esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors and Communication Simulation for Unmanned Traffic Management
## 5 Proposed Simulation Models for Key Elements
### Surveillance Network  
Two main parameters have to be considered in order to model sensors within surveillance networks. First, we have to define whether a sensor is cooperative or non-cooperative. Additionally, for cooperative sensors their specific technology (i.e., ADS-B, FLARM, etc.) needs to be defined. The second parameter to be defined is the sensor coverage, again based on a simplified model based on maximum horizontal and vertical ranges.  
Starting with non-cooperative sensors, each sensor receives the periodical drone state update messages (received through the message bus) that include their real position and checks if it is inside the sensor's coverage. Three stochastic processes are used to generate plots from actual drone states, as depicted in Figure 9. First, the probability that a sensor does not detect the drone (miss rate) is considered. For actually detected drones, an additive measurement error is incorporated into real drone position in order to derive plot measures. In our current simulator, measurement errors are modeled as white Gaussian processes, but more complex models including biases, correlated noises, etc. could be easily incorporated. Finally, the sensor agent can also simulate false alarms within the sensor range, according to a sensor-specified false alarm rate. These stochastic processes parameters are specified in SensorModel objects, part of the simulation scenario specification.  
In the case of cooperative sensors, drone detection depends on it transmitting its position voluntarily (cooperative drone). To simulate this transmission, a logical channel was implemented within the message bus. There, drones publish their estimated position in a series of hierarchical topics that corresponds to the types of collaborative equipment (transponders, emitters) they have installed. Collaborative sen  
Figure 9: Non-cooperative sensor plot generation process.  
sponding topics depending on their type, and they check if the received position is within the sensor's range. If that is the case, a new plot is created.  
Finally, sensors transform plots into tracks that are then sent to the UTM system. A track contains:  
* a sensor identifier,
* the type of sensor that has generated the track,
* a unique track identifier,
* track reference time,
* estimated drone position,
* estimated drone speed, and
* drone identifier (if sensor is cooperative).  
To generate tracks, a classical monosensor tracking chain is simulated (although more complex data processors might be easily incorporated by modifying the sensor agent). Three processes are simulated within each sensor, as shown in Figure 10. First, each plot is associated to an existing track or a new track is created otherwise. Then, the incoming plot is filtered to reduce measurement errors through techniques such as Kalman filters, and the corresponding track is updated with the new information. Finally, all tracks positions are periodically predicted to a common time instant and sent to the UTM system.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 4 Datasets
### RadioML2016.10b  
This dataset contains 11 modulation modes: 8 digital modulation and 2 analog modulation. These include 8 PSK, BPSK, SPFSK, GFSK, PAM4, QAM16, QAM64 and QPSK for digital modulation and WBFM, AM-SSB and AM-DSB for analog modulation. It contains 1,200,000 communication signal data after sampling. The parameters of RadioML2016.10a and RadioML2016.10b are the same except for the difference of modulation types and signal quantity. The display parameters of the dataset are described in Table 8. In addition, the two datasets are generated in the same way.  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **RadioML2016.10a** \\ \hline Number of modulation mode & 11 \\ \hline Number of digital modulation mode & 8 \\ \hline Number of analog modulation mode & 3 \\ \hline Number of analog modulation mode & 3 \\ \hline \multirow{2}{*}{modulation mode} & 8 PSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, \\  & PAM4, QAM16, QAM64, QPSK, WBFM \\ \hline Format of each sample & \(2\times 128\) \\ \hline Number of samples & 220,000 \\ \hline Samples per symbol & 8 \\ \hline SNR(dB) & \(-20\)2:18 \\ \hline \end{tabular}
\end{table}
Table 7: Description of display parameters for Radio2016.10a dataset.  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **RadioML2016.10b** \\ \hline Number of modulation mode & 11 \\ \hline Number of digital modulation mode & 8 \\ \hline Number of analog modulation mode & 3 \\ \hline Modulation mode & 8 PSK, BPSK, SPFSK, GFSK, PAM4, QAM16, \\  & QAM64, QPSK, WBFM, AM-DSB \\ \hline Format of each sample & \(2\times 128\) \\ \hline Number of samples & \(20\)2:18 \\ \hline \end{tabular}
\end{table}
Table 8: Description of display parameters for Radio2016.10b dataset.
## 4 Experimental Results and Discussion
### Mechanical Properties
However, when the fiber content is too high, the total surface area of the fibers increases, and a larger amount of cement paste needs to be applied, which adversely affects not only the bonding between the cement paste and the aggregate, but also the splitting tensile strength. The splitting tensile strengths of BF15 PF5 and BF10 PF10 were higher than that of BF5 PF15 when the total fiber content is 0.2%. In general, the results illustrate that the effect of BF on the splitting tensile strength is greater than that of PF [12; 14]. This is may be because the elastic modulus and tensile strength of BF are much higher than those of PF. When the microcracks expand further, PFs are pulled out or broken, and then, the tensile stress is borne mainly by the BFs. Therefore, BF plays a major role in the intermediate and later stages of fracture development. Results of the analysis of variance are presented in Table 4, which indicate whether or not there are significant differences in the compressive strength and splitting tensile strength between the BPFRC and reference concrete. According to the significance level of 0.05, when the significance coefficient of BPFRC is less than or equal to 0.05, there is a significant difference in the strength between BPFRC and the reference concrete. Otherwise, there is no significant difference. Therefore, when the BF and PF contents are 0.05% each, the compressive strength and splitting tensile strength are not significantly improved. However, when the fiber contents are (0.05% BF + 0.15% PF) and (0.10% BF + 0.05% PF), the compressive strength and splitting tensile strength are significantly reduced and there is a significant difference between BPFRC and the reference concrete.
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 1 Introduction
### Contribution  
The main contributions in this paper can be summarized as follows,  
1. We elaborate Industry 4.0 towards Forestry 4.0, which has been proposed as research initiatives in recent years. While most of the publications have focused mainly on the digital technologies, we have focused on applying the automation theme by proposing dynamic stream query processing using IoFT for forest fire detection use case.
2. We provide a flexible model proposing the ideal window type and size depending on stream rates and application requirements. The proposed dynamic query window selector can monitor various external factors including stream rate, resource constraints, and application requirement to propose the most optimal window size and type for a given query. It is also capable of detecting any in-efficiencies and re-deploy the optimal query.
3. We identify and perform a real-world use case of Forestry 4.0 (i.e., forest fire detection based on IoT data) to evaluate the dynamic window selector in the fire situation when the stream rate suddenly changes i.e., weather sensor starts sending streaming data in high rate.
## 2 State-of-the-Art (Method)
### The Use of Electromagnetic Sensors for Environment Monitoring
#### 2.1.2 Monitoring Parameters with Inductive Coils
In the aforementioned papers, the viability of using the inductive coils as sensing method has been demonstrated for monitoring different environmental variables, which modify on the permeability of the medium, such as conductivity in the water of soil moisture. Although with these coils, we do not directly measure the variable itself, we measure the changes of the \(\upmu_{0}\) and \(\upmu_{r}\). As there is a relation between \(\upmu_{0}\) and \(\upmu_{r}\) and the variable (conductivity of water), we can use this sensing coils. Therefore, we propose the design, calibration, and verification of a prototype to monitor the amount of OF in water based on inductive coils. The prototype is able to measure the changes in conductivity associated with the fertiliser concentration. The OF is composed of organic material that provides less conductivity than inorganic fertilisers, as used in [20]. This causes the increase of the challenge because we have to be able to improve the accuracy and sensitivity of the optimal function of the tested prototypes. In addition, this system supposes an advantage in the irrigation water quality monitoring because no physical sensor for measuring OF has been reported. Thus, the proposed prototype in this paper is the first system able to measure and quantify the amount of OF in water, which will be essential to prevent over-fertilisation and other possible damages to the environment once implemented in the fertigation systems.
## 3 System Model Design
### Calculation of Node Similarity
#### 3.2.1 Mobile Similarity Calculation  
Similar to the research on social relationships between nodes in social networks, mobile similarity, based on both spatial and temporal information, can also describe the closeness degree of node relationships. Generally, the higher the similarity of two nodes on the geographic trajectory, the higher the probability of successful message forwarding between them will be. According to this theory, the proposed algorithm means that the moving trajectory of the node in an opportunistic social network can be represented by the combination of the time stamp and communication area such as \(\langle t_{1},l_{1}\rangle\), \(\langle t_{2},l_{2}\rangle\ldots\ldots\langle t_{n},l_{n}\rangle\), where \(t\) represents the time stamp and \(l\) is the communication area. In addition, we define \(u\) as a node's label, and \(n\) represents the number of voices or data services between the node \(u\) and others. In previous sections we have already defined \(GP(u,r)\) as the mobile distribution of the node \(u\), so it can be computed by the following formula:  
\[GP(u,r)=\sum_{i=1}^{n}\frac{\delta(r,l_{i}(u))}{n(u)} \tag{5}\]  
where \(r\) is the location set of the communication area in an opportunistic social network. In the above Equation (5), when \(r=l_{i}(u)\), \(\delta(r,l_{i}(u))=1\); otherwise, it is equal to 0. This means that only when the node \(u\) contacts with others in the same communication area \(r\), the value of \(\delta(r,l_{i}(u))\) is 1.  
\[MS(u,v)=\frac{\sum_{i=1}^{n(u)}\sum_{j=1}^{n(v)}\theta(\Delta t-|t_{i}(u)-t_{j} (v)|)(\delta(l_{i}(u),l_{j}(v)))}{\sum_{i=1}^{n(u)}\sum_{j=1}^{n(v)}\theta( \Delta t-|t_{i}(u)-t_{j}(v)|)} \tag{6}\]  
Meanwhile, considering the time factor, we strictly define \(\Delta t\) as the time precision, which reflects the proportion of all nodes in the same geographical location at the time. Moreover, taking into consideration the impact of working time or nonworking time, we set different weight values \(\theta\) for different time periods. During working hours, humans in real-life situations may repeat a similar moving trajectory every day [16], such as from where they live to where they work, so the weight value \(\theta\) for this period may be a bit bigger. On the contrary, because human activities are unlimited during non-working hours [15], they are more likely to move randomly in the communication area. To reduce the impact of non-working time, we should give a small value for the weight \(\theta\) of this period. Consequently, the mobile similarity \(MS(u,v)\) between nodes \(u\) and \(v\) can be calculated by the above Equation (6), where \(t_{i}(u)\) represents the \(i\) - th communication time of the node \(u\), \(l_{j}(v)\) is the geographical location of the node \(u\) on the \(j\) - th communication, \(|t_{i}(u)-t_{j}(v)|\) represents the time interval between nodes \(u\) and \(v\), and \((\delta(l_{i}(u),l_{j}(v))\) is the position similarity between nodes \(u\) and \(v\).
# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
###### Abstract  
Path planning is a key task in mobile robots, and the application of Deep Q Network (DQN) algorithm for mobile robot path planning has become a hotspot and challenge in current research. In order to solve the obstacle avoidance limitations faced by the DQN algorithm in indoor robot path planning, this paper proposes a solution based on an improved DQN algorithm. In view of the low learning efficiency of the DQN algorithm, the Duel DQN structure is introduced to enhance the performance and combined with a Prioritized Experience Replay (PER) mechanism to ensure the stability of the robot during the learning process. In addition, the idea of Munchausen Deep Q Network (M-DQN) is incorporated to guide the robot to learn the optimal policy more effectively. Based on the above improvements, the PER-D2 MQN algorithm is proposed in this paper. In order to validate the effectiveness of the proposed algorithm, we conducted multidimensional simulation comparison experiments of the PER-D2 MQN algorithm with DQN, Duel DQN, and the existing methodology PMR-DQN in the Gazebo simulation environment and examined the cumulative and average rewards for reaching the goal point, the number of convergent execution steps, and the time consumed by the robot in reaching the goal point. The simulation results show that the PER-D2 MQN algorithm obtains the highest reward in both static and complex environments, exhibits the best convergence, and finds the goal point with the lowest average number of steps and the shortest elapsed time.  
deep Q network; local path planning; PER-D2 MQN; Gazebo simulation; mobile robot +
Footnote †: journal: Applied sciences
## 4 Experimental Platform and Result Analysis  
In this experiment, we acquired the signals of the MHD ARS measurement system in static and dynamic conditions. The experimental platform is shown in Figure 5. The two-axis turntable generates angular vibration on the azimuth axis. The pitch plane is perpendicular to the azimuth axis. Power supply for motors and MAD ARS. AD conversion module and computer for data acquisition and storage. The resolution of the AD module is 0.9 mV. The algorithms are performed using the computer. We fixed the MHD ARS on the pitch plane of the two-axis turntable and placed it as close as possible to the center point of the pitch plane.  
Figure 4: Image of the proposed thresholding function.
# CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
## 2 Materials and Methods
### Details of the Analyzed Geometric Variants and the Adopted Assumptions of CFD Study
#### 2.3.2 Mesh Generation and Solver Settings  
Since there are no moving bodies or boundaries in the present case, the Eulerian grids are generated using ICEM CFD; in this, the block-structured mesh was created that conforms to the curved boundaries. The generated hexahedral mesh is non-orthogonal, and the cell faces are well aligned to the direction of the fluid flow--this helps to reduce numerical diffusion. Three levels of the grid are considered viz. 214K, 386K and 568K, referred to as level 1, level 2, and level 3 mesh, respectively, and evaluated for grid dependence. Details of the surface mesh (of level 2) for the cyclone models ST2, CV2, and CC2 are illustrated in Figure 3. For all the cases, and all grid resolutions, the maximum aspect ratio was 32, and the mean skewness was 0.88 (near the central brick in the core region of the cyclone). At different grid resolutions, the velocity profiles were found to be nearly the same with no remarkable difference. The maximum difference in pressure drop was found to be less than 3.8%, and for cut-off particle size, the maximum difference was 3%.  
Figure 3: Sample mesh regions for all cyclone models ST1, CV1, and CC1. Three levels of the grid are considered viz. 214K, 386K and 568K (from left to right), respectively.  
Figure 2: Structural representation of all the cyclone models of ST variant, CV variant and CC variant (from left to right), respectively.  
In the present study--assuming a low volume fraction of solid particles loading (less than 10% by volume)--we consider one-way coupling. This has been considered in numerous studies (e.g., [8; 9; 10; 12; 17; 20]) without any loss in generality. The transient simulations were initialized with reference to the inlet velocity and zero gauge pressure at the inlet.  
For pressure and velocity coupling, we use the SIMPLEC algorithm. For pressure discretization, we use the PRESTOI interpolation scheme. The momentum equations are discretized using the QUICK scheme. For turbulent kinetic energy and dissipation rate, the second-order upwind scheme is used. For a stable solution, the first-order upwind scheme is applied to Reynolds stresses. The solver tolerance is set to \(1\times 10^{-5}\) for all the conservation equations. A time step size of 0.0001 s is used for the considered variants. To account for the near-wall effects, we use the standard wall function. Such numerical settings were also used by [16; 47; 48; 49; 50].  
For the solid particles, 1,000,000 integral steps are used to keep track of the particle trajectories. A length scale of 0.005 m has been opted. To consider the effect of turbulence on solid particles in the flow field, the discrete random walk model has been applied. The characteristic lifetime of an eddy is accounted for using the random eddy lifetime model with a trapezoidal discretization scheme [16; 17; 51].
# Joint Communication-Motion Planning in Networked Robotic Systems
## 3 Cognitive-Relay-Assisted Robot System with Interference Constraints
### Joint Communication-Motion Planning: Decide  
#### 3.3.1 Problem Formulation  
After the performance assessment, the JCMP aims at minimizing the total energy consumption. In each step, the energy consumption may contain three parts: the motion and communication costs of the relay robot, and the communication costs of the sensing robots, which can be formulated as an optimization problem given by Equation (11).  
\[\begin{array}{ll}\min\limits_{x_{x,k},P_{\mathrm{S},k},P_{\mathrm{K}}}J=\sum \limits_{k=1}^{K}P_{\mathrm{s},k}t_{\mathrm{s}}+P_{\mathrm
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
## 3 Quad-Rotor Quaternion Dynamic Model
### Coupled Dynamics Strategy  
Notice that the quad-rotor is an under-actuated system, then the force \(f_{u}\) which is expressed in the inertial frame, is the force \(\tilde{f}_{\text{th}}\) rotated using a quaternion double product  
\[f_{u}=\mathbf{q}\otimes\tilde{f}_{\text{th}}\otimes\mathbf{q}^{*} \tag{18}\]  
Since the angular acceleration is given by the external torques and the internal rotational dynamics, then by [PERSON]'s equations of motion, it follows that  
\[\ddot{\theta}=\dot{\omega}=J^{-1}(\tau-\omega\times J\omega) \tag{19}\]thus the relation between the computed control input \(\tau_{u}\) from (14) and the actual applied torque \(\tau\) from (19) becomes  
\[\tau_{u}=\tau-\omega\times J\,\omega. \tag{20}\]  
It is known that the translational and rotational dynamics of the quad-rotor are coupled such that the direction of the total force \(f_{u}\) depends only on the orientation of the UAV given by \(q\), while its magnitude is equal to the total force given by the four rotors \(||\vec{f}_{\text{th}}||\), see Figure 2 and Equation (18). Then a rotation \(\bar{\theta}_{d}=\bar{e}_{d}\theta_{d}\) must exist such that \(\vec{f}_{\text{th}}\) is transformed into \(f_{u}\) in the inertial frame.  
If the scalar product between these two vectors is defined as  
\[\vec{f}_{\text{th}}\cdot f_{u}=||f_{u}||b\cdot f_{u}=||f_{u}||^{2}\cos(\theta_ {d}) \tag{21}\]  
where \(b=[0\quad 0\quad 1]^{T}\) denotes the axis on which the thrust acts in the body frame. The cross product is expressed by  
\[\vec{f}_{\text{th}}\times f_{u}=||f_{u}||b\times f_{u}=||f_{u}||^{2}\sin( \theta_{d})\bar{e}_{d} \tag{22}\]  
Using some trigonometric identities to introduce (21) and (22) into the Euler-Rodrigues Formula from (2), a quaternion attitude trajectory \(\mathbf{q}_{d}\) can be introduced as  
\[\begin{array}{rcl}\mathbf{q}_{d}&=&\frac{(b\cdot f_{u}+||f_{u}||)+b\times f_{u }}{||(b\cdot f_{u}+||f_{u}||)+b\times f_{u}||}\\ f_{\text{th}}&=&||f_{u}||\end{array}, \tag{23}\]  
which is used to close the loop such that the orientation of \(\vec{f}_{\text{th}}\) coincides with \(f_{u}\) thus the position is stabilized in the desired reference.
# Multi Stage Retrieval for Web Search During Crisis
## 3 Materials and Methods
### Okapi BM25 Ranking Scheme  
Okapi BM25 (Best Match 25) is a widely adopted ranking function used in information retrieval systems to estimate the relevance of documents to a given search query [21]. It is a probabilistic model that extends earlier methods like TF-IDF (Term Frequency-Inverse Document Frequency) by incorporating term frequency saturation and document length normalization. The BM25 score for a document \(D\) with respect to a query \(Q\), which is composed of individual query terms \(q_{1},q_{2},\ldots,q_{n}\), is calculated as follows:  
\[\text{Score}(D,Q)=\sum_{i=1}^{n}\text{IDF}(q_{i})\cdot\frac{f(q_{i},D)\cdot(k_ {1}+1)}{f(q_{i},D)+k_{1}\cdot\left(1-b+b\cdot\frac{|D|}{\text{avgdl}}\right)} \tag{1}\]  
where:  
* \(\text{IDF}(q_{i})\) is the Inverse Document Frequency of the query term \(q_{i}\). It measures the general importance of the term in the collection. A common formula for IDF is \[\text{IDF}(q_{i})=\log\biggl{(}\frac{N-n(q_{i})+0.5}{n(q_{i})+0.5}+1\biggr{)}\] (2) Here, \(N\) is the total number of documents in the collection, and \(n(q_{i})\) is the number of documents containing the term \(q_{i}\). The addition of 1 inside the logarithm (or other variants like adding 0.5 to the numerator and denominator) is to prevent division by zero for terms not present in any document or to smooth the IDF values.
* \(f(q_{i},D)\) represents the frequency of term \(q_{i}\) in document \(D\) (i.e., term frequency).
* \(|D|\) is the length of document \(D\), measured as the total number of words (or tokens) in it.
* \(\text{avgdl}\) is the average document length across the entire collection.
* \(k_{1}\) is a positive tuning parameter that controls the term frequency saturation. It dictates how quickly the contribution of a term's frequency to the score diminishes as the frequency increases. A typical value for \(k_{1}\) is between 1.2 and 2.0.
* \(b\) is another tuning parameter, usually between 0 and 1 (a common default is 0.75), which determines the degree of document length normalization. When \(b=1\), the scaling by document length is fully applied, and when \(b=0\), no length normalization is applied.  
Data Structures  
Efficient calculation of BM25 scores relies on the following structures:  
* **Inverted Index:** This is the primary data structure. It maps each unique term in the collection to a list (postings list) of documents that contain that term. Each entry in the postings list often stores the document ID and the term frequency \(f(q_{i},D)\) within that document. The document frequencies \(n(q_{i})\) (number of documents containing term \(q_{i}\)) can be derived from the length of these posting lists.
* **Document Length Storage:** An array or map is typically used to store the length \(|D|\) for every document in the collection, indexed by document ID.
* **Corpus Statistics Storage:** Variables to hold global statistics such as the total number of documents \(N\) and the average document length avgdl.