esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 3 Environment Description and Work Space Partitioning  
Most of the drones are equipped with modern positioning and distance-measuring sensors. Apart from these minimum requirements, even the most modestly equipped drones contain GPS and a few range-measuring LEDs for obstacle detection. Standard drones usually include the following positioning sensors: GPS, front/rear LEDs, camera system, forward/downward/rear vision systems, infrared sensing system, barometer, etc. Drones with basic equipment, based on FC complexity, can be controlled in different flight modes.  
-\(P\)-mode (positioning): works best when the GPS signal is strong. In addition to GPS, other sensors are used: stereo vision system, infra sensing system-obstacle avoiding, moving target tracking.  
-\(S\)-mode (sport): maximum maneuverability. Obstacle sensing disabled.  
-\(A\)-mode (altitude): only uses the barometer to sense altitude. Positioning is no longer available.  
Regarding work space partitioning, the current drone flight rules must be adhered to. The most important rules include the following:  
* maintain an altitude of 120 m
* stay at least 30 m away from other people
* keep the drone in line-of-sight  
From the perspective of the current study, the most crucial regulation is the first one, because it limits the volume (space) to be partitioned. On the other hand, if the drone is kept in visual line-of-sight (LoS), the WS partitioning may seem redundant, but the drone will not always be in LoS. The configuration of the WS can always be useful in the operation of unmanned aircraft, because often, if one of the sensors fails, the FC can only rely on the predefined path on the WS model.  
There are numerous 3D partitioning methods, such as 4-tree (2D partitioning), 8-tree (3D partitioning, split around a point), KD-tree (partitioning along one dimension), R-tree, and the most general, Grid (cube) rasterization.  
The basic concept of 4/8-tree partitioning is as follows: the area/space is divided into 4/8 subparts. Another division is made only on those squares/cubes where obstacles are located, or where parts of obstacles can be found. As a result, the resolution of the division increases, as the drone comes closer to the obstacle.  
A KD-Tree is a binary tree in which each node represents a K-dimensional point (hyperspace). R(rectangle)-Tree groups nearby objects and represents them with their minimum bounding rectangle at the next higher level of the tree [13].  
Voxel grid partitioning is the simplest, and it is also popular, yet not truly effective, because the dimensions of the blocks (cubes) remain the same, regardless of the proximity of the obstacle. On the other hand, the calculations are straightforward, explaining why this partitioning is used in the model studied in this paper.
## 5 Experimental Micro-Doppler Signatures
### UAV Characteristics  
The description of each UAV is provided in Table 5.  
Figure 15 shows the smallest UAV, a Metabird, and its spectrogram. The Metabird mimics a flying bird and is controlled by a smartphone. It is equipped with flexible wings made of carbon fiber, liquid crystal polymers, and an indestructible foam body. It flies by flapping its wings without the aid of a propeller. The micro-Doppler signature of the Metabird consists of periodic flaps of consistent velocities. The body has an undulating nature as it flies. The Doppler bandwidth changes over time due to the rotational behavior of the UAV. The Metabird does not always travel perpendicular to the radar.  
Figure 16 shows the medium-sized AKASO drone and its associated spectrogram. In this spectrogram, periodic shapes are present that are similar to the simulations. This spectrogram shows the behavior of a drone slowly descending in the vertical direction. This was observed through the maximum velocity of the blade tips, which was around 26 m/s.  
Figure 16: Akaso drone (medium UAV) and its associated spectrogram.  
Figure 15: Metabird (small UAV) and its associated spectrogram.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Metabird** & **Akaso** & **BlackHawk** \\  & & **Quadcopter** & **Quadcopter** \\ \hline Weight & 10 gm (0.022 lb) & 750 gm (1.65 lb) & 1775 gm (3.87 lb) \\ Body length & 16 cm (6.3 in) & – & – \\ Wingspan & 33 cm (13 in) & 38.9 cm (15.3 in) & 77.7 cm (30.6 in) \\ End-to-end span & – & 19.5 cm (7.7 in) & 26.0 cm (10.2 in) \\ Blade axis to blade axis & – & 16.3 cm (6.4 in) & 22.0 cm (8.7 in) \\ Blade length & – & 7.0 cm (2.8 in) & 9.5 cm (3.7 in) \\ Height & – & 10.5 cm (4.1 in) & 12.0 cm (4.7 in) \\ \hline \hline \end{tabular}
\end{table}
Table 5: UAV characteristics.  
Figure 17 shows the large BlackHawk drone and its associated spectrogram. The maximum blade tip velocity was much higher compared to the AKASO drone. This is due to the overall weight of the UAV being higher than the AKASO. We observed that the negative velocities showed more pronounced micro-Doppler peaks compared to the positive side. This was because the positive peaks were hidden behind the arms of the drone, concealing the sharp micro-Doppler pattern.
## 3 Circuit Design
### Analog Front-End
#### 3.1.2 Second Amplifying Stage  
A second gain stage is needed to increase the amplitude level of the input signal before multiplexing. Since this stage requires a relative large output swing, a two-stages OTA can be employed. Provided that the first stage has enough gain, the impact of this second amplifier on the input-referred noise is negligible, and its power dissipation can be reduced without affecting the noise performance. In fact, the noise of the second op-amp is dominated by the input transistors, designed to operate in weak-inversion region. If we limit the contribution to the equivalent input noise added by the second stage OTA to less than 0.3 \(\mu V_{rms}\) (_i.e._, \(1/10\) of the dominant contribution), a minimum current of \(100\) nA is needed in the input differential pair of the second op-amp. To be conservative, we set the bias current to \(200\) nA in the first stage, while a current of \(100\) nA is drawn by the second stage.  
Finally, nonlinear distortion may be of some concern in the second stage. Distortion arises from the non-linear high-resistance pseudo-resistors placed in the feedback path, which is driven by a large output voltage swing. For this reason the subthreshold MOS transistors were sized to have resistance values an order of magnitude higher than those in the first stage. This choice makes the signal current flowing through them always several orders of magnitudes lower than the signal through the capacitors, limiting the measured Total Harmonic Distortion (THD) generated by the stage below \(5\%\) even if the pseudo-resistors were modulated by a \(1.5\) V peak-to-peak \(1\) kHz sinusoid.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 4 Numerical Analysis
### Numerical Results
The other two schemes both suffer a performance loss as the interference level grows either inter layer or inner layer. As in Figure 3(b), under full carrier deployment, the proposed solution only achieves better performance when the transmit power of the Femto BSs reaches a certain level. When the transmit powers are lower, the stochastic single flow solution provides the HetNets with better utility because there are more available BSs and carrier bands in the Femtocells for each user to choose a better cell association opportunity. In particular, when cross layer interferences do not exist in Figure 5 ([1 0;0 1], [0 1;1 0]), the transmit power of both Macro BS and Femto BS does not have the same impacts on the network utility as the other carrier deployment circumstances for all three schemes. Figure 2: Network Sum Rate against User Number for Multi-Flow CA. (**a**) \(K=20\); (**b**) \(K=40\).
## 5 Applications
### Petrochemistry  
The petrochemical industry has a high demand for reliable analyzers enabling fast and accurate measurements of various chemical compounds in order to secure the high purity levels necessary to guarantee perfect performance. Concentrations of molecules such as H\({}_{2}\)O, H\({}_{2}\)S, CO\({}_{2}\) and C\({}_{2}\)H\({}_{2}\) have to be monitored closely and most notably with as little time offset as possible.  
TDLS enables very fast measurement cycles (\(<\)1 s) and offers very high measurement resolutions inherent to the system. Furthermore TDLS-based sensor systems require only negligible maintenance and do not need any consumables, so operating costs are very low [61, 67]. Because of these  
Figure 10: Absorption spectrum for gases generated by smoldering wood respectively brown coal fires [63].  
advantages, TDLS is ideally suited for petrochemical applications which have been using more conventional analyzing solutions such as gas chromatography, capacitance probes, broadband light spectroscopy or electrolytic moisture analyzers. Those systems however suffer from various drawbacks, e.g., slow update and response time, slow drift in calibration, sensitivity to changing background concentrations and high maintenance and consumable costs.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 4 The Proposed FuGeF Protocol
### Protocol Analysis  
**Theorem 1**.: _The Overhead complexity of the control messages in the network is \(O(N)\)._  
Proof.: To determine the overhead complexity in FuGeF, suppose at the beginning of each forwarding process, there are \(N-1\) nodes within the range of the sending node, such that \(N-K\)\(ORTS_{msg}\) is broadcasted to these nodes. The candidate nodes, in response to the received message, each sends \(C-K\)\(CTS_{msg}\) as well as a \(C\)\(ACK_{msg}\) from the selected candidate to the sending node. Thus the total number of control message in the network is \((N-K)+(C-K)+C=N-2K+2C\). Therefore, the overhead complexity of the control messages in the network is \(O(N)\)  
**Theorem 2**.: _The Space complexity for selection process in the network is \(O(1)\)_  
Proof.: FuGeF returns a single value for each node after processing the returned response for node selection. The returned chance value for each node is compared and possibly substituted until the best chance value is determined and chosen as the next hop. Therefore, the space complexity of the selection process in the network is \(O(1)\).  
**Theorem 3**.: _Unpredictability and response collection is ensured_  
Proof.: A fixed length collection window, if long enough, gives predictability and ensures response collection, while a dynamic (random) length collection window ensures unpredictability with no guarantee for response collection. FuGeF utilizes a pseudorandom based dynamic collection window. which exhibits dynamism within a certain fixed interval to ensure unpredictability and response collection.  
Figure 4: Fuzzy input membership mapping
# Penetration Performance of Steel Cylinders in Sand Foundations
## 5 Conclusions  
To study the influence of wall thickness of a steel cylinder on its penetration process, this paper used a model test and the CEL method. The research results are as follows:  
1. It can be seen from the test results that the penetration force of a steel cylinder increases gradually with increasing penetration depth, and the influence of wall thickness on the penetration force increases gradually. We established the relationship between the penetration resistance of steel cylinders and the penetration depth under different wall thicknesses. Through the relationship between the two, it can be seen that as the penetration depth increases, the rate of increase in penetration resistance increases.
2. From the numerical simulation results, it can be seen that wall thickness has a significant impact on the uplift of soil. With an increase in wall thickness of a steel cylinder, the uplift height of the soil inside the steel cylinder gradually increases. When the wall thickness increases from 1 mm to 4 mm, the uplift height of the soil inside the steel cylinder increases 2.6 times.
3. In this paper, the penetration process of steel cylinders in sandy soil foundations was studied through indoor tests and numerical simulations, and the penetration force and soil uplift during the penetration process of steel cylinders were studied. According to the comparative analysis of the two, the uplift of soil in the barrel increases with an increase in wall thickness. The operative volume ratio of the soil in the bucket is calculated by means of a formula, and it was found that it has a power function relationship with wall thickness. The research results can provide a reference for the application of penetration force during the penetration process of steel cylinders.  
Conceptualization, W.X. and H.R.; data curation, W.X. and P.J.; formal analysis, W.X. and H.R.; methodology, W.X. and P.J.; writing--original draft preparation, W.X.; writing--review and editing, H.R. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Soil heave height in a steel cylinder with different wall thicknesses after penetration: (a) \(t=\) 1 mm; (b) \(t=\) 2 mm; (c) \(t=\) 3 mm; (d) \(t=\) 4 mm.  
**Funding:** This research was funded by two grants from the National Science Foundation of China (51979128).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of the study are available from the corresponding author upon request.  
**Acknowledgments:** The authors appreciate the comments and suggestions by the editors and anonymous reviewers.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
###### Abstract  
WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.  
intrusion detection; human detection; channel state information; device-free passive Article
## 5 Conclusions  
This paper proposes a novel approach to vessel classification, using the normalization of sparse and incomplete vessel trajectory data into a universal set of features, which is applicable despite varying observation numbers. Our model utilizes the TOCAT (take one class at a time) design, a strategy typically used in support vector machine algorithms, but uniquely applied in this study to individual 'one versus rest' classification tasks using a variety of diverse artificial neural networks (ANNs). We derived distinct mathematical and topological representations from vessel trajectory data for each 'one versus rest' classification task, leveraging the proven capabilities of specific ANNs for recognizing certain vessel classes. The findings, which show a mean accuracy of 83% in a six-class classification task, suggest that the collaborative employment of these specialized ANNs could potentially outperform a single, larger ANN assigned to the entire classification task. During the experiments, we also pinpointed several critical aspects tied to data processing: primarily, the need for statistical sampling of the AIS signals from each vessel's trajectories. This method enables a robust estimation of vital parameters such as the vessel's speed, deceleration, and acceleration. Furthermore, as anticipated, the analysis highlights the importance of avoiding the use of overly complex algorithms, especially when the number of input variables is limited and the samples have not been collected through robust sampling procedures. Future research endeavors may explore the scalability and generalizability of the proposed approaches and extend their applications to other domains beyond vessel recognition and comparisons with the most recent techniques existing in the field.  
Conceptualization, P.M.B.; Methodology, P.M.B., G.M., M.B. and R.P.; Software, P.M.B. and G.M.; Validation, P.M.B. and G.M.; Formal Analysis, P.M.B.; Investigation, P.M.B., G.M., M.B. and R.P.; Resources, G.R. and G.C.; Data Curation, P.M.B., G.M., G.C., M.B. and R.P.; Writing--Original Draft Preparation, P.M.B., G.M., G.C., M.B. and R.P.; Writing--Review and Editing, P.M.B., M.B. and R.P.; Visualization, P.M.B., G.M., M.B. and R.P.; Supervision, P.M.B., G.M., M.B. and R.P.; Project Administration, P.M.B. and M.B.; Funding, P.M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The data are not publicly available due to legal agreements between the research institution and collaborating parties.  
The authors declare no conflict of interest.
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 2 Materials and Methods
### Data Setup  
In optical imaging, each image element receives a beam of light corresponding to a spatial location and has a different pixel value depending on the intensity of the light. The final image obtained by the camera is presented as the sum of the images constructed by all the light rays independently, as shown in Figure 5.  
Figure 4: Deep-learning model: (**a**) generative network; (**b**) discriminative network; (**c**) the self-attentive mechanism.  
The imaging process can be expressed as follows:  
\[E^{in}=\sum_{i=0}^{n}f(x_{i}) \tag{8}\]  
where \(x_{i}\) represents the beam of light emitted outward from the target object, and \(f()\) represents the mapping function of light intensity to the pixel value. When the light beam encounters scattering media during its propagation, it undergoes scattering to form a speckle image superimposed on the final imaging surface. The imaging process can be expressed as follows:  
\[E^{out}=K*E^{in}=K*\sum_{i=0}^{n}f(x_{i}) \tag{9}\]  
The principle of differential geometry states that all complex geometries can be approximated as a large collection of sufficiently small, basic geometric images (e.g., squares). Therefore, in this study, we propose the use of basic geometric elements in the training set so that the mapping relationship between the basic-element speckle autocorrelation images and the truth value autocorrelation images can be learnt by the deep-neural-network model. The model could then be extended to the mapping relationship between the complex target speckle autocorrelation images and the truth value autocorrelation images to recover the complex speckle autocorrelation images.  
To verify the autocorrelation recovery of the proposed method under complex target data, Fashion MNIST datasets were chosen as the test datasets for the experiment. Fashion MNIST is a set of common 28 \(\times\) 28 grayscale images. It is more complex than MNIST, and can thus, better represent the actual performance of the method.
## 4 Implementation
### Simulation  
The learning process was carried out during a fixed number of time-steps, which were divided into episodes of 512 time-steps. Gazebo's _max step size_\(T_{m}\) is an important parameter that indicates the time-step at which Gazebo computes successive states of the model. The default value is 1 ms, but, in this case, \(T_{m}=2\) ms was used, which gave enough stability and enabled a faster simulation. Thus, each iteration of the physic engine meant 2 ms of simulated time and those iterations could be computed as quickly as possible to be able to accelerate the simulation. Besides, the _real time update rate_\(U_{r}\) parameter indicates how many physic iterations will be tried in one second. The _real time factor_ is defined in Equation (10) and indicates how much faster the simulation goes in comparison with the real time. Thus, \(rtf=1\) indicates that the simulation is running on real time.  
\[rtf=T_{m}\cdot U_{r} \tag{10}\]  
In addition, a _frame-skip_\(N_{T_{m}}\) was defined to be able to get a reasonable control rate. In this application, \(N_{T_{m}}=4\) was used so the discrete time-step size is defined in Equation (11) and doing that we achieve a control rate of 125 Hz.  
\[T_{s}=T_{m}\cdot N_{T_{m}}=8\;\text{ms} \tag{11}\]  
Thus, the length of each discrete time-step is 8 ms and those steps are computed faster than real time. Gazebo does not allow the control of the physics engine iterations, so a Gazebo plugin was developed to be able to execute the simulation for some iterations and to be able to compute those iterations as fast as possible. When the plugin was told to run a time-step, it ran \(N_{T_{m}}\) physics iterations and the _real time update rate_ was set to 0 to compute those iterations as quickly as possible. Doing this, the _real time factor_ increased and enabled us to run the simulation about 5-10 times faster than in real time.  
Figure 3: Implemented architecture. ROS: Robot Operating System.  
At each step, the learning algorithm generates an action, specifically a velocity command, which is sent to the mobile base. After executing it and at the end of the step, a reward signal is given evaluating the quality of the action performed by the base. The action is the same for all the experiments and is defined in Equation (12).  
\[a=[v_{x}\;\;v_{y}\;\;\omega_{z}] \tag{12}\]  
To be able to control the mobile base, the _gazebo planar move_ plugin was used, which enabled the sending of velocity commands to the omnidirectional base. In addition, it published the odometry information, which was used to know where the robot is respect to a parent coordinate frame. The path planning and control of the arm was made using MoveIt! [39] and enabled us to plan collision free trajectories. Besides that, MoveIt! uses an internal planning scene where objects can be added to be taken into account when the trajectory is planned.