esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural-Network-Assisted Polar Code Decoding Schemes
## 2 Preliminaries
### Decoding of Traditional Special Nodes
#### 2.2.2 Fast Decoding of an SPC Node  
For an SPC node with a code length of \(N_{S}\), if \(\sum_{i=1}^{N_{S}}\beta_{i}=0\), \((\beta_{1},\beta_{2},\cdots\beta_{N_{S}})\) is its decoding result. Otherwise, find \(\beta_{p}\), which is the smallest absolute LLR of \((\beta_{1},\beta_{2},\cdots\beta_{N_{S}})\). Let \(\beta_{p}\)\(=1-\beta_{p}\). Then, \((\beta_{1},\beta_{2},\cdots\beta_{N_{S}})\) is the decoding result.
## 3 Methodology
### System Implementation  
In this study, the monitoring system was applied to monitor the assembly line process for producing door-trim at an automotive manufacturing in Korea, as shown in Figure 3. The developed IoT-based sensor consists of a Raspberry Pi [68] as the single main board and Sense-HAT [69] as an add-on sensor board. Raspberry Pi is a small single-board computer with the dimensions of 85.60 mm \(\times\) 53.98 mm \(\times\) 17 mm, weighing only 45 g, and is affordable at approximately $25-35 USD. It has USB, LAN, HDMI, audio, and video ports for various input and output operations. In addition, general-purpose input-output (GPIO) connectors enable additional devices, or add-on boards such as sensors, to be connected to the main board [70]. The detailed specifications of the Raspberry Pi board can be seen in Table 1. The Sense-HAT board is an add-on sensor board that measures temperature, humidity, accelerometer, and gyroscope data and is designed as an official add-on board for the Raspberry Pi. The detailed specifications of the Sense-HAT board can be seen in Table 2. The Sense-HAT board is attached to a Raspberry Pi via GPIO 40 pins. The assembled and real-case implementation versions of the IoT-based sensor device can be seen in Figure 3.  
In this study, we developed a python-based program as a client using the supplied official application programming interface (API) to gather sensor data from IoT-based sensors [71]. The IoT-based sensors continuously collect temperature, humidity, gyroscope, and accelerometer data, which are transmitted to a cloud server wirelessly. As can be seen in Figure 3, an IoT-based sensor device is attached to the desk of a workstation panel along the assembly line process. The IoT-based sensor senses the environmental conditions and sends the sensor data to a cloud server every 5 s. The sensor data are processed by the big data processing system and analyzed further in real-time. Finally, the historical sensor data are saved in MongoDB and presented on a web-based monitoring system in real-time.  
Figure 2: An example of sensor data generated by the IoT-based sensor presented in JSON format **(a)**; and when stored in NoSQL MongoDB **(b)**.
# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## 5 Experimental Evaluation
### Example 4: Time Consumption  
In this section, we compare the approaches in terms of their feature selection efficiency. The time consumption here merely records the feature selection time, excluding the classification cost. All of the tests were implemented in Matlab on an Intel Core i7-4790 CPU (@3.6 GHz) with 32 GB memory (Intel Corp., Santa Clara, CA, USA). We respectively selected \(k^{\prime}\) (\(k^{\prime}\in\{100,300,500,700,900\}\)) features on the enrol dataset and recorded the time consumption of each compared approach. As illustrated in Figure 3, ELC and CMFS are comparably efficient to converge, while MIFS is most time-consuming, which may be mainly attributed to its involved label clustering process.  
Figure 3: Time consumption of each multi-label feature selection approach on the enrol dataset.  
Figure 2: Residual label correlation information: (**a–c**) are the residual scales of the label correlation information that are not embedded by the feature selection approaches on the emotions, birds, and enrol datasets, and the lower of the residual scale is the better.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} V2X & vehicle-to-everything \\ OTFS & orthogonal time frequency space \\ FTN & faster-than-Nyquist signaling \\ SEFDM & spectrally efficient frequency division multiplexing \\ BER & bit error rate \\ ADL-MP & adaptive damping log-domain message passing \\ EVA & extended vehicular A model \\ HST & high speed train scenario \\ OFDM & orthogonal frequency division multiplexing \\ IDI & inter-Doppler interference \\ ICI & inter-carrier interference \\ ISI & inter-symbol interference \\ DD & delay-Doppler \\ EVD & eigenvalue decomposition \\ TF & time-frequency \\ FFT & fast Fourier transform \\ IFFT & inverse fast Fourier transform \\ ISFFT & inverse symplectic finite Fourier transform \\ DFT & discrete Fourier transform \\ IDFT & inverse discrete Fourier transform \\ \end{tabular}
### Functionally Graded Lattice Structures Incorporating a Thickness Gradient under Compressive Loads
This was accompanied by the near-total loss of load-bearing capacity until the densification stage began. [PERSON] et al. [18] studied two lattice structures produced with SLM using grade 23 Ti-6 Al-4V in quasi-static compression. The lattices included an identical linear thickness change along the compression direction and were designed with high average relative densities of 29% for the cubic lattice and 48% for the hexagonal honeycomb lattice. The reported specific energy absorption of up to 50% strain was 40% lower for the cubic lattice and 10% higher for the hexagonal lattice. Upon rotating the lattice geometries by 90 degrees, with the thickness gradient applied to the compression direction, the graded cubic and hexagonal lattices showed increased absorption capabilities of 68% and 19% compared with the uniform lattices, respectively. A study by [PERSON] et al. [19] considered a TPMS lattice made of 304 L stainless steel produced with Selective Laser Melting. Five different continuous, linear thickness changes oriented along the compression direction were examined. The relative density was kept constant at approx. 56% for all specimens. The experimental verification of two thickness gradient choices showed nearly identical load-displacement curves and did not allow for a clear ordering of their specific energy absorption capabilities. [PERSON] et al. [20] compared a BCC lattice with uniform and nonlinearly changing thickness. Additionally, a variant with non-uniform unit cell height was considered. The specimens had an average relative density of approx. 18%, were manufactured via SLS using PA2200, and were compressed in a quasi-static manner. In a strain range of up to 55%, both the uniform lattice and the one with non-uniform cell height showed an almost linear increase in specific energy absorption, with the latter being slightly higher. The thickness-graded lattice exhibited far lower SEA during most of the compression procedure, but its SEA curve grew approx. cubically and reached a performance level almost equal to the uniform and size-graded variants at their respective final strain values.
## 3 Basic Assumptions
### Background of Elliptic Curve Cryptography (ECC) Algorithm  
The idea of the Elliptic Curve Cryptography (ECC) algorithm is used in our proposed research. As so here, the basic features and characteristics of ECC are described. An asymmetric based elliptic curve is used in the key cryptosystems of ECC. The mathematical expression for the elliptic key of the generic ECC algorithm is given below [34].  
\[y^{2}=x^{3}+ax+b\ (mod\ p)\ (\text{With $a$ and $b$ constants}) \tag{1}\]  
Those integers have to satisfy the ECC properties \(4a^{3}+27b^{2}\
eq 0\) as so to steer clear of singular points. A trapdoor function is used here; it is a one-way function it has a simple computation process in one direction. Hence, it is a one-directional process, and it becomes hard to compute in the opposite direction. In terms of key size, ECC is the most significant. Table 3 shows the key size comparison of DSA/RSA and ECC with equal security. Compared to other cryptographic key methods, it is very faster, effective as well as lightweight. It is able to provide short and fast keys during the communication process.  
Figure 6: Working of Black hole attack [32].
## 3 Results and Discussion
### Adsorption Study  
The influence of process parameters (equilibrium time, pH, initial concentration of MB, adsorbent dosage, and process temperature) on MB removal using nZVISLP was investigated systematically. In this regard, the experiments were conducted with each variable in different ranges as follow: C\({}_{0}\) (5-40 mg/L), contact time (0-300 min), pH (4-10), temperature (25-45 \({}^{\circ}\)C), and adsorbent dosage (0.4-1.2 g/L). The analyses of these experiments are described below.
# TwinNet: A Double Sub-Network Framework for Detecting Universal Adversarial Perturbations
## 4 Method
### Architecture of the TwinNet Framework  
A TwinNet framework comprises two sub-networks sharing the same network architecture (Figure 4), which are used to enhance normal-related features and adversarial-related features respectively: one is the target classifier \(f_{tagt}\), which is a popular DNN classifier, and the other is the shadow classifier \(f_{shdw}\), which is trained based on \(f_{tagt}\). These two sub-networks have exactly the same parameters for their feature extraction layers while maintaining different parameters for their classification layers independently. When it comes to normal examples, \(f_{tagt}\) and \(f_{shdw}\) will output normal and adversarial results, respectively. As for adversarial examples, they will both output adversarial results.  
The detecting unit is responsible for fusing the results output by the sub-networks. According to the fusion result, TwinNet will determine the type of example and adopt different strategies: if example was evaluated as adversarial, it would be rejected by the system; If not, a classification label would be computed based on the output of \(f_{tagt}\), because \(f_{tagt}\), which is trained for legitimate users, possesses better classification accuracy on normal examples. The detailed mechanism of the fusion method and the evaluation criterion are presented in Section 4.4.  
Formally, let \(f(x)\) denote the output vector of sub-networks in TwinNet for a given input example \(x\). Thus, \(f(x)\) is the set of class-membership probabilities for classes \(C\) that equals to  
\[P(C|x,\theta_{comm},\theta_{i}),\ \theta_{i}\in\left\{\theta_{tagt},\theta_{ shdw}\right\} \tag{2}\]  
where \(\theta_{comm}\) denotes the common parameters shared by two sub-networks; and \(\theta_{tagt}\) and \(\theta_{shdw}\) are the classification layer parameters of \(f_{tagt}\) and \(f_{shdw}\), respectively. Furthermore, the detection rule is  
\[\left\{\begin{array}{ll}reject,&\text{if $x$ is adversarial}\\ c^{*}=argmax_{c}P\big{(}C|x,\theta_{comm},\theta_{tagt}\big{)},&\text{if $x$ is normal} \end{array}\right. \tag{3}\]  
where the label \(c^{*}\) is the corresponding class of the highest element in the vector output by \(f_{tagt}\) if the example is evaluated as normal.
## 2 Study Area and Data  
The hyperspectral remote sensing monitor employed in this experiment is the HY-9010-U unmanned aerial vehicle (UAV)-mounted hyperspectral imaging equipment, which was developed and produced by Hangzhou Spectral Imaging Technology Co., Ltd. (Hangzhou, China), as shown in Figure 1a, the details can be seen in Table 1. The site of data collection is near the coast of Yantai, with favorable aquatic conditions. The seawater in this area is 1-20 m in depth, crystal-clear, and free from various obstructions such as high-rise buildings. Also, the flat terrain along the coast is conducive to taking off and landing of the UAV. During the experiment, the weather was sunny, and the lighting conditions were good, ranging from 700 to 895 lux, which was conducive to the collection of hyperspectral images by the unmanned aerial vehicle. The data were collected on 9 October 2022, by deploying the UAV that mounted with a hyperspectral camera to perform aerial detection of the sea surface, the unmanned underwater vehicle (UUV) (Figure 1b) was used as the underwater loading platform, and the acquired hyperspectral data of underwater targets are shown in Figure 1c. The experimental area is an aquaculture zone where suspended particles in the water can reduce light penetration and may have an adverse effect on the experiment.  
The UUV shown in Figure 1b is approximately 50 cm in length and 50 cm in width. As can be seen from Figure 1c, the channels of R, G, and B correspond to the 111 th, 60 th, and 32 nd bands, respectively. The red area is the real reference of the UUV on the ground. To evaluate the impact of the UUV on target detection performance at different water depths, the hyperspectral dataset at four different water depths: 0.1 m, 1.5 m, 3.0 m, and 5 m were collected in this study. The number of samples at different depths is listed in Table 2. Light waves of different wavelengths are absorbed to varying degrees when penetrating water, showing a significant variation in the intensity of absorption. Since there is a valley in the absorption spectrum of water at 300 nm to 900 nm, it is favorable for light waves of this wave band to penetrate water, while those of other wave bands cannot penetrate due to the absorption by water.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Imaging Mode** & **Dispersive Push-Broom Mode** \\ \hline Spectral range & 400–1000 nm \\ Spectral resolution & 2.8 nm \\ Spatial resolution & 7 cm \\ Number of spatial pixels & 1920 (1\(\times\)), 480 (4\(\times\)) \\ Number of spectral bands & 1200 (1\(\times\)), 300 (4\(\times\)) \\ Slit & 25 \(\mu\)m \\ Relative aperture & F2.6 \\ Output pixel bit depth & 12 bits \\ Maximum frame rate (full spectrum acquisition) & 128 fps \\ Lens focal length & 12.5 mm/35 mm interchangeable. \\ Height of UAV & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the HY-9010-U UAV -mounted hyperspectral imaging instrument.  
Figure 1: (**a**) HY-9010-U UAV -mounted hyperspectral imaging instrument; (**b**) UUV; (**c**) The visible light image corresponding to the hyperspectral image where the red area indicates the location of UUV.
## 3 System Building Blocks
### Acquisition  
For this application, it is necessary to use cameras that are capable of obtaining not only the visual characteristics but also the geometric information of the scene either directly or indirectly. In this case, the scene corresponds to faces positioned in front of the camera so that information related to depth can be extracted. In the development of this application, three types of cameras were considered and studied, namely plenoptic cameras (also known as light-field cameras), time-of-flight cameras and stereo cameras.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
###### Abstract  
The importance of synergy with industry lies in the possibility of experimental validation of the work research results. With Software-Defined Radio (SDR) platforms, it is possible to implement a physical layer and to have tests with hardware in a real environment. In this paper, we investigate the validation of an impulsive noise resistant physical layer based on Orthogonal Frequency Division Multiplexing (OFDM) and an interesting concatenation of forward error correcting codes: Rank metric Code (RC) and Convolutional Code (CC). We fully design and implement a new block namely RC Encoder + WiFi Mapper in GNU Radio, which acts as a forward error correcting code to mitigate impulsive noise occurring in substations. After showing by simulations that using this coding scheme is very efficient in mitigating the bursty nature of impulsive noise, we then confirm that the same performance is maintained even with various impulsive voltages and experimental scenarios, which confirms the high performance of the proposed approach.  
smart grid; impulsive noise; channel coding; OFDM; software-defined radio; USRP; experiments +
Footnote †: journal: Sensors
## 2 Materials and Methods
### D-InSAR Data Processing  
This study utilized the D-InSAR technique for processing SAR images to retrieve radar line-of-sight (LOS) deformations throughout the complete time series [23]. The methodology involves the computation of separate interferograms using SAR images acquired during contiguous time intervals. In this approach, the secondary image obtained from the initial distinct interferogram is utilized as the primary image for the subsequent interferogram [28; 29; 30]. Subsequently, the LOS deformation is converted into vertical displacement. Ultimately, the vertical subsidence of the Earth's surface is determined for each time interval and superimposed to ascertain the comprehensive subsidence over the entire investigation period. In this study, we aimed to maintain a short time baseline for each interferometric image pair, ideally set at 12 days. However, the time baseline for two pairs, 10 April 2021 to 4 May 2021 and 4 May 2021 to 28 May 2021, extended to 24 days. For data processing, we utilized the SARscape software (version 5.6.2) by SARmap, Caslano, Switzerland, which operates on the ENVI remote sensing image processing platform. The primary stages of D-InSAR data processing are illustrated in Figure 3. The methodology comprises the following procedural steps: image registration, interferogram generation, flat-Earth phase removal, adaptive filtering, coherence generation, phase unwrapping, refinement and re-flattening, phase to displacement conversion, geocoding, and conversion of Line-of-Sight (LOS) deformation to vertical deformation. To derive subsidence values for each monitoring point throughout the time series, we utilized the \"Extract Values to Points\" tool in ArcGIS, as shown in Figure 4. The total subsidence for each monitoring point was determined by aggregating the subsidence values across all time periods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**UAV** & **Lens** & \begin{tabular}{c} **Heading** \\ **Overlap Rate** \\ \end{tabular} & **Sidelap Rate** & \begin{tabular}{c} **Aerial** \\ **Height (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Flight** \\ **Speed (m/s)** \\ \end{tabular} &
\begin{tabular}{c} **Ground** \\ **Resolution (cm)** \\ \end{tabular} \\ \hline DJI Matrice300 & PSDK 102S & 80\% & 70\% & 159.8 & 13 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of UAV oblique photogrammetry.  
Figure 2: UAV oblique photography: (**a**) acquisition of UAV photos; (**b**) acquisition of image control points.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 2 System Overview
### Scenario
According to this, the received rate per user depends on the capacity delivered by its serving UAV, the number of users in the same region and their positions. Moreover, the scenario also includes the implementation of an out-of-band wireless backhaul to be shared among all the ABBSs using FDM. Thus, a backhaul link is created for each UAV, which has to support the service delivered to the subscribers attached to each ABBS. The sum of the bandwidths of the individual backhaul links is considered to be limited. In the following sections, it is explained how the proposed iterative algorithm calculates an appropriate distribution of the total backhaul bandwidth among ABBSs to optimize system performance given an initial generic distribution. Again, in the simulations, we consider that this bandwidth is equally distributed among ABBSs al the beginning, although this is not required. Figure 1 represents the scenario. In Figure 1, the red lines represent the backhaul links between the ABBSs and the backhaul ground station and \((x_{i},y_{i},h_{i})\) is the position of the \(i\)th UAV. As previously mentioned, these coordinates are obtained using the GPS units installed in each UAV and are sent to the central unit controlling the positions of the UAVs. \(B_{i}^{BH}\) is the bandwidth of the backhaul link between the \(i\)th ABBS and the ground station.
## 3 Results and Discussion
### Optimization Using RSM  
The Response Surface Methodology (RSM) optimization technique was implemented to determine the optimal combination of input variables, such as the spindle speed, feed rate, and drill diameter, which resulted in the lowest feasible process temperature. Figure 6 illustrates the optimization procedure that was designed to consider the model's predicted minimum process temperature and desirability constraints. The red color represents the optimization input, the blue color indicates the predicted outcome of the experiment, and the black dots correspond to the level of each parameter as shown in Figure 6. The results of the validation process, which encompass a thorough comparison with the experimental data, are comprehensively presented in Table 3. Notably, the model exhibits a remarkably low error rate of just 0.45%, underscoring its exceptional accuracy and reliability.  
Within the studied range of input values, the process temperature can be reduced to a minimum of 35.05 \({}^{\circ}\)C. The most favorable outcomes were achieved using a tool diameter of 2 mm, a feed rate of 40 mm/min, and a rotation speed of 1000 rpm. As shown in Figure 6, a comprehensive array of adjustable input parameters is available to ensure that the temperature applied to the bone remains safe, effectively minimizing the risk of damage to the bone tissue.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Spindle Speed (rpm)** & **Feed Rate (mm/min)** & **Drill Bit Diameter (mm)** & **Average T\({}_{\text{max}}\)** (\({}^{\circ}\)C)** \\ \hline
2000 & 20 & 3 & 49.9 \\
2000 & 40 & 4 & 62.5 \\
1500 & 30 & 4 & 41.7 \\
2000 & 20 & 2 & 44 \\
2000 & 20 & 4 & 53.6 \\
1500 & 20 & 2 & 37.5 \\
2000 & 40 & 3 & 63.8 \\
1500 & 20 & 3 & 43.4 \\
1500 & 40 & 4 & 48.6 \\
1500 & 40 & 2 & 37.5 \\
1000 & 40 & 2 & 35.5 \\
1500 & 20 & 4 & 42.9 \\
1500 & 30 & 3 & 46.5 \\ \hline
1000 & 20 & 3 & 35.7 \\
2000 & 30 & 2 & 49 \\
2000 & 30 & 4 & 55.6 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 5: (**a**) Illustration of bone drilling site and (**b**) schematic diagram of thermocouple location from the drilling site.
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 2 Related Works
### Methods Based on Semantic Information  
Recent advancements in deep learning have significantly enhanced the environmental adaptability of visual SLAM systems. Several studies [29; 30; 31] leverage object detection algorithms to identify and eliminate dynamic objects, thereby improving localization accuracy in dynamic environments. Ref. [32] introduces a convolutional neural network (CNN)-based approach for feature extraction, generating compact yet distinctive image representations for improved loop closure detection. More sophisticated solutions [33; 34] integrate instance segmentation networks into the SLAM system to eliminate potential moving objects in the scene, then use camera pose and epipolar constraints to filter out motion feature points, thus enhancing robustness in high-motion feature scenarios.