esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The development of a photonic device based on a non-diffracting surface plasmon polariton (SPP) beam can effectively improve the anti-interference ability. Furthermore, an easily adjustable on-chip routing device is highly desirable and extremely important in practical optical communication applications. However, no non-diffracting SPP-beam-based spin routing devices with high tunability in multiple degrees of freedom have been reported. In this study, we theoretically designed a simple micro-nano structure to realize a highly adjustable non-diffracting SPP-beam-based spin router using Finite-Difference Time-Domain (FDTD) simulation. The simulation results show that the structure enables spin-controlled nondiffracting SPP-beam directional launching. The launching direction of the nondiffracting SPP beam can be dynamically rotated counterclockwise or clockwise by changing the incident angle. Hence, the routing SPP beam can be coupled to different output waveguides to provide dynamic tunability. Moreover, this device shows good broadband response ability. This work may motivate the design and fabrication of future practical photon routing devices.  
surface plasmon polaritons; photon spin router; nondiffracting beam Article Simulation of On-Chip Broadband Photon Spin Router Base on Nondiffracting Surface Plasmon Beam Launching. _Appl. Sci._**2021**, _11_, 10643. [[https://doi.org/10.3390/appl12210643](https://doi.org/10.3390/appl12210643)]([https://doi.org/10.3390/appl12210643](https://doi.org/10.3390/appl12210643))
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 4 Principle of NOMA System
NOMA power-domain utilises the users' power-difference to identify various users concerning successive interference cancellation (SIC) at the receiver side. The NOMA Code-domain is comparable to CDMA or multi-carrier CDMA (MC-CDMA) unless its decision to employ the low-density sequences or non-orthogonal sequences has a low cross-correlation [50].
## Appendix A Sample Design and Preparation
### Experimental Setup  
The setup for torsion test shown in Figure 17 consists of an electromechanical tension-torsion test system and a stereovision imaging system. The stereovision system used to obtain images of each specimen during mechanical loading consists of a pair of stereo digital cameras, high-intensity, low-heat, white LED light sources, and a calibration grid. A rigid crossbar is used to mount the cameras, and a tripod is employed to mount the rigid bar. A suitable random pattern was adhered to the surface of the LDT samples by initially coating surface with a thin coat of flat white paint and lightly over-spraying with flat black paint to obtain high contrast speckles with an average size of 0.250 mm. Adedicated computer with StereoDIC image processing software VIC-Snap and VIC-3D [63] was used to acquire synchronized images and perform image analysis to measure full-field strains. Calibration of the stereovision system was performed using standard patterns ([PERSON] et al. [64], [PERSON] et al. [65], [PERSON] et al. [66], Correlated Solutions, Inc., Irmo, SC, USA [63], with the authors adopting a modified methodology using a large angle calibration procedure described previously ([PERSON] et al. [67], [PERSON] et al. [68; 69; 70]). The mechanical loading system has tension/torsion capacities of 22 kN/300 N-m and is configured so that each test specimen can be subjected to uniaxial force, torsional moment or a combination of axial force and torsional moment in a proportional or non-proportional manner using either load control or displacement control.  
The test system for the miniature RDD specimens included a stereomicroscope for high magnification image and a Psylotech micro-tensile test system (Psylotech Incorporated [71]) to apply tensile loading. Figure 14 presents the experimental setup. The Psylotech micro-tensile test frame used in these studies is a flexible, computer-controlled system capable of communicating with the stereo-microscope image acquisition system so that StereoDIC can be performed on images that are synchronized with the applied load. The RDD specimens also were patterned by applying a similar light coat of white paint, followed by a micro-scale black pattern applied using an airbrush system. Average speckle size on the RDD specimens is 0.013 mm. A specialized back-lit calibration pattern methodology is used in to calibrate the stereo-microscope imaging system. Both the RDD imaging system components (e.g., cameras, lenses, lighting) and the VIC-3D image analysis parameters (e.g., subset size, subset spacing) are summarized in Table 15.  
The LDD specimens are uniaxially loaded in a table-top MTS system without stereo imaging for strain measurement. Instead, an MTS extensometer is attached to the gage section for axial strain measurements.
###### Abstract  
Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency.  
rolling contact fatigue (RCF); multi-layer defect; eddy current pulsed thermal-tomography (ECPTT); sweep excitation frequency; defect characterization and quantification +
Footnote †: journal: Applied Sciences
# Deep Learning-Based Efficient Analysis for Encrypted Traffic
## 3 System Framework of Encrypted Traffic Identification
### Preprocessing Methods for Encrypted Traffic Datasets  
This paper uses the ISCXVPN2016 dataset [22] published by researchers at the University of New Brunswick, which contains a set of PCAP (packet capture) files, including six different types of application traffic and their corresponding OpenVPN. In total, there are 12 types of encrypted traffic.  
The traffic types and specific network applications or protocols included in this dataset are listed in Table 1[18]. Protocols such as SMTPS use SSL/TLS protocols at the transport layer for encryption. Applications such as BitTorrent and Skype use customized encryption methods at the application layer. In addition, there are six traffic types that use OpenVPN to encrypt the aforementioned application traffic at the network layer, which are implemented based on the SSL/TLS components provided by the OpenSSL library.  
The content of the PCAP file provided by the dataset is a file organized according to the standards specified by the application programming interface (API) of the same name. In the Windows system, it corresponds to the WinPcap and Npcap libraries, which are used to monitor and capture the traffic in the network in real time. Each data packet contains the header fields and payloads of layers 2-7 in the OSI model. The exported PCAP file structure is shown in Figure 2.  
Figure 1: The framework of an encrypted traffic identification system based on CNN.  
In PCAP files, the initial 24 bytes represent the global header, which provides essential details about the file version, timestamp, and the network's data link. Following the global header, there are multiple packet records. The packet header contains metadata pertaining to each packet, such as the capture time and packet length. Packet data refer to the actual packet content.
## 3 Results and Discussion
### Molten Pool Quality Monitoring  
During arc welding, the weld quality is affected by various factors, including the external environment. Similar to manual welding, in which a welder wears a welding helmet and performs welding while observing the molten pool, it is important to observe the various characteristics of the molten pool using a vision system for robotic welding. In vision monitoring, molten pool images can be used to determine the weld quality in real time. Images of the molten pool were captured during mild-steel GMAW using the proposed vision system. Figure 12 shows the images for each quality state of the weld under various welding conditions. Figure 12a shows a humping bead that occurs when the welding speed is too high or when the flow of the molten pool is unstable. Figure 12b shows a magnetic arc blow in which the electromagnetic force accumulates, and the weld bead is tilted. Figure 12c depicts silicate island separation that causes defects in the appearance of the weld bead owing to the separation of silicate at the end of the molten pool. Figure 12d shows burn-through owing to an excessive welding current setting or excessive gap in the weld. Figure 12e,f shows the spatter and undercut caused by inappropriate welding conditions, such as current, voltage, and speed settings, respectively.  
Figure 11: Molten pool images of mild-steel GMAW obtained for 1 s; frame rate: 30 frames per second.  
Figure 10: Information configuration of image obtained by combining 830 nm band-pass filter and OD1.0 ND filter during mild-steel GMAW.
## 5 Experiment
### Ablation Study
#### 5.3.3 Semantic Transformation  
Semantic transformation is utilized during the input of the first stage (semantic forwarding) and the output of the second stage (semantic backwarding). We conducted another experiment without semantic transformation to observe the effectiveness of this module. The experimental results are shown in Table 5. It is evident that there is a significant drop in performance (1.9 QM score), highlighting the importance of this module.
## 3 Results
### Reliability of Temperature Measurements  
Using a combination of K-type thermocouples, which were (1) harpooned in the melt pool and (2) inserted from the backside through 2 mm drilled holes from previously deposited passes and spot welded on the surface of the joint, was successful, and more than 90% of thermocouples survived. When comparing the measurements obtained by the harpooned and the inserted thermocouples, very low dispersion (from 0.1 to 2.2 s) was found in the time at which the weld metal was exposed to the different temperature ranges of interest. As an example, the temperature vs. time registered during the welding of Weld Pass 7 in the GR weld is shown in Figure 5. Two thermocouples (tc 13 and tc 14) were inserted through drilled holes, whilst two other thermocouples (tc 15 and tc 16) were harpooned. The time at which Weld Pass 7 was exposed to the critical temperature ranges is also shown in Figure 5.  
To prove the accuracy of the temperatures measured by the thermocouples, the FR weld was placed in a furnace with the 20 thermocouples used to monitor the temperature in the weld passes connected (see Figure 6). All the thermocouples yielded a signal and reacted to the heat pulses in the furnace. Most importantly, the measurements obtained in the furnace proved to be accurate, with a range of dispersion of only 2 \({}^{\circ}\)C.  
Figure 5: Temperature vs. time plots in Weld Pass 7 in the GR weld registered by using two thermocouples harpooned in the weld metal and two thermocouples inserted into drilled holes. The table embedded in the plot shows the time at which the deposited weld metal was exposed at the three ranges of temperatures studied.  
Figure 6: Temperature vs. time plot given by the thermocouples used in the FR weld inserted in a furnace. All the thermocouples yielded signal and reacted to the heat pulses in the furnace. Most importantly, the measurements proved to be accurate, within a range of dispersion of 2 \({}^{\circ}\)C.
###### Abstract
In particular, the past review articles investigated change detection techniques using different sources of EO data from a descriptive perspective and thus, do not offer a quantitative comparison between Landsat-specific algorithms and applications. As such, the main objective of this study is to fill this gap via an analytical review of remote sensing change detection with a focus on data from the Landsat program. Specifically, this paper provides an overview of Landsat change detection research and a quantitative assessment of common processing pipeline for change detection algorithms using Landsat data. To capture a broad perspective on Landsat change detection and to identify scientific gaps, 490 published papers were selected, reviewed, and compiled into a database for this meta-analysis and review.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Network Layer  
Network communications follow a centralized scheme, where the GW adopts the main role and assumes the responsibility of managing network associations, delivering network addresses, and periodically notifying the start of new routing processes.  
STAs adopt a subordinate role waiting for orders coming from the GW. In the routing process, they organize themselves in paths autonomously, but all subsequent data transmissions are addressed to the GW, directly or through other STAs. Conversely, the GW can make use of its greater transmission power to periodically send broadcast messages to all network STAs, or send unicast messages to selected STAs.
## 1 Introduction
The verification for the numerical simulation model of the transmission towers is performed by comparing it with the data from the experimental measurements. Next, based on the database of natural frequencies computed by the finite element method, a Kriging surrogate model is built to effectively reduce the interference or uncertainties in the identification of the transmission tower settlement. The accuracy and convergence of the Kriging surrogate model are confirmed in typical tests. Finally, some brief summaries are drawn in the last section.
## 4 Results and Discussion
### Discussion of This Paper
#### 4.3.3 Comparison of Other Defects and Rail Weld on ABA  
The above results were obtained by assuming that the wheel tread is smooth and that welds only exist on the rail surface; the influence of other flaws in the wheel/rail contact interface was not considered. In fact, a variety of defects such as rail corrugation, wheel polygonal wear, wheel flats, etc., exist in operation, as shown in Figure 16. It is necessary to identify these interfering signals.  
A schematic diagram of ABA induced by rail corrugation and a wheel flat are presented in Figure 17 with horizontal and vertical axes representing the running time and acceleration of the axle box, respectively. As the geometric form of rail corrugation is continuously fluctuating, the excited  
Figure 16: Rail corrugation (**a**) and wheel flat (**b**).  
Figure 15: Flowchart of an approach to estimate rail weld.  
ABA in corrugation geometric size \(\lambda\) (a typical size is 65-80 mm) is continued wavelength vibration, as shown in Figure 17a. The vibration mode is different from that in Figure 10a. ABA at welds will gradually decay upon three to four wave crests due to the lack of a continued excitation source, while the rail corrugation can excite longer and with continued fluctuation. Similarly, wheel polygonal wear is continuous with circular geometry fluctuation appearing in the wheel tread. The excited acceleration of the axle box should resemble that of rail corrugation. Thus, the same method can be adopted to identify its influence.  
Differing from rail corrugation, wheel flat is a local defect existing in the wheel tread. An impact will be generated on the track system with the wheel rotating every circle. The change in ABA with time is presented in Figure 17b. It can be observed that the distance between two wave crests is the circumference of the wheel. Wheel circumference is generally about 2.7-2.9 m, which is far less than the distance (100 m) of two adjacent rail welds. On the other hand, according to site measurements, the typical wavelength range of the wheel flat in high-speed railways is from 15 to 40 mm, less than the size of a rail weld, which means that a higher ABA can be raised by wheel flats compared to rail welds. So, interference of a wheel flat in the signal of ABA can be identified by the above two differences. To be sure, a more reliable method is to install the acceleration sensors in different wheels to avoid the influence of a flat in a certain wheel.
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
###### Abstract  
Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.  
Keywords:load disaggregation; multi-scale; attention mechanism; residual network +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Impact of Sources of Si and Al  
Si and Al sources critically impact the crystallization and nucleation of zeolites due to their dissolution rate (reactivity and solubility) during synthesis and ageing processes. SSZ-13 membranes can be synthesized utilizing diverse sources of Si and Al, e.g., colloidal silica Ludox(r) AS-40 [35], monomeric tetraethylorthosilicate (TEOS) [36], fumed silica Cab-O-Sil(r) M5 [37], aluminum isopropoxide [38], sodium aluminate [29], and aluminum hydroxide [39], respectively. During this study, three different sources of Si and Al were used as shown in Table S3. The gas tightness test revealed that only membranes prepared using aluminum isopropoxide in the gel precursor exhibited significant N2 diffusion (1.2 \(\times\) 10\({}^{-7}\) [mol/(m\({}^{2}\)sPa)]), indicating a high defect density. Figure 2c demonstrated the superior CO2/CH4 separation performance of membranes synthesized using Ludox(r) AS-40 and Al(OH)3, since Al(OH)3 is less soluble compared to precursors like Al-isopropoxide or sodium aluminate and leads to slower and more controlled release of Al ions during synthesis that can favor the formation of a more ordered and well-defined zeolite framework. That is, Al sources with different surface areas can lead to dissimilar crystallization rates (kinetics of dissolution during zeolite formation and growth), crystal dimensions, and microstructural characteristics. Moreover, Al(OH)3 is more stable and has a lower reaction temperature. In addition, membranes synthesized from silica suspensions revealed nearly the same values in both CO2 and SF6 permeance. Nevertheless, when fumed silica was used, CO2 permeance was remarkably decreased. Mainly, Si sources with lower surface area and solubility like fumed silica promote the growth of large crystals (and may lead to the formation of unreacted Si particles), while high surface area Si sources (e.g., colloidal silica) induce the growth of small crystals and can be effectively dispersed within gel precursors [40].
## 3 Materials and Methods
### Method for Generating the Sonic Watermark
#### 3.2.1 The Data Input Blocks  
* The ENF measurement system--Provides the primary data for the sonic watermark. The proposed system receives the value of the ENF averaged over one second, every second. The measurement should be made with great resolution, at least \(10^{-2}\) Hz. Systems that offer \(10^{-3}\) Hz exist [6]. In  
Figure 4: Linear up-chirp signal: **(a)** Time domain representation. The signal’s frequency increases linearly as the time passes; **(b)** The variation of the frequency of the signal that is illustrated in panel **(a)**.  
Figure 5: The block diagram of the proposed sonic watermark generator. The yellow blocks deliver the data to be embedded in the recordings, the blue blocks are the controlled audio signal generators, the gray blocks are signal processing stages, and the green block is the output stage, the speaker, playing the sonic watermark in the room.  
the design process of the proposed system, the resources were allocated according to these finer measurements. This approach allows the system to work also with ENF measurement devices that offer coarser measurements (\(10^{-2}\) Hz resolution). According to the power grid specifications in [24], the variation of the ENF is in the range of \(50\pm 0.2\) Hz. If the measurement of the ENF is made with a resolution equal to \(10^{-3}\) Hz, 401 values are obtained (from 49.8 Hz to 50.2 Hz with a 0.001 Hz step size) which can be binary encoded using 9 bits. The ENF measurement is not the main subject of this paper and this is the reason those systems are not thoroughly detailed here. They are well documented in [25]. For this work, the ENF values measured at each second for the year 2015 were analyzed, because they were made available by [6]. Their histogram was computed and illustrated in Figure 6. It can be observed that the data follows a normal distribution with the estimated mean equal to 50 Hz, and the standard deviation equal to \(20.279\cdot 10^{-3}\) Hz.
* The room identification number (room ID)--represents the secondary information carried by the sonic watermark. The room ID is an integer number set once when a sonic watermark system is installed in a room. It can be used later to precisely determine the room in which the conversation that is investigated took place. Because no two rooms should have the same identifier, this number should be assigned automatically by an external room management system (e.g., a server that holds a database of the rooms in which sonic watermarking systems are installed). The room ID is used as the seed of a pseudorandom binary sequence (PRBS) generator. The generator delivers one bit per second.