esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Target Recognition Based on Improved ORB Feature
## 2 ORB Algorithm Principle (Orient Fast and Rotated Brief)
### Calculate Feature Point Descriptors  
ORB uses the improved brief algorithm to calculate the descriptor of a feature point and solves the primary defect that brief itself has no rotation invariance. Its core idea is to select \(N\) point pairs in a specific pattern around feature points \(P\) and combine the comparison results of these \(N\) point pairs as descriptors.  
The brief descriptor is simple and fast, which is based on the idea that the image neighborhood can be expressed by a relatively small amount of intensity contrast.  
Define the criterion \(\tau\) of the \(S\times S\) image neighborhood \(P\):  
\[\tau(p;x,y)=\left\{\begin{array}{l}1,p(x)<p(y)\\ 0,\mathit{others}\end{array}\right. \tag{5}\]  
Figure 1: Schematic diagram of the extraction of fast feature points.  
Here, \(p(x)\) is the pixel intensity at the point of the \(\mathrm{x}=(u,v)^{T}\), which in the image is spot \(P\) after the filtering process. Choose n\({}_{d}\) and the \((x,y)\) position pair, which uniquely defines the binary criterion. The brief descriptor is a binary bit string of n\({}_{d}\) dimensions.  
\[f_{nd}(p):=\sum_{1\leq i\leq n}2^{i-1}\tau(p;x,y) \tag{6}\]  
The pixel value can be 128, 256, 512, etc. Select different values to weigh between speed, storage efficiency, and identification.  
The criterion of image neighborhood in the brief considers only a single pixel, so it is sensitive to noise. To solve this defect, each test point in ORB uses a 5 \(\times\) 5 sub-window in the neighborhood of 31 \(\times\) 31 pixels, in which the selection of the sub-window obeys Gaussian distribution and then uses the integral image to accelerate the calculation.  
The brief itself is undirected and has no rotation invariance. The solution of ORB is to try to add a direction to the brief. At position \((x_{i},y_{i})\), for any \(n\) binary criterion feature set, defining a \(2n\) matrix:  
\[S=\left(\begin{array}{c}x_{1},x_{2},\cdots,x_{n}\\ y_{1},y_{2},\cdots,y_{n}\end{array}\right) \tag{7}\]  
Using the neighborhood direction \(\theta\) and the corresponding rotation matrix \(R_{\theta}\), build a corrected version of \(S\), \(S_{\theta}:S_{\theta}=R_{\theta}S\). So the Steed Brief descriptor is  
\[g_{n}(p,\theta):=f_{nd}(p)|(x_{i},y_{i})\,\in S_{\theta} \tag{8}\]  
After getting Steered brief, use greedy search [29], finding 256-pixel block pairs with the lowest correlation from all possible pixel block pairs, that is, obtaining the final brief. Figure 2 is a schematic diagram of the descriptor calculation.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 6 Summary and Conclusions
\begin{tabular}{l l} A-RoF & Analog Radio-over-Fiber \\ ABNO & Application-Based Network Operations \\ A-CPI & A Controller Plane Interface \\ ADC & Analog-to-Digital Converter \\ API & Application Programming Interface \\ AWG & Arrayed Waveguide Grating \\ B-RAS & Broadband Remote Access Servers \\ BB & Base Band \\ BBF & BroadBand Forum \\ BBU & Base Band Unit \\ BER & Bit Error Rate \\ BGP & Border Gateway Protocol \\ BS & Base Station \\ BtB & Back to Back \\ BW & Bandwidth \\ C-RAN & Cloud Radio Access Networks \\ C-RoFN & Cloud-based Radio over optical Fiber Network \\ CAPEX & Capital Expenditure \\ CDR & Clock Data Recovery \\ CIR & Committed Information Rate \\ CO & Central Office \\ CoMP & Coordinated Multipoint \\ COP & Control Orchestration Protocol \\ CPE & Custom Premises Equipment \\ CPRI & Common Public Radio Interface \\ CS & Central Station \\ DBRu & Dynamic Bandwidth Report upstream \\ D-RoF & Digital Radio-over-Fiber \\ DAC & Digital-to-Analog Converter \\ \end{tabular} \begin{tabular}{l l} DAS & Distributed Antenna Systems \\ DBA & Dynamic Bandwidth Allocation \\ DC & Direct Current \\ D-CPI & D Controller Plane Interface \\ DL & Downlink \\ DMT & Discrete Multitone \\ DSB & Double Side Band \\ DSLAM & Digital Subscriber Line Access Multiplexer \\ DSL & Digital Subscriber Line \\ DSP & Digital Signal Processing \\ DSS & Distributed Synchronization Service \\ DU & Distributed Unit \\ RU & Remote Unit \\ DWDM & Dense Wave Division Multiplexing \\ E-CORD & Enterprise CORD \\ EDF & Erbium-Doped Fiber \\ EMBS & Elastic Mobile Broadband Service \\ EON & Elastic Optical Network \\ EPON & Ethernet Passive Optical Network \\ EPC & Evolved Packet Core \\ ETSI & European Telecommunications Standards Institute \\ EVM & Error Vector Magnitude \\ EMI & Electromagnetic Interference \\ FANS & Fixed Access Network Sharing \\ FBMC & Filterbank Multicarrier \\ FEC & Forward Error Correction \\ FEXT & Far-end Crosstalk Interference \\ FDD & Frequency Division Duplex \\ FFR & Fractional Frequency Reuse \\ FFT & Fast Fourier Transform \\ FPGA & Field-Programmable Gate Array \\ FSO & Free-Space-Optics \\ FSR & Free Spectral Range \\ FITC & Fiber to the Cabinet \\ FTTP & Fiber to the Cremises \\ FTTH & Fiber to the Home \\ FTTX & Fiber to the X \\ G.Fast & Fast Access to Subscriber Terminals \\ GFDM & Generalized Frequency Division Multiplexing \\ GMPLS & Generalized Multi-Protocol Label Switching \\ GPON & Gigabit Passive Optical Network \\ GPP & General Purpose Processor \\ GPRS & General Packet Radio Service \\ GTP & GPRS Tunneling Protocol \\ HA & Hardware Accelerator \\ HARQ & Hybrid-Automatic Repeat Request \\ I2 RS & Interface 2 Routing System \\ IA & Interference Alignment \\ I-CPI & I Controller Plane Interface \\ IETF & Internet Engineering Task Force \\ IF & Intermediate Frequency \\ InP & Infrastructure Provider \\ IoT & Internet of Things \\ IP & Internet Protocol \\ IQ & In-phase/Quadrature \\ \end{tabular}
## 7 Tests and Results
### Range Tests  
The article [3] has shown the reliability of the communication between two or more ZigBee modules under different environmental conditions is satisfactory. Nevertheless, following the procedure shown in [3], we performed some tests directly in the field where the lamp posts and the presence sensors are in line of sight (maximum distance about 45 m). Standard Xbee modules with patch antenna from Digi-MaxStream have been used because from datasheet [21] and from [3] they seemed to satisfy the range needs.  
According to [3], we perfomed several tests, each one foreseeing 10,000 transmissions both during clear weather and during rain. Using an appropriate adapter to simulate the retransmission and using the X-CTU software provided by Digi-MaxStream, we checked the transmissions verifying that the test packetd, sent through the network by an Xbee module, arrived at the coordinator lamp and were correctly returned back. Using the minimum transmission power available, we obtained the results reported in Table 4, with an average reliability of 100%.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Discussion
#### 4.4.1 Depth-Estimation Algorithm  
In this study, we presented a deep neural network architecture for monocular depth estimation. Our architecture contains four layers, with each layer as a residual block. Each residual block performed well in extracting depth information. On the decoder, we generated feature information with the help of a smart attention model. This gave us not only better qualitative results but also quantitative results. Certain test results from the NYU-depth v2 dataset are shown in Figure 12.  
Figure 11: Training curve for the NYU-depth v2 dataset.  
Figure 10: Training curve for the DIODE dataset.  
Figure 12: Test samples from the NYU-depth v2 dataset predicted in our model. The first column is RGB images, the second column is ground truth, the third column shows our prediction.  
Our proposed network has approximately 39 million parameters and is a large and sophisticated network. As a result of having such parameters compared with the baseline, the model requires extensive computational and memory resources. The potential for real-time performance is further constrained by the network's high degree of complexity. We trained our network on squared input images, but it is not limited to a fixed input size. If the GPU memory is sufficient, the network can accept any image with a size divisible by 16 and return the result.  
It could also be easily modified to accept any sized image, although this would require some interpolation steps. Our model can also easily be adapted to the task of semantic segmentation or other pixel-wise dense prediction tasks. This property, together with the non-fixed input size, make it simple to retrain the network for new tasks with different datasets and input sizes.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 1 Introduction
### Radio Noise Level Predictions  
When the ambient noise is indeed higher than the internal noise generated by the sensor, knowledge of the expected radio noise level is important for system design, as it factors in the link budget calculations. Additionally, when compatibility studies are performed for the assignment of frequency spectrum to new radio applications, the ambient noise level is often taken as a reference level. Unwanted emissions at levels below the expected radio noise level are then considered to be no nuisance to the incumbents. For these reasons, the Radio Sector of the International Telecommunication Union (ITU), the organization of the United Nations responsible for world-wide radio spectrum management, maintains Recommendation ITU-R P.372 [15]. This \"recommendation\"--it is actually more of a standard rather than a recommendation--contains information on the expected levels of radio noise generated by different natural and man-made sources. Of course, due to its wide spectral and geographic coverage (all frequencies, the entire world), the information in this Recommendation cannot be overly specific and precise. Still, it is considered the authoritative reference on radio noise world-wide. The current version of the Recommendation is version 17. In this article, it will be referred to as 'P.372'.
## 3 Materials and Methods
### Proposed Method  
The process of the proposed method for attack detection based on incremental online learning is outlined in the diagram below (Figure 1):  
The basic idea of the proposed approach is to use the data collected from the monitoring of the VANET nodes behavior in real-time and train an online model using incremental online learning algorithms. The incremental model performed is used for classifying  
Figure 1: The process of the proposed approach for attack detection using incremental online learning.  
VANET nodes as normal or attacker. The primary steps of the proposed method are as follows:  
* Initial model training  
An initial model is trained on initial data to build a base model before online incremental learning is performed. This model will serve as the starting point for the online incremental learning process.
* Incremental model training  
The model is incrementally updated after processing each new data instance provided in real-time. This process is repeated each time a new data instance is available, and the updating of the models occurs continually [6]. The model is updated or refined employing new data rather than being trained from scratch on the entire dataset. This ensures that the model is always up-to-date. The updated model is used for classifying VANET nodes as normal or malicious.
* Attack detection  
This component performs online classification of unlabeled data, leveraging the updated trained model to classify new data. Online classification makes it possible to detect attacks in real-time by identifying patterns that indicate malicious activity. If the pattern of the data is matched with an attack, an alarm is triggered.  
The proposed attack detection method involves data collection using a monitoring system designed to record various metrics and behavioral parameters within each vehicle node in the network. This includes recording the most relevant features to distinguish between normal nodes and black hole nodes in the network.  
The following subsections provide technical details on how the data are collected and preprocessed. This includes network simulation environment, scenario-based data collection, and the most relevant features for attack detection.
# An ICN-Based IPFS High-Availability Architecture
## 5 Simulation Results and Analysis
### Application Layer Performance Comparison  
#### 5.1.1 Setting of Experimental Environment  
In order to evaluate the performance enhancement of ICNs with field resolution systems on IPFS systems, we used a simulation scheme similar to the one mentioned in the protocol lab [26] and introduced IBIHA for comparison. This scheme focuses on the performance improvement of the enhanced nodes compared to the traditional IPFS nodesat the application level. Thus a fully connected network consisting of \(n\) nodes is constructed through NS3, where \(n\) is set to [10; 15; 20; 25; 30]. Since the network uses a fully connected topology with direct connections between the nodes, the enhanced nodes are only used as peer points in the IPFS network and no routing feature is used. One of the nodes acts as a service node for storing information, and the rest are request nodes. The bandwidth of all links is 100 Mbps, and the propagation delay is 100 ms. We simulate three different route resolution systems to evaluate the network data delivery capability, including: DHT, IPFS, and hybrid resolution architectures. Initially, we deploy DHT (Kademlia) as the underlying routing system in the network. Then we introduce Bitswap of IPFS as a supplement to the routing system, where we set the number of peer neighbors of each node to \(l=5\) and do not directly connect with peers that have data. Moreover, the TTL of retrieval signaling of Bitswap is 1. Finally, the on-site resolution system is introduced as a supplement to the system, and the number of enhanced nodes within the coverage of the resolution system is set according to Equation (16).
## 3 Results
### Characterization Methods
#### 3.3.4 FT-IR Analysis  
FT-IR spectra of RPNS and PNS500 samples is given in Figure 6. Spectrum of RPNS shows the presence of a broad characteristic band at 3327 cm\({}^{-1}\) for the stretching vibrations of the (-OH) group [43], a peak at 2909 cm\({}^{-1}\) which can be attributed to stretching vibration of aliphatic C-H and a band at 1732 cm\({}^{-1}\) which could be attributed to the presence of C=O stretching vibration in carboxylic acids. The presence of these peaks confirms the presence of carbonyl function group on the surface of RPNS. Furthermore, the absorption peak at 1590 cm\({}^{-1}\) can be associated with the C=C stretching in unsaturated ketones. The huge peak at 1027 cm\({}^{-1}\) is related to the presence of C-O-C, which is characteristic peak in hemicellulose and cellulose [30; 34]. This spectrum also confirms the presence of small peaks between 1450-1310 cm\({}^{-1}\) which could be related to bending vibrations of O-H and C-H groups.  
On the other hand, the spectrum of the thermally treated sample (PNS500) shows little shifting of the positions of some peaks compared to their analogs in the spectrum of RPNS. For example, the peaks at 1590 and 1732 cm\({}^{-1}\) that were attributed to the C=C and C=O groups in RPNS, appear at 1579 and 1740 cm\({}^{-1}\) in PNS500. In addition, the spectrum shows the disappearance of some peaks such as the one at 3327 cm\({}^{-1}\). Such a behavior might have been caused by the de-hydroxylation that occurs during  
Figure 5: Raman analysis of raw pistachio nutshells and thermally activated samples at different temperatures range from 250–500 \({}^{\circ}\)C (PNS250–PNS500).  
the thermal treatment process. Depending on the TGA/_d_TGA thermograph and FT-IR spectrum of RPNS, the nutshell of pistachio is formed mainly of hemicellulose, cellulose, and lignin. Therefore, it can be concluded that the thermal activation leads to the removal of hydroxyl, carboxylic, carbonyl, and esters groups in the form of water, CO, CO2, and small volatile organic compounds [42].
## 2 Materials and Methods
### Data Preprocessing  
Data were pre-processed using Data Processing Assistant for Resting-State fMRI (DPARSF, V5.0, [[http://rfmri.org](http://rfmri.org)]([http://rfmri.org](http://rfmri.org)) [60], accessed on 23 November 2021, Statistical Parametric Mapping (SPM V12) [61], and MATLAB V2018b [62] Toolboxes.  
The steps for data preprocessing were as follows: one subject was discarded because of having too-few time points in image data (67 instead of 150). For the rest of subjects, we removed the first five volumes, leaving 145 volumes; corrected slice-timing and realigned images; manually reoriented the T1 and functional images; co-registered T1 images into functional images then segmented images to gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF); nuisance covariates were regressed (including Friston 24 head motion parameters [63] and white matter and cerebrospinal fluid signals), functional images were then normalized into MNI space DARTEL Method [64] and resliced to 3.0 \(\times\) 3.0 \(\times\) 3.0 mm\({}^{3}\); spatial smoothing of normalized images was performed at 6 mm FWHM; band-pass filtering images at 0.01-0.08 Hz as it was found that resting-state signals are consistent low-frequency fluctuations in this range [65]. To address head motion in fMRI, image volumes with mean framewise displacement FD (Jenkinson) \(>\) 0.5 mm were scrubbed to reduce its effect using cubic spline interpolation [66; 67]. Subjects were not excluded as they did not exceed the head transition \(<\) 3 mm, rotation \(<\) 3\({}^{\circ}\)[68].
## 4 Results and Discussion
### Influence of Beam Current
(**c**) The number of coincidence events as a function of incoming current scales only linearly with incoming current. The corrected signal taking the efficiency into account is shown (green). Please note that the corrected fit better approximates a linear function. (**d**) The measured electron efficiency with a notable decrease for higher currents due to dead time in the detector. (**e**) The SNR of the coincidence signal as a function of incoming current when using a constant acquisition time. (**f**) The signal-to-noise (SNR) of the coincidence signal as a function of incoming current when the total dose has been fixed. such that the product of time and current is constant. The resulting \(SNR_{\mathcal{C},N}\) is shown in Figure 2f where, as expected, a decrease of SNR is observed with increasing current implying that when having a fixed dose, the lower the current, the higher the SNR at the expense of acquisition time. Please note that this is in strong contrast with conventional EELS or EDX where the SNR depends only on the total dose.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 4 Experiments
### Comparison Algorithms  
The algorithm proposed in this article is compared numerically with the following four band selection algorithms. After implementing band selection for front-end learning, we use the same classifier for classification and compare the performance of five band selection algorithms. The information for all of these methods is summarized as follows.  
ASPS_MN [34]: The core idea is to divide the HSI cube into multiple sub-cubes via an adaptive subspace partition strategy and then select the band with minimum noise in each  
Figure 4: Salinas: (a) pseudo-color image; (b) ground-truth image; (c) class names.  
Figure 5: Indian Pines: (a) pseudo-color image; (b) ground-truth image; (c) class names.  
Figure 3: TeaFarm: (a) pseudo-color image; (b) ground-truth image; (c) class names.  
sub-cube as the representative band to reduce the data dimension while retaining more useful information.  
SEASP [35]: By calculating the correlation coefficient between adjacent bands to identify the orderliness between bands, a subset of characteristic bands is selected from the partitioned subspace using information entropy as the metric.  
S\({}^{4}\)P [36]: The spatial structure of HSI is captured through superpixel segmentation, and the spectral correlation between bands is learned using a self-representation model. An adaptive and weighted multi-graph fusion term is designed to generate a unified similarity graph between different superpixels, and the importance of the bands is measured by imposing an \(l_{2,1}\) norm on the self-representation coefficient matrix.  
RLFFC [37]: Spatial information is obtained through superpixel segmentation, and the separability among bands is enhanced by a latent feature fusion strategy, thereby improving the performance of band selection.  
GRSC [22]: The spatial information of HSI is preserved through superpixel segmentation, and representative bands are selected by combining spectral correlation and spatial structure information using a self-representation subspace clustering model, thereby reducing data redundancy and noisy bands.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Encryption Performance
#### 4.1.2 Gray Histogram Analysis  
The gray histogram describes the number of pixels of different brightness levels in an image, which is an important feature in statistical analysis. An ideal encrypted image histogram should be uniform and flat. Figure 8 shows the gray histograms of the original and encrypted images. The encrypted image's histogram appears to be very different from the original image's histogram, with a quite uniform distribution. Therefore, the encryption scheme can resist statistical analysis.  
Figure 8: _Cont_.
## 3 Monitoring System and Optimized Building Operation
### Prediction Approach
#### 3.1.1 Building Demand and Production  
All systems and the thermal behavior of the building are dynamically modeled in the simulation environment INSEL [35]. The following Figure 12 gives an overview over all the components, their interaction, and the data flow. All the individual parts are described further below. A more detailed description and assessment of the model is also given in [36].  
Dynamic Building Model  
The utilized model provides a high level of detail while allowing for simplification at the data model level. It is based on the nodal method. This method is common for building simulations, especially when simulating multiple zone buildings. This approach also enables a good balance between the level of detail in the results and in the calculation time. The model contains a one-dimensional numerical solution of the heat conduction equation for each wall. Additional nodes contain room air and windows. Long-wave radiative exchange between the room surfaces is also considered.  
Heat Pump  
The applied model simulates the compressor's behavior based on a polynomial function (according to norm DIN EN 12900). The heat exchangers on the evaporator and condenser side are resembled with the NTU method (number of transferred units). This simplifies the calculation process, because no complex calculations due to complicated streamlined shapes have to be carried out.  
Thermal Buffer Storages  
Within the thermal buffer storage model, each layer is modeled physically. Heat conductivity between the layers is calculated. Thermal losses are added to the suitable building zone.  
The modules are represented by a two-diode model that is suitable for mono- and polycrystalline Si (silicon) as well as for CIS (copper indium gallium selenide) modules. The parameters are created via a parameter fit according to module characteristic curves. The inverter is also represented through parameter fit by characteristic curves provided by the manufacturer. The PV modules are linked to a MPP (Maximum Power Point Tracking) loop.  
The battery storage model is kept simple. It does not simulate the electrochemical process of the battery. The charging and discharging efficiency, power, inverter efficiency, and self-discharge are considered. The aging effect is considered dependent on number of cycles (linear aging coefficient).