attack detection and mitigation strategy for a UAV system is proposed, where the attack is detected within a few hundred milliseconds. ML-based cyber-physical attack detection methods must consider real-time constraints on physical systems. However, due to the enormous computation loads under a complex ML model, attack detection time is delayed, which causes a disruption of the physical system. To satisfy real-time constraints on cyber-physical security, reducing the complexity of an ML model is required while achieving high levels from conventional ML evaluation metrics (9)-(12).
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 2 Materials and Methods  
The intelligent constellation is composed of satellites and inter satellite links, forming a space transmission backbone network. Due to the large scale of satellites, constellation networks often adopt sub constellations with different orbital heights and inclination angles to form multi-layer hybrid constellations. As the access node of space network, satellite plays the role of space-based mobile base station. Microwave inter satellite links can be established between satellites to realize data relay and forwarding. The contact graph routing (CGR) algorithm based on the connection graph model can not only calculate multiple paths at the same time, but also consider the consumption of link bandwidth. It can be used not only in deep space networks, but also in earth orbiting satellite networks. Satellite networks usually adopt dynamic graph model and optimization theory to design and optimize information transmission. With the periodic and predictable orbital motion characteristics, some topology control strategy is generally used in traditional satellite networks to shield the dynamics of topology. Then the static topology sequence is optimized for transmission and routing.  
The satellite communication system mainly adopts point-to-point unicast transmission when providing services to another node. However, for the current fast-growing multicast services, this physical layer transmission method is not efficient. Multicast service mainly refers to the service type that needs to send the same information to specific multiple nodes. If the point-to-point physical layer transmission mode is adopted directly, the more wireless resources will be consumed. Satellite has the broadcasting characteristics of wireless communication. For these multicast services, point-to-multipoint multicast transmission can be used in the physical layer to achieve more effective transmission. Facing the huge application demand and more diversified business types in the future, the satellite communication system will face new transmission models, namely multi-channel unicast transmission and multi-channel multicast transmission. Many inter satellite link propagation models can be represented by directed graph which is given in Figure 1. With \(K\) satellite communication nodes, a weighted digraph consists of \(K\) nodes and at most \(K(K-1)\) directed edges. If there is a directed edge from node \(i\) to node \(j\) and its weight is \(d_{i,j}\), it means that node \(i\) sends a message flow with degree of freedom \(d_{i,j}\) to node \(j\). If two nodes are not adjacent, it means that no message is transmitted between the two nodes.
## 9 Conclusions  
We have presented a data-foraging-oriented reconnaissance algorithm based on bio-inspired indirect communication for aerial vehicles. One original contribution is the definition of an artificial pheromone, as an abstract data type, oriented to perform stigmergy-based communications. Through the virtual segregation of such pheromones, the algorithm allows aerial vehicles which sense a given area, to communicate indirectly their findings. In this way, aerial vehicles can create several paths oriented to explore the environment and recognize profitable data sources. By considering the energy constraints of aerial vehicles and their impact on their movement capabilities, the operational environment was discretized in the form of a set of regions organized into a Hextille. Then, based on the Hextille, the environment is formally modeled as a connected undirected graph called Data Foraging Graph (DFG). The artificial pheromones segregated are related to an area that is the region visited, which corresponds to a node in the DFG. The Data Foraging-Oriented Reconnaissance problem has been defined. We identify and define the required and sufficient movement capacity capabilities of the aerial vehicle per trip to explore an environment according to the depth of the DFG. The solution proposed was formally specified and mathematically evaluated. The results prove the viability and efficiency of the solution. Additionally, we have presented a study increasing the aerial vehicle's movement capability. The results of this study show that the average number of trips and the run time to explore the environment highly decrease as the movement capability increases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **AVG Trips** & **Std. Deviation** & **Variance** & **Max** & **Min** \\ \hline Proposed alg. \(G_{2}\) & 3.8922 & 0.8326 & 0.6933 & 5 & 3 \\ MULES \(G_{2}\) & 13.7152 & 10.5584 & 111.4790 & 122 & 3 \\ Proposed alg. \(G_{3}\) & 10.6784 & 2.9283 & 8.5751 & 26 & 5 \\ MULES \(G_{3}\) & 206.6570 & 181.1481 & 32,814.6230 & 2290 & 6 \\ Proposed alg. \(G_{4}\) & 19.7463 & 5.3766 & 28.9077 & 54 & 7 \\ MULES \(G_{4}\) & 3189.4170 & 2981.9314 & 8,891,914.6828 & 33730 & 66 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of our proposed algorithm compared with MULE for DFGs \(G_{h}\) of size \(h=(2,3,4)\).  
**Acknowledgments:** This work is supported by the National Council for Science and Technology of Mexico (CONACYT) through the Master scholarship number 390398 and the project ID PDCPN2013-01-215421.  
**Author Contributions:** The five authors contributed proportionally in these categories: Conception or design of the work, data analysis and interpretation, drafting the article, critical revision of the article and final approval of the version to be published.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### In Situ Chemical Reduction (ISCR)  
Similar to ISCO, In Situ Chemical Reduction (ISCR) has been developed since the 1990s. This is a technique involving the targeted liquid injection or placement of solid and chemically reducing reagents in the path of the contaminant plume or near the contaminant source. By adding ISCR reagents to the subsurface environment, a sequence of different processes can create very strong (e.g., \(\text{Eh}<-550\) mV) reducing conditions that stimulate the reduction of contaminant concentrations of interest to a desired level [318]. In general, the process is equivalent to In Situ Chemical Oxidation (ISCO), although this method has only been applied to the treatment of contaminant plumes, particularly through the use of permeable reactive barriers [319; 320; 321]. Injections upstream of the contaminant source have also been developed in the last decade [322]. Today, three types of natural and abiotic reducing agents are most commonly studied and used: minerals derived from a reducing form of iron [323; 324; 325], minerals that are composed of a reducing form of iron and sulphur [326; 327], and molecules derived from organic material, quinones, or self-made soybean oil emulsion [328; 329].
## 3 Ergodic Performance Analysis of the Proposed Scheme Using Rayleigh and Rician Fading Channels
### The First Scenario  
#### 3.1.1 Upper Bound for the Achievable Rate Using Rayleigh Fading Channels for \(D_{1}\)  
In the case of Rayleigh fading channels, the existing links \(S-I_{1}-D_{i}\), \(S-I_{2}-D_{i}\) and \(S-I_{3}-D_{i}\) are assumed to have only non-line of sight (NLOS) components and hence can be modeled as Rayleigh fading channels, i.e., \(h_{I}=\bar{h}_{I}d_{I}^{-\alpha/2},l\in\{S1_{1},I_{1}D_{i},SI_{2},I_{2}D_{i}, SI_{3},I_{3}D_{i}\}\), where \(\bar{h}_{I}\) is the complex-Gaussian small-scale fading channels with zero mean and unit variance.  
**Proposition 1**.: _The upper bound for \(D_{1}\) using the Rayleigh fading channels is given as_  
\[R_{\mathrm{upper,D_{1}}}^{(1)}=\log_{2}\bigg{(}1+\mathrm{E} \bigg{[}\gamma_{D_{1}}^{(1)}\bigg{]}\bigg{)} \tag{19}\] \[=\log_{2}\bigg{(}1+\frac{\gamma\chi_{1}\big{(}\frac{1}{\bar{h}} \beta_{1}+\frac{1}{\bar{h}}\beta_{2}+\frac{1}{\bar{h}}\beta_{3}\big{)}}{\gamma \chi_{2}\big{(}\frac{1}{\bar{h}}\beta_{1}+\frac{1}{\bar{h}}\beta_{2}+\frac{1}{ \bar{h}}\beta_{3}\big{)}+\big{(}\mathrm{Y}_{S}^{2}+\mathrm{Y}_{D_{1}}^{2}\big{)} \gamma\big{(}\frac{1}{\bar{h}}\beta_{1}+\frac{1}{\bar{h}}\beta_{2}+\frac{1}{ \bar{h}}\beta_{3}\big{)}+1}\bigg{)},\]  
_where \(\beta_{1}=d_{SI_{1}}^{-\alpha}d_{I_{1}D_{1}}^{-\alpha}\eta^{2}N_{1}\big{(}16+( N_{1}-1)\pi^{2}\big{)}\), \(\beta_{2}=d_{SI_{1}}^{-\alpha/2}d_{I_{1}D_{1}}^{-\alpha/2}d_{SI_{2}}^{-\alpha/2 }\,d_{I_{2}D_{1}}^{-\alpha/2}\eta\eta\)\(\sqrt{N_{1}N_{2}(16+(N_{1}-1)\pi^{2})(16+(N_{2}-1)\pi^{2})}\), \(\beta_{3}=d_{SI_{2}}^{-\alpha-\alpha}d_{I_{2}D_{1}}^{-\alpha}\eta^{2}N_{2} \big{(}16+(N_{2}-1)\pi^{2}\big{)}\)._  
Proof.: See Appendix A.
## 4 Key Design Requirements and System Components  
This section highlights the high-level architecture, key design requirements, and main components of the SSFXG model. As shown in Figure 1, this model compartmentalizes the NW into six layers, each composed of different NW nodes/components with distinct privileges and roles, as explained in detail below.
## 2 Materials and Methods  
### Hyperspectral Hybrid Frequency-Domain (FD) and Continuous-Wave (CW) Diffuse Optical Imaging DOI  
The proposed hybrid FD/CW method is a three-step process starting with capturing quantitative FD measurements at multiple tissue views over the tumor-containing heterogeneous tissue. In this paper, the FD measurements were acquired at a limited number of wavelengths (660, 690, 785, and 830 nm), which were used to recover optical reduced scattering \(\mu_{s}^{\prime}\) and calculate a reliable prefactor (a) and power (b) by fitting the data to the empirical Mie scattering relationship \(\mu_{s}^{\prime}(\lambda)=a\big{(}\frac{\lambda}{500~{}nm}\big{)}^{-b}\)[28; 29].  
In the hybrid problem, we adopt the Finite Element Method (FEM)-based scheme and implemented it by NIRFAST software [30; 31] to solve the inverse problem. The goal of the inverse problem is to recover optical properties of the tissue/medium, which is accomplished via an iterative optimization/minimization scheme, which seeks the minimum misfit between the measured and estimated data (represented by an objective function). In the hybrid method, FEM-based reconstructions are performed with hard priors of tumor location and size. In this research, we have confined our study to two region reconstructions, where optical properties are reconstructed for normal and tumor regions of the tissue. In hard prior reconstructions, the tissue volume can be segmented into multiple regions by appropriately labeling all nodes within each region [31]. This in turn reduces the dimensions of the Jacobian (sensitivity matrix), thereby making it a smaller well-conditioned matrix [31].  
In the second step of the hybrid method, \(\mu_{s}^{\prime}\) was estimated for each CW wavelength from the recovered a and b for wavelengths in the spectral range 650 nm to 1000 nm at every 5 nm. In the final step, the broadband \(\mu_{s}^{\prime}(\lambda)\) were used to constrain CW recovery based on multiple broadband reflectance measurements to extract the optical absorption spectrum \(\mu_{a}(\lambda)\). This approach mimics a hybrid FD/CW technique used in conventional topographic DOI imaging for homogeneous tissue [16]. Figure 1a depicts the hybrid method overview, and Figure 1b demonstrates an example of \(\mu_{a}(\lambda)\) and \(\mu_{s}^{\prime}(\lambda)\) estimation of a simulated 0 mm deep ellipsoidal inhomogeneity (20 mm \(\times\) 30 mm \(\times\) 20 mm) using the FD and CW combined method.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 2 Detection System in the Peruvian Navy
### Radar System Architecture  
Plot detection is a real-time process and requires both immediate and accurate responses. Real-time processes and hardware have a critical relationship [38]. Depending on the hardware components used, fast and permanent access to memory, reliable storage mechanisms, and high speed of operation can be guaranteed. Consequently, it can be said that the radar system's architecture influences the performance of capturing, transmitting, processing, detecting, and tracking contacts. The infrastructure used in the Peruvian Navy includes VME (Versa Module Eurocard) Host Processor cards, PMC or Mezzanine Cards for radar data input, scanning converters, and graphics cards integrated with PCI (Peripheral Component Interconnect) communication buses. Each vessel with navigation radars consists of 32 multicore processors, including 16 backup processors. The hardware, together with the middleware software and the CFAR plots detection algorithm, which are currently running, respond within the established minimum latency time. However, CFAR has shortcomings in the rate of false alarms and false negatives. The objective of this work is to search for a contact detection algorithm that exceeds the accuracy capability of the current CFAR algorithm. Therefore, the predominant factor in evaluating and choosing the new detection model is the accuracy capability over speed. Figure 3 shows the radar system architecture diagram.