esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Basic Principles of 3 MA
### Harmonic Analysis (HA) Method  
A low frequency (f\({}_{\rm LF}\) = 10-1000 Hz) sinusoidal voltage excitation is supplied into a magnetization coil (see Figure 2, no. 4), wounded around a magnetic yoke (no. 3), which is placed on or in vicinity to the specimen. This generates a variable magnetization in the specimen.  
This magnetization can be chosen in the amplitude range between 10 and 70 A/cm. The amplitude is controlled via a Hall sensor (no. 6), which is placed in the middle of the detection zone, between the pole shoes of the magnetic yoke (no. 3). This Hall sensor is also used to record the entire tangential magnetic field development during the hysteresis cycle. A Fourier analysis processes this recorded signal. Due to the hysteresis symmetry, only odd higher harmonics can be determined. The amplitudes and phase shifts of these upper harmonics are observed. Further measuring parameters can be determined such as UHS (upper harmonics sum) and the signal distortion factor K, which are described in Table 1. In total, the HA method provides 11 measuring parameters. Table 1 shows all of the detected parameters. A\({}_{1}\), A\({}_{9}\), and P\({}_{9}\) are not used as own parameters. The HA method permits the analysis of deeper material ranges and it can even be applied to shallower structural and stress (tension) gradients, as occurring in surface hardened parts, for example. For a specific excitation (yoke + magnetization coil) system, the electromagnetic interaction depth is only dependent on the magnetic field strength (H\({}_{\rm I}\)) and the excitation frequency (f\({}_{\rm LF}\)) and it can reach depths of up to 10 mm. On the other side of the coin, the HA method only has a limited sensitivity to the near-surface properties of the material, when compared to the BN (see Section 3.3) or IP (see Section 3.4) method.
# Image Semantic Segmentation Fusion of Edge Detection and AFF Attention Mechanism
## 2 Method  
### Hybrid Model of Improved PSP-Net Network Combined with Improved HED  
In order to accurately detect and segment image edges, improved HED is integrated into the improved PSP-Net network. The network structure can obtain shallow edge information and deep semantic information; its structure is shown in Figure 1.  
The network consists of three major parts: a semantic segmentation network, an edge detection network, and a semantic and edge feature fusion module.  
1. Semantic segmentation module: composed of the improved PSP-Net model, the module integrates multi-scale and multi-level features to extract image semantic information.
2. Edge detection network: composed of the improved HED model, this part detects and learns edge features of images through feature extraction and fusion, so more detailed edge information can be obtained.
3. Semantic and edge feature fusion module: the edge information is mapped to semantic features to realize the fusion of semantic information and edge information.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 2 Related Work  
Considerable research has been conducted on adapting data sending rates in IEEE 802.11 wireless networks. In this section, we briefly introduce some of the results and other related research.  
In [15], the authors propose adapting data sending rates based on request-to-send/clear-to-send (RTS/CTS) messages to differentiate frame collisions from frame transmission failures. In [16], the authors suggest making receivers estimate the channel status and return the results by embedding them in ACK frames. In [12], the authors use probing packets to estimate the channel status. In [17], the authors propose using bit error rates to control data sending rates. In [18], the authors suggest using the error vector magnitude (EVM) of orthogonal frequency division multiplexing (OFDM) symbols to control data sending rates. In [19], the authors use group request-to-send/group clear-to-send (G-RTS/G-CTS) messages to improve overall network throughput in wireless mesh networks. The proposed algorithm in this paper is different from these approaches mainly because senders adapt their sending rates using ACK frames without requiring any modifications to the existing CSMA/CA mechanism.  
Recently, researchers have been using theories from stochastic geometry to analyze various kinds of issues related to wireless networks, such as SINR distributions and outage (or coverage) probabilities for a given target SINR [20] in cellular networks and the transmission capacity [21] for wireless networks that use simple medium access control (MAC) protocols such as slotted ALOHA [22]. Compared to cellular networks and wireless networks of simple MAC protocols, analyzing IEEE 802.11 wireless networks seems more challenging owing to the dynamic nature of the CSMA/CA mechanism adopted in IEEE 802.11 standards. In [23], the authors analyze CSMA/CA wireless networks with stochastic geometry and show coverage probabilities with different PPP densities of nodes. In [24], the authors analyze interference in CSMA/CA wireless networks. In [25], the authors show distributions of throughputs with different PPP densities of nodes.  
The proposed algorithm in this paper heavily depends on the accuracy in the measurement of the SINRs. For accurate measurement of the SINRs of the ACK frames, we use the IEEE 802.11 Ext module from [26] in ns-2 simulations. Within the module, nodes not only use signal strengths and arrival times of packets but also use the sizes and transmission speeds of the arrived packets (i.e., durations) to calculate cumulative power at the nodes. The cumulative power is used to calculate the SINRs of packets when and while they are received. Figure 1 illustrates the changes of the cumulative power (described as cumulative noise in the figure) when packets of different received signal strengths and durations arrive at a node [27].
###### Abstract  
Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification have been challenges. In this paper, an integrated approach to geospatial and infrared image processing was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by the Sharjah Electricity, Water, and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used to pinpoint the leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.  
w +
Footnote †: journal: Applied sciences  
0000-0002-2300-3300][PERSON]  
0000-0002-1331-10416
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 5 Simulation Setup and Experimental Results
### Data Recovery  
Although data loss is very critical issue, very little information is publically released even when substantial data is lost. A wide variety of failures can cause physical mutilation to the quality of service of the applications. To retain the lost data, the backup recovery approaches perform vital role. However, data recovery methods are not capable enough particularly in wireless sensor and actor networks. In our proposed approach, we have a node monitoring algorithm that monitor the status of the node prior to failure as well as post-failure.  
As a result, backup nodes take the responsibility of storing the data. Figures 13 and 14 show data loss and recovered data with 1400 \(\times\) 1400 m\({}^{2}\) network topology using 72 connections. In Figure 13, the total data loss is 15 KB when monitoring 10 events. Based on the results, we observed that EAR lost 15 KB data and recovered 15 KB that shows our scheme of data recovery is fault-tolerant, whereas other approaches also lost the same amount of data, but recovered 11.1-13.5 KB data. It is confirmed that EAR has 10%-26% improvement over other competing approaches.  
Figure 13: Data loss vs. Data recovery during 10 events.  
In Figure 14, data loss is 30 KB with 20 events. As some of the events are not highly critical so that less amount of data is lost with 20 events. As EAR recovers 29.82 KB out of 30 KB that is quite better recovery as compared with other competing approaches. The other competing approaches are greatly affected due to the increase in events so that other approaches have recovery data from 23.8 to 29.1 KB. The least adaptable data recovery algorithm is DPCRA with 20 events. The results also validates that EAR has 2.41%-20.66% improvement over other competing approaches.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 2 Related Works  
### A Brief on Edge Detectors and LBP Code  
An edge is a boundary between two distinct sections of an image, or it can be described as a group of contiguous pixel positions or abrupt changes in intensity values. Edges may exist vertically, horizontally, or diagonally. Edge detection is the process of segmenting an image into areas of discontinuity. The edge detectors Canny, Sobel, Log, Prewitt, Kirsch,Laplacian, and Fuzzy are very widely utilized. In general, edge detectors are employed in digital image processing, pattern recognition, image morphology, feature extraction, etc.  
A \(3\times 3\) kernel's center pixel and its eight surrounding pixels are compared to produce the local binary pattern or LBP. The visual properties of the image are represented by this 8-bit pattern, which adapts to non-uniform environmental changes. Consider a \(3\times 3\) pattern's block where \(K_{c}\) and \(K_{p}^{i}\) denote the center and other pixels of a block, respectively, and \(1\leq i\leq 8\). At the same time, we also take a sample image block of \(3\times 3\) pixels. The LBP method classifies the block contents into two regions-(i) pixel values greater than \(k_{c}\), which are represented by 1, and (ii) the other pixels in the pattern, which are represented by 0. Figure 1 shows a step-by-step approach to generating LBP code values.  
In order to keep the structural and statistical characteristics of an image while reducing the quantity of data in it, edge detection and LBP are used. Many publications on that topic have been studied by our team. Among those, we found the works of [PERSON] [24], [PERSON] [25], [PERSON] [44], and [PERSON] [27] and read them very carefully and attentively and built the foundation of our proposed work upon their findings.
## 4 NMR Relaxation Results Obtained Using the Spectrometer and Peltier Cooled Probe
### Bulk Ice T\({}_{2}\) Measurement  
Using the Peltier cooled variable-temperature probe, the Free Induction Decays (FIDs) for bulk brittle ice were measured, from below \(-20\) C to \(-2\) C. As expected, a Gaussian curve was the preferred fit for all the data sets; the automated fitting routine with initial linearised fit, followed by a full nonlinear fit, was used to extract the amplitudes and relaxation times as before.  
Four example measurements at \(-20\) C, \(-15\) C, \(-10\) C and \(-5\) C are shown in Figure 1. These have all been Boltzmann corrected, to take into account the amplitude changes as a function of temperature [51].  
It requires a fairly fast recovery NMR spectrometer to measure these FIDs in brittle ice. This linear NMR transmitter does not have the power of the Lab-Tools digital NMR transmitter (still at the proto-type stage), but can still generate an RF B\({}_{1}\) of about 1 mT in a 5 mm OD NMR sample tube (just under a 6 \(\upmu\)s \(\pi\)/2 NMR pulse). These brittle ice FIDs were measured using the variable-temperature (NMR Cryoporometric) probe, with a 3 mm OD NMR sample. Due to the constraints of a probe optimised for Cryoporometry, this generates a lower RF B\({}_{1}\) of about 0.7 mT (about a 9 \(\upmu\)s \(\pi\)/2 NMR pulse).  
Measuring the Ice FIDs, from the middle of the NMR pulse (necessary to correctly fit a Gaussian), the first 4.5 \(\upmu\)s are occupied by the NMR transmit pulse, and about the next 3 \(\upmu\)s are the probe ring-down and receiver recovery. Thus, with the decimation set to full band-width, and the integral count set to 2, so as to reduce ringing from the decimation filter, then the recovered signal is fully usable at 7.5 \(\upmu\)s from the centre of the pulse. This recovery performance is achieved because the probe is designed deliberately to be broadband, with about a 1 MHz bandwidth. This also has the advantage of making the tuning of the probe insensitive to sample changes, such as the freezing or melting of a liquid sample, or very wide probe temperature changes.  
The measured Gaussian T\({}_{2}\)s were plotted as a function of temperature, and a polynomial curve fitted to them, Figure 2.  
Figure 7: The longer fitted exponential component has been subtracted from the measured data, and the resultant (red line, blue dots) plotted logarithmically and fitted with a straight line (green), to confirm the presence of a double exponential decay.
## 5 Conclusions  
In this paper, the equivalent circuit model of the stator winding and the connecting cable of the permanent magnet machine are built, respectively. By changing the input PWM pulse frequency and the length of the end connecting cable, the distribution law of the ground voltage and the inter-turn voltage distribution at the end of the stator winding coil is studied. The conclusions are as follows:  
1. The pulse frequency is small, for example, between 10 kHz and 50 kHz, the second pulse wave's rising edge before the arrival of the first pulse wave's falling edge has been completely attenuated, and the change in frequency does not affect the distribution of ground voltage and inter-turn voltage; the maximum ground voltage is located at the end of the stator winding coil, and the maximum inter-turn voltage is concentrated between the 2-3 conductors in the first end coil 1; when the frequency is large, such as 100 kHz, the second pulse wave rising edge overvoltage will be affected by the first pulse wave falling edge overvoltage and thus higher, the highest position of 3.7 kV, 1.32 times the input voltage. The results of this test show that there is a critical frequency, which in the motor used in this paper lies between 50 kHz and  
Figure 14: Distribution characteristics of winding inter-turn voltage under different cable lengths.  
Figure 13: The maximum inter-turn voltage of winding under different cable lengths.  
100 kHz. The degree of influence of frequency on voltage is small and the maximum magnitude is only 1.32 times.
2. There is a critical value corresponding to the length of the cable. Before the critical value, the length of the cable increases, and the voltage at the end of the stator winding will increase. After the critical value, it tends to be stable, and the ground voltage can reach up to 5.4 kV. As the cable length increases, the voltage distribution of the entire winding begins to become uniform, and the maximum ground voltage is concentrated in the middle and rear of the coil. The maximum inter-turn voltage decreases first, then increases, and finally decreases with the increase in cable length, which is concentrated between the 1-2 and 2-3 conductors of the head coil, up to \(185\,\mathrm{V}\). The cable length has a large effect on the overvoltage, and the maximum voltage can be close to twice the input voltage at constant frequency.  
Conceptualization, S.Z.; Methodology, D.C.; Software, S.L.; Validation, H.L.; Formal analysis, F.T.; Writing--original draft, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Beijing Goldwind Science and Technology Innovation Wind Power Equipment Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Proposed Methodology
### System Infrastructure
#### 3.1.2 Energy Model  
The system's architecture comprises many sensor nodes connected to a single base station (BS). The \"common node\" is one type of node, whereas the \"cluster head node\" is the another. The CH node receives sensor data from the common nodes, monitoring the surroundings. The cluster head node carefully selects a common node from among them and gathers, combines, and transmits data to the BS.  
\[E_{Tx}(L,d)=(E_{dec}\times L)+\left(\varepsilon_{amp}\times L\right) \tag{1}\]  
\[E_{Rx}(L,d)=E_{dec}\times L \tag{2}\]  
where \(d\) represents the distance between the transmission and receiving nodes. In Equation (1) \(E_{Tx}(L,d)\) denotes the energy consumed through the transmission of an L-bit packet; and in Equation (2), \(E_{Rx}(L,d)\) is the energy consumed through the reception. \(E_{dec}\) denotes the energy consumed per bit in the transmitter and receiver sensor nodes. \(\varepsilon_{amp}\) represents the energy the amplifier consumes during the transmission phase, which may be computed using Equation (3).  
\[\varepsilon_{amp}=\left\{\begin{array}{ll}\varepsilon_{fs}\times d^{2},&\text{ when }d\leq d_{0}\\ \varepsilon_{amp}\times d^{4},&\text{when }d>d_{0}\end{array}\right. \tag{3}\]  
where \(d_{0}\) is a threshold value associated with the sensor node's transmission model. The sensor node will use the free-space propagation model if its distance, \(d\), is less than or equal to \(d_{0}\). The multipath fading channel model is employed. \(\varepsilon_{fs}\) and \(\varepsilon_{amp}\) are parameters for communication energy. Equation (4) is used to determine \(d_{0}\).  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{4}\]
###### Abstract  
Increasingly more governmental organizations in the U.S. have started to implement artificial intelligence to enhance the asset management process with an objective of controlling the costs of data collection. To help the Wyoming Department of Transportation (WYDOT) to automate the data collections process, related to various assets in the state, an automated assets management data collection was proposed. As an example, the automated traffic barriers asset dataset would collect geometric characteristics, and barriers' materials' conditions, e.g., being rusty or not. The information would be stored and accessed for asset-management-decision-making and optimization process to fulfill various objectives such as traffic safety improvement, or assets' enhancement. For instance, the State of Wyoming has more than a million feet of roadside barriers, worth more than 100 million dollars. One-time collection of various characteristics of those barriers has cost the state more than half a million dollars. Thus, this study, as a first step for comprehensive data collection, proposed a novel approach in identification of roadside barrier types. Pre-trained inception v3, denseNet 121, and VGG 19 were implemented in this study. Transfer learning was used as there were only 250 images for training of the dataset for each category. For that method, the topmost layers were removed, along with adding two more new layers while freezing the remaining layers. This study achieved an accuracy of 97% by the VGG 19 network, training only the few last layers of the model along with adding two dense layers for top layers. The results indicated that although there are not enough observations related to traffic barrier images, a transfer learning application could be considered in data collection. A simple architecture non-transfer model was also implemented. This model achieved an accuracy of 85%, being better that the two other transfer learning techniques. It should be reiterated that although non-transfer learning technique outperformed inception and denseNet networks, it comes short significantly when it come to the VGG network.  
data collection; transfer learning; image recognition; convolutional neural network; traffic barriers +
Footnote †: journal: Physics Letters  
1
Footnote 1: [[https://doi.org/10.3390/signals2010007](https://doi.org/10.3390/signals2010007)]([https://doi.org/10.3390/signals2010007](https://doi.org/10.3390/signals2010007))
###### Abstract  
It is well known that simulation tools are essential for the design and optimization of wireless communication systems. This paper proposes a Python script that can be used for planning and predicting a connection link budget by analyzing its basic parameters. Our proposal consists of an application that calculates the connection budget for point-to-point links operating at 5 GHz and 11 GHz, taking into account all the necessary microwave parameters. For validating the efficiency of the proposed tool, this paper presents comprehensive simulation results derived from comparing our tool to a couple of other simulation tools by means of calculating the same parameters.  
link budget; point to point; Python GUI; simulation tools +
Footnote †: journal: Network  
0000-0001-6000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-0000-0000-00000-0000-00000-0000-00000-00  
of a local network monitoring tool using the Python language. This application presents a simplified calculation of a link budget for two point-to-point microwave wireless Ethernet bridge connection scenarios, which are part of a local LAN network, enabling users to extract information regarding the calculation of specific parameters. Subsequently, the results are compared to five real simulation programs for the scenarios used with the same parameters. The computed results significantly match those of Airlink, the program developed by the company that manufactures the equipment used for the same scenarios. The main points of this survey's contribution are as follows:  
* To propel digital transformation by emphasizing the indispensability of programming expertise in conjunction with wireless communication fundamentals.
* To promote technical flexibility and use Python GUI framework development to create an application recommended for problem solving in telecommunications that would provide multifaceted solutions.  
The survey is conducted as follows. Section 2 describes some of the graphical user interface tools developed for radio communications. Section 3 describes the theoretical background to calculating the link budget for radio transmission. Section 4 describes the proposed simulation tool. Finally, the results and conclusions of the proposed tools are presented in Sections 5 and 6.