esa-sceva/satcom-chunk-collection huggingface dataset:
# Pixel Resolution Imaging in Parallel Phase-Shifting Digital Holography
## 2 Principles and Methods
### Hologram Reconstruction
Figure 3b shows that the implementation of the OSPM for enhancing the sampling frequency of the light field can be simplified to perform nearest-neighbor interpolation on the extracted sparse phase-shift holograms \(I_{0}(x,y;\delta)\) in the form of HIM, obtain four interpolated aligned phase-shift holograms \(I(x,y;\delta)\), and calculate the object light field \(u(x,y)\) according to Equations (1) and (2). Compared with the traditional numerical calculation implementation of interpolation [23,30], a simpler implementation can be represented by the convolution of an image and an interpolation kernel using the following equation [26]:  
\[I(x,y;\delta)=I_{0}(x,y;\delta)*f(x,y) \tag{3}\]  
where \(*\) denotes the convolution operation, and \(f(x,y)\) represents the interpolation kernel. Table 1 shows the classical convolutional interpolation kernels, including nearest-neighbor interpolation [31]. To reduce the calculation time, [26] proposed a fast image reconstruction technique based on the convolution theorem. Figure 3: Proposed oversampled super-pixel method (OSPM) for PPSDH: (**a**) schematic diagram and (**b**) calculation procedure. The proposed OSPM increases the frequency sampling bandwidth of the object light diffraction field from \(B_{SPx}=1/2\Delta\) to \(B_{OSPMx}=1/\Delta\), which is equivalent to increasing the imaging lateral resolution in the case of \(B_{SPx}<B_{X}<B_{OSPMx}\). In addition, the maximum lateral resolution can also be increased from \(4\Delta\) to \(2\Delta\), which is twice the pixel pitch \(\Delta\) of the actual image sensor. To further increase the sampling frequency, additional physical operations such as a source or sensor shifting are required [34]. However, due to the limited bandwidth \(B_{x}\) of the diffraction field caused by the minimum recording distance, which is equal to the thickness of devices such as the BS in Figure 1, further PSR imaging in PPSDH has a smaller effect on the resolution improvement than the digital holographic microscopy [34,38]. Since the proposed OSPM can be regarded as the HIM based on nearest-neighbor interpolation, the image resolution and lateral spatial resolution of the reconstructed image of the HIM are not worse than those of the SPM. In addition, the nearest-neighbor interpolation--which may be ignored in PPSDH due to poor reconstruction accuracy--has a smaller kernel size and, thus, can retain more high-frequency information of the target, which perhaps corresponds to a higher lateral resolution.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results
### Performance Comparison of the Three Classifiers Using the Macro-Averaging F1-Score as a Metric
This outcome showed that the ML-based classifications, regardless of the type of the classifier, were more effective in the multi-class identification of UWB data than the traditional approaches described in Section 2.
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## Methods
### Airborne LiDAR 3D Topographic Survey  
As part of a regional coastal monitoring program, the Aquitaine coastal zone is surveyed every year with airborne LiDAR by the IGN in cooperation with the BRGM. In the present work, the LiDAR topographic survey of the Aquitaine coast performed between 4 and 7 of October 2017 was used for comparison with the RTK-GPS and Pleiades observations. Note that the LiDAR survey was executed approximately a month before the field campaign and Pleiades acquisition. The airborne-LiDAR survey was performed with the Leica ALS70-HP LiDAR, mounted on an aircraft, and acquiring topographic information with a density of 8 points per square meter. The planimetric coordinates of the LiDAR point-cloud were referenced to the Lambert 93 coordinate system, with a precision of 30 cm, and terrain topography referenced to the NGF-IGN 1969 datum, with an accuracy of 15 cm (information provided by the LiDAR survey metadata). Simultaneously with the LiDAR acquisition, high resolution (10 cm) aerial orthophotography was obtained with an 8-head IGN V2 (focal length of 135 mm) camera. The position and orientation of the images were obtained from the GPS and inertial sensors embedded in the aircraft. Two products were obtained from this flight: a DEM of the study area, with a spatial resolution of 1 m, and an orthophoto map with 10 cm resolution. For comparison with the other datasets, the coordinate systems of these LiDAR products were converted to WGS 84 UTM 30N.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 2 Related Work
### Unknown Internet Traffic Identification
This leads to low computational efficiency and limits scalability. To overcome the shortcomings of existing unknown traffic identification methods, we aimed to establish a hybrid detection model in this study that not only enables closed-world traffic classification but also detects unknown application traffic. The model autonomously learns based on the knowledge of existing datasets and detects unknown traffic categories without requiring training of the classifier from scratch each time.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results  
This section contains the main contribution of this paper--the results demonstrating the performance and comparing the two LoRaWAN deployment alternatives along the three tracks (each presents as a separate sub-chapter):  
* **Performance evaluation**--provides an evaluation of the performance of the public and the private networks. First, we report the results of the outdoor measurements to confirm our claim about the importance of channel selection and its impact on the network parameters (signal strength, signal-to-noise ratio, and loss rate). Further, we provide extensive experimental measurement results for indoor scenario, coverage, penetration, and loss rate, which should give a sufficient overview of the LoRaWAN behavior in the indoor environment.
* **Security evaluation**--gives accurate information about the recent changes in the LoRaWAN protocol based on the newest documentation. We look at the basics of information security parameters, such as authentication, encryption, and data integrity, but also at key establishment and key update. We also discuss possible vulnerabilities and compare the older with the newest version of the LoRaWAN protocol. Finally, we summarize the differences regarding security in private and public networks.
* **Deployment ease evaluation**--introduces the deployment difficulties, a methodology for the deployment of the public or private network, and evaluates the possible expenses in the context of private and public networks.
## 2 Materials and Methods
### Influence of Atmospheric Pressure and Temperature  
The atmospheric pressure and temperature also have an influence on the atmospheric transmittance. Pressure and temperature measurements are usually available for most tower-based flux systems. However, if these values are included in the LUTs for the estimation of atmospheric transmitances, many more simulations are needed and more uncertainty will be introduced into the LUTs. Therefore, following [PERSON] et al. (2018), we used an empirical equation to compensate for the influence of pressure and temperature by using an \"equivalent\" RTPL (ERTPL). According to [PERSON] et al. (2018), the O\({}_{2}\) transmittance can be empirically approximated as:  
\[\mathrm{T}(\lambda)=\exp\left[-\left(10^{C(\lambda)}\left(\frac{p}{p_{0}} \right)^{n}\left(\frac{T_{0}}{T}\right)^{m}U\right)^{a}\right] \tag{7}\]  
where \(p_{0}\) and \(T_{0}\) are the reference atmospheric pressure and temperature, respectively, \(U\) (atm\(\cdot\)cm) is the total absorber amount in the path length, and \(C^{\prime}\) is the spectral coefficients. The MODTRAN code also follows this approximation. For oxygen absorption, the values of the parameters \(a\), \(m\), and \(n\) are set to 0.5641, 0.1936, and 0.9353 (refer to [PERSON] et al. (2018) for details). According to the Beer-Lambert Law, assuming homogeneous extinction medium, the transmittance is exponentially related to RTPL. Therefore, the ERTPL compensating the influence of pressure and temperature can be expressed as:  
\[\mathrm{ERTPL}=\left(\frac{p}{p_{0}}\right)^{0.9353}\times\left(\frac{T_{0}}{T }\right)^{0.1936}\times\mathrm{RTPL}, \tag{8}\]  
If the pressure and temperature for a particular set of conditions are \(p\) and \(T\), respectively, the ERTPL can be calculated using the equation. Therefore, only LUTs simulated under the reference pressure and temperature conditions are needed, and the influence of the varying pressure and temperature can be compensated for by using the ERTPL instead of the real RTPL.
# Indoor Emission Sources Detection by Pollutants Interaction Analysis
## 2 Measurements and Method
### Correlation Coefficient-Based Emission Sources' Detection
#### 2.3.1 Time Series Analysis  
Given a time series data \(\mathbf{X}=\{\mathbf{x}(1),\mathbf{x}(2),\ldots,\mathbf{x}(t),\ldots,\mathbf{x}(T)\}\) involving emission \(L\) events, in which \(\mathbf{x(t)}=\{a_{1}(t),a_{2}(t),\ldots,a_{M}(t)\}\) denotes a data sample consisting of \(M\) chemical contaminants and \(T\) represents elapsed time. A single point is insufficient for emission sources detection, but a time series could be very long, sometimes containing millions of observations. To analyze the relationship between pollutants, it is desirable to apply a sliding window \(w\) to \(\mathbf{X}\) that will produce a sequence of shorter time series. Figure 2 shows the procedure of sliding windows subsequence extraction with any of the real-valued representations.  
\begin{table}
\begin{tabular}{l l} \hline \(\mathbf{X}\in\mathcal{R}^{T\times M}\) & The basic data structure, which can cover a majority of the indoor conditions \\ \(t\) & The index of the time point \\ \(T\) & Total elapsed time \\ \(\mathbf{x}(t)\in\mathcal{R}^{1\times M}\) & Samples at time \(t\) \\ \(w\) & The size of the sliding window \\ \(M\) & The total number of observed pollutants \\ \(m\) & The index of the pollutant \\ \(N=T-w+1\) & The total number of subsequences \\ \(i\) & The index of the sliding window \\ \(\mathbf{C}\) & A set of matrices extracted by slicing time series with sliding window \(w\) \\ \(\mathbf{c}_{i}\in\mathcal{R}^{w\times M}\) & An extracted subsequence from the \(i\)th sliding window \\ \(r_{i,j,k}\) & The correlation coefficient of two pollutants (i.e., \(j\)th and \(k\)th) for the \(i\)th sliding window \\ \hline \end{tabular}
\end{table}
Table 2: A summary of the notation used in this paper.  
Figure 1: The experimental environment.  
As a result, we stored all extracted subsequences as \(C=\{\mathbf{c}_{1},\mathbf{c}_{2}\ldots,\mathbf{c}_{n}\}\), where \(n=T-w+1\) is the number of subsequences and \(\mathbf{c}_{i}=\{\mathbf{x}[(\mathbf{t}):(\mathbf{t}+\mathbf{w}-\mathbf{1})]\}\) represents a subsequence. Note that the corresponding label \(y_{i}\) is included in the calibration data according to the events of emission sources for each subsequence.
## 3 Technical Background
### Wireless Charging Implementation Circuit
Furthermore, when the voltage signal reaches to a threshold value of 1.2 V, the booster circuit generates an output pulse signal around 3.3 V. However, this value can be increased or decreased by attaching an external resistor to the terminal V\({}_{\text{SET}}\) and to the ground as described in the circuit data sheet [35];
4. Dipole and Patch Antennas: Figure 2g shows a dipole antenna with a 360-degree reception range, where as a patch antenna shown in Figure 2f has a 120-degree reception range. Both antennas operate at 915 MHz ISM frequency band. Furthermore, in order to obtain optimal reception, the antennas lengths are designed to have at least half of the utilized wavelength, which equals to 32.76 cm, and normalized 50 \(\Omega\) impedance. The patch antenna is able to receive more power as it has a gain of 6 dBi, as compared to the dipole antenna that has a gain of 1 dBi. 5. Access point (WSN-AP-01): The Access point in Figure 2b has a USB connection that can be connected to a personal computer (PC) to display the information of the received packets, as well as, the amount of the received power. It also has the capability to receive information from 8 sensor boards. It receives the packets at a frequency of 2.4 GHz that is similar to the one used for radio modules, and displays the information on the PC. It maintains a counter for each of the sensor boards to record the total number of packets received by each sensor board;
6. PICkit 3 programmable debugger (PG164130): The PICKit shown in Figure 2e is used to program the MCU on the sensor board or the access point. The user can update the PICkit tail attached to it and with the help of MPLAB IDE program, the microchip code.
## 3 Materials
The SAR dataset includes 120 images acquired in X, C and L bands (central wavelengths: 2, 5, and 21 cm respectively) in dual polarization (HH and VV for X band) and in quad-polarization (HH, VV, VH and HV for C and L bands). Flights were weekly carried out from the 9 th of April to the 2 nd of August by an airborne E-SAR (Experimental SAR), operated by the German Aerospace Center (_Deutsches Zentrum fur Luft- und Raunfahrt_--DLR). The E-SAR sensor was on board a Do228 aircraft; although a total of 16 radar flights were executed during the AgriSAR 2006 campaign, the 12 performed in the E-W direction were considered in this research. The deployment of corner reflectors of 20 cm \(\times\) 20 cm during one of the in situ campaigns allowed obtaining multi-look, geo-coded and calibrated \(\sigma^{\circ}\) products at a raw spatial resolution, \(R_{\rm S}\), of 2 m.  
During the airborne SAR acquisitions, high variability of \(m_{\rm v}\), \(F_{\rm v}\) and \(LAI\) occurred over both bare and vegetated soils, including different cultivars. Thus, AgriSAR 2006 dataset allows investigating (i) how \(\sigma^{\circ}\) is controlled by changes of SVV and, (ii) how SAR configuration (acquisition geometry, frequency and polarization) rules the relationships between \(\sigma^{\circ}\) and SVV.
## 5 Impact of Color Conflicts Caused by Late Arrivals, Node Mobility or Topology Changes
### Conflict Causes
#### 5.2.3 Existence of Asymmetric Links  
Another reason for color conflicts is the existence of asymmetric links as shown in [29] where we propose a solution to limit the number of such conflicts. However, this number cannot be nullified. Notice that conflicts created by topology changes, asymmetric links, node mobility or late node arrivals cannot be avoided even by using exclusively links of good quality.
## 5 Results and Discussion
### Antioxidant Activity Analysis of Theabrownins
#### 5.8.2 Hydroxyl Radical Scavenging Activities  
Hydroxyl radicals are strongly oxidizing free radicals generated during human metabolism. They can lead to a series of diseases, such as gastric ulcers and cancers [43]. The phenolic hydroxyl groups of theabrownins can combine with metal ions to prevent the generation of hydroxyl free radicals. Upon increasing the theabrownins concentration in the range of 0.5-5 mg/mL, the scavenging rate of hydroxyl radicals increased. When the concentration of theabrownins was 5 mg/mL, and the clearance rates of theabrownins in solid-state and liquid fermentation were 62.39% and 70.91%, respectively. The scavenging rate of theabrownins to hydroxyl radicals was lower than that of VC (Figure 3C,D).  
8.3 The Scavenging Ability of Theabrownins to Superoxide Anion Radical (O\({}_{2}\)\({}^{-}\)\({}^{\bullet}\))  
The superoxide anion is a reactive oxygen radical that harms the body by disrupting normal cell functions and affecting amino acids and other biomolecules [44]. When the concentration of theabrownins in solid-state and liquid fermentation was 0.2 mg/mL, the clearance rates were 4.22% and 11.04%, respectively. When the concentration was 1 mg/mL, the clearance rates were 55.07% and 68.78%, representing increases of 92.33% and 83.94%, respectively (Figure 3E). The clearance rate of liquid-fermented theabrownins was better than that of solid-fermented theabrownins, both of which were lower than that of VC. According to the calculated \(IC_{50}\) value, the superoxide radical scavenging effect of the liquid-fermented theabrownins, solid-fermented theabrownins, and VC were 0.5152, 0.4473, and 0.4100 mg/mL, respectively.
# Colored 3D Path Extraction Based on Depth-RGB Sensor for Welding Robot Trajectory Generation
## 3 Experimental Setup  
The integrated vision system incorporates an RGB-D camera (Intel RealSense D435), an active stereo depth camera to compute the stereo depth data for real time. It also has an optional infrared (IR) projector that assists in improving depth accuracy. The sensor is physically supported on a test arm that will allow an image acquisition from a top view of the work object at a distance ranging from 30 to 70 cm over the welding work zone, as shown in Figure 3.  
Figure 3: Experimental setup: camera mounted on a pedestal with top view of the working object.  
Figure 2: Point cloud color map registration: (**a**) depth information; (**b**) color information; (**c**) colored point cloud through RGB registration.  
The proposed robotic system consists of an RGB-D camera that captures the surface point cloud of the workpiece, the welding seam detection algorithm that locates the color seam region in the input point cloud, and the trajectory generation method that processes the point set and outputs a 3D welding trajectory.  
The image acquisition and trajectory planning algorithms implementation were carried out on a personal computer with the Windows 10 operating system and operating with an Intel i7 CPU @ 2.40 GHz, with the necessary USB 3.0 ports required for the communication with the RealSense D435 camera.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 3 Our Merging Approach: Ring Reunion Algorithm
### Identification of New Overlays  
For the merging of several unconnected overlays, at first it is necessary that at least one node in one overlay (the initiator) makes contact with a node from an other overlay (contact node) to start a merging algorithm. Finding a first single node in an existing overlay is the classical bootstrap problem in peer-to-peer overlay network. Common solutions are to use either (a) to keep track of previously know hosts (passive list), (b) to use an external/public list (active list) provided out of band, or to (c) actively search for network members.  
A passive list, as proposed in the Ring-Unification Algorithm [7; 32], is a simple list of known contacts that is maintained while nodes are part of an overlay network. Each node keeps a list of node contacts (IP address, port, node ID) associated with nodes that were encountered. Nodes which are detected to be offline are moved from the routing table into the passive list, where they are periodically tested for reachability. Once nodes in the passive list are detected to be reachable again are removed from the list and are considered as possible contact nodes from a separated overlay that is to be merged. The disadvantage of using the passive list is that only nodes from the original routing table are considered to be mergeable nodes. In case of network partitioning events only those nodes can be found, which have been known previously.  
In order to avoid this restriction, we introduce an alternative procedure to find new or separated overlays: a public list of randomly chosen nodes is maintained individually at each participating node. The public contact list itself is obtained by every node from the bootstrap node during the join process.  
We decided to limit this public contact list to a maximum of 160 nodes which is equal to the number of finger entries in our Chord simulations. Additionally, the public list of a specific node \(n\) could be updated with nodes which want to join the overlay and therefore visit node \(n\) as the first-contact node. IP-scans could be used to search actively for further separated overlays. Similar to the passive list, nodes on the public list are periodically probed to be reachable and considered as mergeable contacts. In contrast to the passive list, nodes are not removed from the public list if they are found to be reachable. Only in thecase of a size-limited list, nodes might be replaced by other (better suited) nodes. Within our evaluation, we compare the public list approach to the behavior of the passive list without updating the public list, since the number of participating nodes is clear during simulations.
## 2 Finite Element Analysis for Bridge Structures with Damage and Deterioration
### Pier Model with Scour  
The pier's dimensions and the FE model are shown in Figure 6. The observation points are marked by red points No. 1, 2, and 3, where the powers generated by the MEMS vibrational power generator were to be calculated. The concrete material properties are as follows: elastic modulus 30 GPa, Poisson's ratio 0.2, and density 2400 kg/m\({}^{3}\). The \(x\)-axis pointed toward the river flow direction (perpendicular to bridge longitudinal), the \(y\)-axis toward the bridge longitudinal, and the z axis toward bridge transverse.  
Two cases were considered: one free from and the other one subject to scouring. As shown in Figure 7, the scouring-free pier model had a uniform riverbed of height 3000 mm and the scouring pier model had an inclined riverbed linearly varying from 500 mm at P1 (upstream side) to 3000 mm at P4 (downstream side). The riverbeds were modelled by fixed boundary conditions for simplicity.