esa-sceva/satcom-chunk-collection huggingface dataset:
# Copper Nanowires for Transparent Electrodes: Properties, Challenges and Applications
## 3 Oxidation Prevention
(**a**) \(Rs\) over time of pure Cu NW compared with core-shell NWs with different fractions of Ni; (**b**) \(T\) vs. \(Rs\) plot of Cu NW and core-shell NWs; (**c**) energy dispersive X-Ray spectroscopy image of a Cu-Ni core-shell NW. Adapted with permission from Ref. [20]. Copyright 2012 American Chemical Society. Cu-graphene structures were obtained by [PERSON] et al. using a chemical vapor deposition process [51]. Here the NW network was tested for 30 days in ambient conditions with little \(Rs\) change, while for pure Cu NWs the \(Rs\) increased sharply after less than five days. Composites with graphene sheets which were exfoliated in the liquid phase were recently reported by [PERSON] et al. [52]. Here the network stability was investigated for 46 days, showing a 27% increase in ambient condition and a 92% increase at 60 \({}^{\circ}\)C, with a significant improvement compared to bare Cu NW networks [52]. A different approach was recently proposed by [PERSON] et al., who applied a benzotriazole passivation layer [53]. Here benzotriazole, an organic corrosion inhibitor, made the Cu NWs stable under ambient conditions for 370 days without affecting the optoelectrical properties. Such higher stability was demonstrated also in more aggressive conditions like high temperatures \(p\) to 150 \({}^{\circ}\)C and humidity up to 90% [53]. In summary, this section provided an overview for the methods proposed in the literature for the protection of Cu NWs from oxidation under ambient or adverse conditions. Overall, all methods have in common a protective layer around the NW, which can be another metal, like Ni, metal oxides, another material like graphene, or molecules that act like corrosion inhibitors.
## 3 Numerical Analysis
### Train and Wheel-Rail Contact Model  
The CRH2 high-speed train, comprising 8 carriages, is simulated using a mass-spring damping system with 31 degrees of freedom (DOFs) per carriage, including the lateral, floating, rolling, yawing, and nodding DOFs of the car-body and 2 bogies, as well as the lateral, vertical, and yawing DOFs of the 4 wheelsets. The car body is linked to the bogie through a secondary suspension system, while the bogie is connected to the wheelset through a primary suspension system. Indeed, the suspension system comprises both springs and dampers. In particular, the car body, bogies, and wheelsets can be considered rigid bodies and ignore the coupling effect between trains (as shown in Figure 4). Details of train parameters can be found in the work by [PERSON] [27]. The actual running speed of the train is 250 km/h.  
Figure 4: Train and finite element model.  
Figure 3: Finite element model of the bridge.  
Locomotive vehicles experience vibrations due to track irregularities and earthquakes, which are then transmitted to the track and bridge structures through the contact point between the wheels and rails. This creates a dynamic interaction process within the train-bridge system. In order to consider both the vertical and lateral wheel-rail contact and the nonlinear characteristics of wheel-rail contact state, the [PERSON]'s nonlinear theory [35] and the Kalker creep theory [36] are used to obtain the vertical contact stiffness of \(1.325\times 10^{9}\) N/m [37] and the lateral contact stiffness of \(1.671\times 10^{7}\) N/m [38].
## 2 Experimental Section
### Structural Characterization  
The crystalline structure of the synthesized Ni-Co nano-arrays nanotube were examined by X-ray diffraction (XRD; Bruker D8) with Cu Ka radiation (\(\lambda\) = 0.154 nm). The state valence were characterized through X-ray photoelectron spectroscopy (XPS; Thermo Scientific Escalab 250 Xi). The surface morphological studied were carried out by scanning electron microscopy (SEM; FEI Quanta 250) and transmission electron microscopy (TEM; JEOL, JEM-2010), elemental distribution were examined by energy-dispersive X-ray spectroscopy (EDS) system, while high resolution-transmission electron microscope (HRTEM; JEOL, JEM-2010 F) is for the investigation of lattice structure. The EDS mapping experiments were conducted by transmission electron microscopy (TEM, FEI Titan G2, operated at 300 kV). Areal capacitances were calculated using Equation (1) [27].  
\[\text{Ca}=\text{I}\times\text{\Delta t}/(\text{S}\times\text{\Delta V}) \tag{1}\]  
where constant discharge current were measured as I (mA), \(\text{\Delta t}\) (s) is the total discharge time, \(\text{\Delta V}\) (v) is the potential drop during discharge and S is the electrochemical active area of the electrode.
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 2 Proposal
### Considered Scenarios  
Household supply systems are currently connected directly to the electrical network. Therefore, electrical appliances are supplied with AC and domestic demand depends on how these appliances are being used. Most of this demand occurs during peak hours when the electricity prices are higher. The use of a battery at home opens the opportunity to offer an adapted demand response, where a higher cost-effectiveness can be attained. Therefore, a modification is proposed in this study with the purpose of reducing the costs, which combines the current system with the use of a battery. However, as a battery needs an AC/DC converter for interacting with the electrical power system, this involves lower operating efficiency. Consequently, as mentioned, a new system is here proposed, where the electrical appliances are fed with DC in combination with a battery, with the consequence of discarding the losses in each appliance and in the battery output.  
As aforementioned, a comparison is presented here, assuming identical operating conditions, among three possible modes, i.e., (a) a conventional system with AC appliances and direct connection to the electrical network, (b) an AC system with AC appliances and a battery installed and (c) a combined AC/DC system with DC appliances and direct connection to the battery. The proposed models are shown in Figure 1.  
Figure 1: Simplified architecture of the proposed models: (**a**) Current AC system. (**b**) AC system with battery. (**c**) Combined AC/DC system with battery.  
In Table 1, the electrical appliances considered in the study are shown, classified per application in eight types [12]. The efficiencies in all the appliances have been established and the elements where standby is present have been pointed out.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Methods
#### 2.3.1 Selection of Vegetation Indices  
For this study, normalized difference vegetation index (_NDVI_), Simple Ratio Index (_SR_), near-infrared reflectance of vegetation (_NIR\({}_{\rm{p}}\)_) and greenness vegetation index (_GVI_) were selected for the retrieval of forest stand age in the Loess Plateau. Spectral changes in forest canopy at different forest stand ages can be better applied to the exploration of the relationship between vegetation indices and forest stand age. Table 1 shows the calculation formulas of each vegetation index.  
_NDVI_ is used to detect the forest vegetation growth state and forest coverage, eliminate some radiation errors and reflect the influence of canopy background (such as soil, snow, dead leaves and roughness) and vegetation cover [25; 26]. _SR_ is the ratio of light scattered in the near-infrared to light absorbed in the red band, which reduces the influence of atmosphere and topography. For the vegetation with a larger leaf area index (LAI) or higher canopy, the value is higher. For soil, water and non-vegetation elements, the value is lower [27; 28]. _NIR\({}_{\rm{p}}\)_ is a canopy vegetation reflectance index calculated using _NDVI_ and can  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Equation** & **References** \\ \hline Normalized difference vegetation index (_NDVI_) & \(NDVI=\frac{NIR-R}{NIR+R}\) & [25; 26] \\ Near-infrared reflectance of vegetation (_NIR\({}_{\rm{o}}\)_) & \(NIR_{\rm{p}}=NDVI\times NIR\) & [29; 30] \\ Simple Ratio Index (_SR_) & \(SR=\frac{NIR}{NIR}\) & [27; 28] \\ Greenness vegetation index (_GVI_) & \(GVI=-0.2941\times B-0.234\times\widetilde{G}-0.5424\times R+0.7276\times\) & [31; 32] \\ \multicolumn{3}{c}{\(NIR+0.0713\times SWIR_{1}-0.1608\times SWIR_{2}\)} \\ \hline \hline \end{tabular} Notes: _NIR_ is the near-infrared band, \(R\) is the red band, \(B\) is the blue band, \(G\) is the green band, \(B\) is the blue band and _SWIR_ is the short-wave infrared band.  
\end{table}
Table 1: Vegetation indices selected in this study.  
Figure 2: Flow chart in this study (quantitatively describe the relationship between spectral information and forest stand age and establish a remote sensing inversion model of forest stand age with strong spatiotemporal suitability; then, verify accuracy and evaluate the error of remote sensing data of forest stand age).  
effectively reduce the influence of soil background reflectance. \(NIR_{\text{p}}\) has become an effective tool for studying plant photosynthesis in recent years. Studying the relationship between \(NIR_{\text{v}}\) and gross primary productivity (GPP) on multiple time scales is of great significance for exploring GPP at global and regional scales [29; 30]. \(GVI\) is the weighted sum of the radiation brightness values of each band, and the radiation brightness is the comprehensive result of atmospheric radiation, solar radiation and environmental radiation, so it is greatly affected by external conditions (such as atmosphere and soil) [31; 32].
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 1 Introduction
Another challenge in sound source localization is localizing the source in the presence of noise and reverberation. There have been many attempts to obtain the interaural features in a complex environment and diminish the effects of room reverberation and background noise [21; 22; 23]. However, prior knowledge of the room's acoustical characteristics is required for most of those methods, and, without this prior knowledge, the extracted interaural features may not be optimized and some interference would be retained. Therefore, a model with high tolerance to noise and these interferences would be expected for practical applications. This article investigates the dependency between interaural features and sound source position, particularly for elevation mapping. The Mutual Information (MI) is used to demonstrate the existence of elevation-related dependency on both the ILD and IPD spectrum. Then, based on the results of the MI analysis, a chain rule-based probabilistic model for mapping the features and source locations in a 3-D space is proposed. The model is constructed based on probability estimation trees (PETs) and Random Forest (RF) framework. The PETs are formed by an information gain-based data partition technique. The features exploited by the model are further investigated, which justifies that the non-linear-dependent features (e.g., high-frequency IPD spectrum) can also contribute to an accurate localization performance. Then, aiming at evaluating the accuracy and robustness of the proposed method, the localization performances are compared with the state-of-the-art probabilistic piecewise affine mapping (PPAM) model proposed in [17] in different noise and reverberation environments without prior knowledge of the acoustical conditions.
## 2 Background
### Object Detection
#### 2.2.4 Faster R-CNN  
With aims to real-time object detection, Faster R-CNN is a consolidated algorithm based on a Region Proposal Network (RPN). RPNs start by taking an image as input. As output, they provide a set of different rectangles of object proposals. In this architecture, the selective search algorithm is different with respect to the previous R-CNN architectures. RPNs share full-image convolutional features with the detection network. It is a fully convolutional network (FCN) that simultaneously predicts object bounds at each position. This network is trained in an end-to-end base to learn the task of generating detection proposals. First, it generates a convolution feature map, which then can be passed into the RPN. Finally, it is passed into a classification layer responsible for determining whether an object or not is present [25]. The mentioned process can be visualized in Figure 7.  
As the original paper's title proposes, this architecture's main objective was to get as close to a real-time object detection algorithm as possible. This architecture has achieved outstanding performance and time response (between 0.05 and 0.2 s). The proposal's accuracy increased critically in various applications, being its most significant advantage over other analog methods. It uses a reduced number of needed rectangle region proposals  
Figure 6: Fast R-CNN architecture.  
Figure 7: Faster R-CNN algorithm.  
to 300, minimizing the computer processing need. One drawback compared to its most similar architecture (i.e., YOLO) is that all R-CNN architectures are two-stage algorithms, with YOLO being a single-stage one. Hence, YOLO is faster. YOLO is proposed as the faster approach in the literature, while Faster R-CNN is more accurate. The YOLO algorithm will be explained later in this same section.
# Opportunistic Weather Sensing by Smart City Wireless Communication Networks
## 2 OISAC of Weather--Current Situation
#### Commercial Microwave and mmWave Links
Then, the rain-induced component is to be extracted from \(\mathbf{A}_{T}(\mathbf{t},\mathbf{L})\) by filtering other-than-rain components from the overall attenuation (many methods for performing it have been proposed, e.g., [20; 30; 38; 39] among many others). An alternative approach for transforming signal level measurements in CMLs to rain measurements is a data-driven one. As the amount of data collected by CMLs has become sufficient (in the sense of sufficient historic data and climatological diversity), data-driven approaches have become possible, and several algorithms to do so have been presented (e.g., [21; 40; 41; 42], to name a few). Moreover, some data sets are even freely available online [35; 43; 44], which encourages developing more data-driven algorithms for the processing layer in the CML-based IoT for weather monitoring. Given the wide spread of CMLs, once each is converted to at least a single virtual rain gauge, it is possible to produce a number of rain products, mainly wide range rain maps at a resolution of at least the standard weather radars, and often better [22; 23]. Lastly, it is worth mentioning that although rain is the major factor that causes attenuation in wireless signals at the K-band and the E-band frequency ranges, other-than-rain water-based phenomena can also be monitored via the proposed OISAC approach. A few examples are as follows: humidity [45], dew [36], fog [46] retrieval and detection [47], and the estimation of accumulated mixed precipitation (e.g., snow, rain, and sleep) [48].
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
The message is (4), \(\mathbf{M_{G\_Formulating-Network}}\):
* After establishing the hierarchy of the network, the leaf node(s) can select its connection based on parameters of RSSI and number of connections that the router/gateway can have/already have. For this step, load balancing is required. * The gateway node disseminates connectivity message (5), \(\mathbf{M_{G\_L(n)}}\): Connectivity, for the unconnected leaf nodes within the LOS. Herein, based on the criteria, the eligible leaf nodes for connectivity send the acknowledgment message (6), \(\mathbf{M_{L(n)}}\)-G: Acknowledgment-
* The same procedure can be applied for the router/coordinator nodes in connecting to the other leaf nodes, which can discover better connectivity with those nodes. The messages \(\mathbf{M_{R\_L(n)}}\): Connectivity, \(\mathbf{M_{L(n)}}\)-R: Acknowledgment are the related messages (7) and (8), respectively. * As the gateway node is the group head, the other router nodes need to reference or update the gateway with its new connections. Message (9), \(\mathbf{M_{R(n)\_G}}\): Update, is for the connectivity update. Figure 3: General communication messages for network structure. The physical WSN testbed is designed to showcase various use cases that can validate the virtual testing available in our earlier works. The testbed for scenarios such as dynamic events and network capacity (size) is highlighted with the use of TI CC2538 due to its alignment with the virtual network, while scenarios such as node distribution with a focus on dual protocols have been seen to be aligned with components such as the TI CC1352R. The use cases as well as the general components of the testbed are further explained in this section.
## 1 Introduction  
Steganography is one of the crucial fields in information security that aims to conceal sensitive and secret data within seemingly ordinary media [1]. Unlike cryptography, which transforms the appearance of secret images into suspicious ones, steganography does not arouse suspicion from parties observing the resulting images. Steganography achieves this by hiding secret images within another image (i.e., cover image), so its existence is concealed behind the cover image. This additional layer of security is an effort of \"security through obscurity\", an advantage that steganography has over cryptography [2].  
Despite the existing advancements in steganography, there is a continuous need for enhancing the security and robustness of these methods, especially in the context of digital images. One area that has shown promise in this regard is the use of permutation techniques to randomize the embedding locations of secret data [3, 4]. The Josephus permutation, traditionally used in cryptographic applications [5, 6], offers a novel approach to enhancing the randomness and security of steganographic methods.  
In this study, we propose a novel steganography algorithm that integrates the Josephus permutation and a 3-3-2 LSB embedding scheme. The Josephus permutation is employed to enhance the randomness of pixel distribution locations derived from the keystream generated by a chaotic logistic map. Subsequently, the 3-3-2 LSB embedding technique is utilized to embed a secret RGB image into a cover RGB image. The incorporation of the Josephus permutation into the keystream serves to extend the security obscurity period of the LSB 3-3-2 embedding.  
Our approach explores the use of the Josephus permutation in image steganography, which has not been extensively studied before. Historically, the Josephus permutation has been applied primarily in cryptographic scenarios. By introducing this technique into the steganographic process, we aim to leverage its ability to generate highly random and non-repetitive sequences, thereby enhancing the security of the embedded data.  
As far as our knowledge, unlike existing methods which often use LSB techniques for embedding text or grayscale images, our approach is also the first to employ the LSB 3-3-2 method for RGB-to-RGB image steganography, representing a new way of using the LSB 3-3-2 in the context of image steganography. Prior research has primarily focused on grayscale images, which do not fully utilize the potential of color information in enhancing security and capacity.  
By addressing these gaps, our proposed method aims to advance the field of image steganography by providing a more secure and efficient technique for hiding color images. This research offers practical implications for its application in secure communications.
## 6 Conclusions  
WSNs are generally densely deployed in specific fields for monitoring required parametric values. The major aim of any wireless sensor network is prolonging overall network lifetime as much as possible. Therefore, energy efficiency is a high priority parameter for any sensor network, and thus, any efficient management needs to be focused on it. In this paper, two quality of services-based parameters such as energy efficiency and end-to-end delay were considered for the evaluation of the proposed protocols. Specifically, the main contribution of the work with regard to authors' previous work can be summarized as follows: in other to increase the lifetime of the network, the Levenberg-Marquardt neural network was embed into well-known energy efficient strategies: LMNN approach was implemented in LEACH, Sub-LEACH and EESR protocols, as shown in Figures 10-14. Simulation results showed that Sub-LEACH-LMNN outperforms the competing algorithms in terms of both energy and end-to-end criteria. Additionally, as a second contribution of the work, this paper worked toward anomaly detection: SVM was used to classify normal and anomaly classes, also showing higher accuracy compared to other existing models. Therefore, our management of a network with energy efficiency and security is suggested, and our results are extensible to the diverse base technologies such as IEEE 802.15.4 and ZIG BEE, by merely changing basic configurations in the simulation parameters. Specifically, the IDS proposed model achieved a 96.15% TPR and a 5.11% FPR, improving the rates by 85.00% and 5.43%, respectively, given by the best of its competitors. In future works, more sophisticated algorithms based on machine learning techniques will be integrated to obtain a more energy efficient and cost-effective protocol.  
Conceptualization, M.M. and R.P.d.P.; methodology, M.M.; software, M.M. and Y.K.; validation, Y.K. and S.N.; formal analysis, S.N.; investigation, R.P.d.P. and Y.K.; resources,Y.K. and S.N.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, R.P.d.P. and J.E.M.-E., Y.K. and S.N.; visualization, M.M. and S.N.; supervision, Y.K. and S.N.; project administration, M.M. and R.P.d.P.; funding acquisition, R.P.d.P. and J.E.M.-E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
NonenclatureBS & Base Station CH & Cluster Head LMNN & Levenberg-Marquardt neural network MEMS & micro-electromechanical system N-CH & non-Cluster Head LEACH & Low-Energy Adaptive Clustering Hierarchy EESR & Energy-Efficient Sensor Routing SN & Sensor Node SVM & Support Vector Machine TDMA & Time Division Multiplexing Access WSN & Wireless Sensor Network
# Review on Rail Damage Detection Technologies for High-Speed Trains
## 1 Introduction
How to quickly, accurately, and intelligently detect railway damage has always been an important challenge for many scientific researchers, and it is also a very important task in the railway maintenance process. Currently, there is a great demand for very completeand intelligent railway maintenance, upkeep, and detection systems and equipment, the development of which is a difficult problem for many scientific and technical personnel to tackle. At present, the main method of rail inspection still relies on manual inspection, such as manual patrol, the scattering detection magnetic powder method, and other manual methods for inspection. Methods based on new eddy current meters, comprehensive track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, laser radar, new ultrasonic detectors, rail inspection vehicles, rail inspection robots, laser vehicle-mounted rail inspection systems, track recorders, self-propelled carts, etc., have certain applications in rail inspection, but there are still certain limitations. Developing more efficient, reliable, fast, accurate, and intelligent systems and detection equipment is particularly important for ensuring the safety of railway track quality.