esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
###### Abstract  
Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image match in terms of the detection repeatability and the matching accuracy. To improve the matching performance, a feature detection and description method based on consistent edge structures of images (DDCE) is proposed in this paper. First, consistent edge structures are detected to obtain similar contents of infrared and visible images. Second, common feature points of infrared and visible images are extracted based on the consistent edge structures. Third, feature descriptions are established according to the edge structure attributes including edge length and edge orientation. Lastly, feature correspondences are calculated according to the distance of feature descriptions. Due to the utilization of consistent edge structures of infrared and visible images, the proposed DDCE method can improve the detection repeatability and the matching accuracy. DDCE is evaluated on two public datasets and are compared with several state-of-the-art methods. Experimental results demonstrate that DDCE can achieve superior performance against other methods for infrared and visible image match.  
infrared and visible image match; common feature detection; consistent edge structure; edge orientation histogram +
Footnote †: journal: Applied Sciences
# Perspectives in Liquid-Crystal-Aided Nanotechnology and Nanoscience
## 4 LC-Aided-Assembly and Self-Assembly of Nanomaterials
The induced helical pitch of the cholesteric CNC suspension is also intricate. Although many studies have demonstrated a decrease of pitch with increasing CNC concentration [403; 404; 405], the CNCs in all these cases are electrostatically charged. It is thus unlikely that the decrease of the pitch is solely due to the concentration increase. It may also be related to electrostatic screening effects [406; 407; 408]. So far, the manipulation of the pitch has been successfully realized by controlling various parameters including the concentration of ions [409], different kinds of solvents [410], ultrasound treatment [411], etc. However, the methods for controlling  
Figure 15: **(a)** POM images and **(b)** macroscopic photographs between crossed polarizers of GO aqueous dispersions in planar cells with mass fraction \(f_{\text{m}}\) of \(5\times 10^{-4}\), \(1\times 10^{-3}\), \(3\times 10^{-3}\), \(5\times 10^{-3}\), \(8\times 10^{-3}\), \(1\times 10^{-2}\) (from 1 to 6). Reproduced with permission from [165]. Copyright (2011) American Chemistry Society. **(c)** Schematic illustration of GO LC membrane in protic ionic liquid for nanofiltration. Reproduced with permission from [392]. Copyright (2018) American Chemistry Society. **(d)** SEM images of an as-spun GO fibre. Reproduced with permission from [387]. Copyright (2013) John Wiley and Sons. **(e)** Illustration of fire ignition retardation behaviour for **(I)** neat PU foam and **(II)** PDA/GO-coated PU foam. Reproduced with permission from [393]. Copyright (2018) John Wiley and Sons. the orientation of the helix axis are still limited. Recently, a cholesteric CNC film with controllable helix was achieved by [PERSON] et al. The authors successfully controlled the cholesteric domains by using small commercial magnets. By manipulating the direction of the magnetic field, the orientation of the helix can be adjusted from 0\({}^{\circ}\) to 17\({}^{\circ}\), leading to interesting optical properties [412]. The geometrical characteristics of CNCs [413], as well as the nature of the solvents [414], appear to play an important role in the behaviour of pitch. A most desirable feature of the cholesteric CNC suspensions is the drying to solid films. The helical superstructure will not vanish due to kinetic arrest; in contrast, the pitch of the helix will be decreased to the range of a visible selective reflection, making the films like photonic crystal paper [415] that can be used as a colorimetric humidity sensor [416] or temperature and PH sensor [409], for optical encryption [417; 418], in chiral plasmonics [419], as light scattering shutters [420], as nano-templates [421], and even in mirrorless lasing [160]. Recently, [PERSON] et al. reported a cholesteric CNC photonic semi-spherical film with a polydomain morphology where the thickness, structure and optical properties of the film changed along the surface, leading to a broadband reflection covering the entire spectral range [422].
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 2 Motivation and Problem Statement
### Problem Statement  
Sensors are mainly used to transmit collected data or forward data from other sensors to a base station in a sensor network. A packet can be transmitted when there is enough energy or it needs to wait until the sensor receives enough energy. Assume there are \(n\) packets to be transmitted or forwarded. How to transmit as much amount of data as possible while consuming as little energy as possible and taking full advantage of the wireless receiving energy is a problem. Assume that  
Figure 3: Different transmission orders result in different energy waste. (a) Order of 5,3,2,4,4,2; (b) Order of 5,4,3,2,2; (c) Order of 2,2,3,4,5.  
Figure 2: Illustration of energy overflowing.  
the transmission rate is much higher than the low power wireless charging rate. Then, data can be transmitted immediately as long as there is enough energy.  
Let the capacity of the battery equipped on a sensor be \(C\). In each period, the sensor receives \(E\) units of wireless energy. Let \(C=mE\), i.e., the battery is full after \(m\) periods of wireless charging. For simplicity, let \(m\) be an integer. In this paper, we assume that the battery is empty initially. Given \(n\) packets to be transmitted to the base station, packet \(i\) is of size \(s_{i}\) which means it costs \(s_{i}\) units of energy to transmit this packet. The object of our problem is to minimize energy waste, or, on the other side, to maximize the amount of transmitted data in the periodical wireless charging mechanism. As shown in Figure 3, different transmission orders result in different energy waste. A wise transmission order wastes little energy, which is a rare resource in sensors and transmits as much amount of data as possible.  
We first consider the situation where the number of wireless charging periods \(K\) is given. The problem can be formalized as follows.  
Definition 1: Let the battery capacity be \(C\), and the charging energy in each period be \(E\), where \(C=mE\). Given \(n\) packets, \(S=\{s_{1},s_{2},\ldots,s_{n}\}\), where \(s_{i}\) is the size of packet \(i\), the problem is to find a transmission order to maximize the amount of transmitted data in \(K\) periods of wireless charging.  
This problem is the case that sensors stay within the range of wireless power source for a limited time, i.e., \(K\) charging periods. In such a case, the sensors need to transmit as much amount of data as possible in the limited charging time.  
If the sensors can carry out wireless charging for a long enough time, we then consider another version of this problem, which is formalized as follows.  
Definition 2: Let the battery capacity be \(C\), and charging energy in each period be \(E\), where \(C=mE\). Given \(n\) packets \(S=\{s_{1},s_{2},\ldots,s_{n}\}\), where \(s_{i}\) is the size of packet \(i\), the problem is to find a transmission order to minimize the number of wireless charging periods so that all the data can be transmitted.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 1 Introduction  
Sound source localization refers to the process of recording and analyzing the acoustic signal of sound propagation through an elastic medium from a source that radiates sound waves to deduce the location of the sound source. Sound source localization technology is widely used in aerospace, military, medical, seismic, intelligent voice recognition and non-destructive testing applications. Sound source localization technology utilizes acoustic, signal detection, digital signal processing and many other technologies.  
Acoustic emission (AE) source localization technology is an important part of structural health inspections. AE is a physical phenomenon in which a localized source in a material rapidly releases energy to produce elastic waves. AE is ubiquitous in daily life, since both fracture and deformation of materials can produce AE phenomena. Sources related to the fracture and deformation of materials are called AE sources, which include pipe leakage, friction, collision and combustion. Sources that are not related to the fracture and deformation of the material are called secondary AE sources. AE source signals contain important information about material deformation, crack extension and damage location and extent. The process of recording, processing and analyzing the AE signals and deducing the location of the AE source from the AE signals is known as AE localization technology.  
This article mainly summarizes the methods of acoustic emission source localization in different dimensions and provides a detailed solution process for acoustic source localization using one-dimensional, two-dimensional and three-dimensional time difference methods. As dimensions increase, positioning becomes increasingly complex and difficult. The three-dimensional positioning method is currently a key research direction whichhelps to apply acoustic emission source positioning technology to practical engineering. At present, there is no detailed review and comparison of the localization methods for three-dimensional structures in research. This article classifies the current three-dimensional sound source localization methods and compares the sound source localization methods for three-dimensional structures. Notes on this research direction are proposed at the end.
# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 3 Methods
### Landsat Maps of Snow Extent  
In order to assess the quality of S1 based snow maps, we generated maps of snow extent from Landsat data over study areas in the Alps and in Iceland. Due to widespread cloudiness in these regions during the snowmelt seasons, the choice of suitable Landsat images for comparison with S1 snow maps is rather limited. The acquisition time, geographic location and solar elevation of the used Landsat scenes are specified in Table 2.  
The images cover the Tr\(\ddot{\text{o}}\)llaskagi Peninsula in northern Iceland and two subareas of the Alps. The Iceland image is cloud-free. The image of the Swiss Alps contains a few small cumulus clouds. The image of the Otztal Alps shows a substantial number of scattered cumulus clouds, mainly over snow-free mountain slopes. Clouds of this type can be well detected by means of visible and short-wave infrared reflectance. We use thresholds in Landsat-7 bands 3 and 5 for cloud screening. Cloud shadows are identified visually by mapping dark pixels in the neighborhood of the clouds, taking into account the direction of solar illumination. The resulting cloud masks include the clouds and the cloud shadows.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Satellite** & **Date \& Time (UTC)** & **Lat/Lon** & **Sun Elev.** \\ \hline A1, Tr\(\ddot{\text{o}}\)llaskagi, Iceland & Landsat-8 & 27 June 2015 12:45 & 65.59\({}^{\circ}\) N/19.98\({}^{\circ}\) W & 47.3\({}^{\circ}\) \\ A2, Otztal Alps, Austria & Landsat-7 & 5 June 2015 10:03 & 47.45\({}^{\circ}\) N/11.02\({}^{\circ}\) E & 61.4\({}^{\circ}\) \\ A3, Swiss Alps & Landsat-7 & 3 June 2015 10:16 & 46.03\({}^{\circ}\) N/7.39\({}^{\circ}\) E & 62.1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the Landsat scenes used for comparison with S1 snow maps. Latitude, longitude and sun elevation refer to the scene center.  
Landsat-7 images are impaired by data gaps due to failure of the SLC. In order to minimize the impact of the SLC failure, we restrict the analysis to the central parts of the images, which are only slightly affected by this failure and, exclude the missing lines for the statistical comparison.  
For generating the Landsat snow maps, we apply a binary algorithm with threshold-based segmentation in normalized difference snow index (\(NDSI\)) images, using as threshold value of \(NDSI=0.4\), as applied for generating Landsat climate data records on snow extent [35]. The \(NDSI\) is computed from top-of-atmosphere reflectances:  
\[NDSI=\frac{R\left(VIS\right)-R\left(SWIR\right)}{R\left(VIS\right)+R\left(SWIR\right)} \tag{5}\]  
For the Landsat-7 Enhanced Thematic Mapper (ETM+) we use the visible (VIS) band 2 (0.52-0.60 \(\upmu\)m) and the shortwave infrared (SWIR) band 5 (1.55-1.75 \(\upmu\)m), for the Landsat-8 Operational Land Imager (OLI), the band 3 (0.53-0.59 \(\upmu\)m) and the band 6 (1.57-1.65 \(\upmu\)m). The binary snow map is posted at 30 m pixel size in UTM projection, corresponding to the pixel size of the Landsat images.
## 3 Experimental Tests
### Specim Design  
#### 3.2.1. Grider Specim Design  
Four piles were horizontally arranged in a single row beneath the abutment in the IAB. Because of the spatial limitations, one-quarter of the width of the girder-abutment-pile cap connection (4.25 m) for one pile was considered for the design of the test specimen. Based on the scale ratio (\(1/10\)) and flexural strength similarity relationship, a girder specimen with a length (\(L\)) \(\times\) width (\(B\)) \(\times\) height (\(H\)) of 0.7 m \(\times\) 0.425 m \(\times\) 0.151 m was obtained, as shown in Figure 3. The XTRACT v3.0.5 software [39] was used to design the longitudinal rebars and stirrups for the girder specimen. Twenty-two round rebars with a 6 mm diameter were arranged in a single row in the rectangular cross-section of the girder, with nine rebars along each of the top and bottom sides, considering a 10 mm cover thickness. Seven stirrups with a 6 mm diameter and 75 mm pitch were arranged along the girder's length, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Dimension** & **Values (m)** \\ \hline \multirow{2}{*}{Grider (prestressed concrete, single-box girder with three cells)} & Width & 17 \\  & Height & 1.6 \\ \hline \multirow{3}{*}{Abutments (rectangular wall)} & Height & 5.5 \\  & Thickness & 1.0 \\  & Width & 17 \\ \hline \multirow{2}{*}{Piles beneath each abutment (4 piles with a circular cross-section)} & Length & 25 \\  & Diameter & 1.5 \\ \hline \multirow{3}{*}{Piers (rectangular twin-column piers)} & Height & 6.06 \\  & Thickness & 1.2 \\  & Width & 1.8 \\ \hline \multirow{2}{*}{Piles beneath each pier (2 piles with a circular cross-section )} & Length & 32 \\  & Diameter & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main features of the bridge.  
Figure 2: Engineering background: (**a**) overall view of the IAB; (**b**) details on the girder, integral abutment, and pile (unit: cm).
# Fuzzy Sliding Mode Control of Vehicle Magnetorheological Semi-Active Air Suspension
## 3 Modeling of Quarter Vehicle MR Semi-Active Air Suspension System
### Modeling of Quarter Vehicle MR Semi-Active Air Suspension System  
Passive suspension is mainly composed of a passive damper and spring with immutable stiffness, and its structure is relatively simple, as shown in Figure 11. The damping force and stiffness of passive suspension cannot be adjusted in real time with different road surface and vehicle parameters. Under a certain suspension relative speed, only a fixed suspension output damping force can be provided. Therefore, the vibration damping capacity of passive suspension decreases significantly, and its road adaptability is poor under a certain range of road surface conditions, especially on a bad road surface.  
Figure 12 shows the model of a quarter vehicle MR semi-active air suspension system, which is composed of a car body (sprung mass), wheel (unsprung mass), tire stiffness, damping and suspension stiffness.  
According to [PERSON]'s second law, the motion equation of the system is as follows:  
\[\left\{\begin{array}{l}m_{b}\ddot{x}_{b}+k_{a}(x_{b}-x_{w})+c_{0}(\dot{x}_{b} -\dot{x}_{w})=f_{d}\\ m_{w}\ddot{x}_{w}+k_{a}(x_{w}-x_{b})+c_{0}(\dot{x}_{w}-x_{b})+k_{t}(x_{w}-x_{t })=-f_{d}\end{array}\right. \tag{19}\]  
where \(m_{b}\) and \(m_{w}\) are the mass of the sprung body and the unsprung wheel, respectively, \(k_{a}\) is the stiffness of the air spring, \(k_{t}\) is the stiffness of the tire, \(x_{b}\) is the displacement of the  
Figure 11: Model of quarter car passive suspension.  
Figure 12: Model of quarter vehicle air suspension.  
mass of the body, \(x_{w}\) is the displacement of the wheel, \(x_{t}\) is the displacement of the road surface excitation, and \(f_{d}\) depicts the output damping force of the MR damper.  
The system state variable can be denoted as follows:  
\[x=(x_{b},x_{w},\dot{x}_{b},\dot{x}_{w}) \tag{20}\]  
The output variable can be depicted as follows:  
\[y=(\ddot{x}_{b},x_{b}-x_{w},k_{t}(x_{w}-x_{t})) \tag{21}\]  
where \(\ddot{x}_{b}\) is the acceleration of the car body, \(x_{b}-x_{w}\) is the dynamic deflection of the suspension, and \(k_{t}(x_{w}-x_{t})\) is the dynamic load of the tire.  
The state space expression of the suspension system is as follows:  
\[\left\{\begin{array}{c}\dot{x}=Ax+Bu\\ y=Cx+Du\end{array}\right. \tag{22}\]  
where  
\[A=\left[\begin{array}{cccc}0&0&1&0\\ 0&0&0&1\\ -\frac{k_{x}}{m_{b}}&\frac{k_{x}}{m_{b}}&-\frac{c_{0}}{m_{b}}&\frac{c_{0}}{m _{b}}\\ -\frac{k_{x}}{m_{w}}&-\frac{k_{x}+k_{t}}{m_{w}}&\frac{c_{0}}{m_{w}}&-\frac{c_{ 0}}{m_{w}}\end{array}\right],\ B=\left[\begin{array}{ccc}0&0\\ 0&0\\ 0&\frac{1}{m_{b}}\\ \frac{k_{t}}{m_{w}}&-\frac{1}{m_{w}}\end{array}\right]\]  
\[C=\left[\begin{array}{cccc}-\frac{k_{x}}{m_{b}}&\frac{k_{x}}{m_{b}}-\frac{c_ {0}}{m_{b}}&\frac{c_{0}}{m_{b}}\\ 1&-1&0&0\\ 0&k_{t}&1&0\end{array}\right],\ D=\left[\begin{array}{ccc}0&\frac{1}{m_{b}} \\ 0&0\\ -k_{t}&0\end{array}\right]\]
## 4 Finite Element Results  
A total of 336 models were analyzed to investigate the effect of the length-to-height ratio (_L/h_) on the elastic critical LTB of steel beams. For this, simply supported beams were simultaneously subjected to negative end moments and transverse loadings on the top flanges. As mentioned, the values of the ratio of _L/h_ from 10 to 40 with an interval of 5 were used. In addition, values of \(\alpha\) from 0 to 1 and \(M_{q}\)/\(M_{1}\) and \(M_{p}\)/\(M_{1}\) ratios ranging from 0.5 to 3 with an interval of 0.5 were considered.  
Figures 7 and 8 show the FEA results for steel beams with LC\({}_{1}\) and LC\({}_{2}\), respectively. The representative cases with \(\alpha\) of 0 and 1, corresponding to the cases of one negative end moment and negative end equal moments, are presented. Moreover, the results are presented as the ratio of the elastic critical buckling of beams with LC\({}_{1}\) and LC\({}_{2}\), M\({}_{cr}\), to the elastic critical buckling of beams subjected to end equal moments, \(M_{ocr}\), with respect to \(M_{q}\)/\(M_{1}\) or \(M_{p}\)/\(M_{1}\). Obviously, these figures show that the ratio of \(M_{cr}\)/\(M_{ocr}\) for beams with LC\({}_{1}\) and LC\({}_{2}\) increases as the ratio of _L/h_ increases. As can be seen from the figures, the data also indicate that the values of M\({}_{cr}\)/M\({}_{ocr}\) are greater than 1 and change with the shape of the bending moment diagrams. In the case of \(\alpha=0\), the ratio of \(M_{cr}\)/\(M_{ocr}\) gradually decreases as the \(M_{q(p)}\)/\(M_{1}\) ratio increases for both LC\({}_{1}\) and LC\({}_{2}\), as shown in Figures 7a and 8a, respectively. In the case of \(\alpha=1\), the ratio of \(M_{cr}\)/\(M_{ocr}\) increases when \(M_{q(p)}\)/\(M_{1}\) is less than 1. The ratio of the \(M_{cr}\)/\(M_{ocr}\) ratio gradually decreases when the \(M_{q}\)/\(M_{1}\) ratio is greater than 1, as shown in Figures 7b and 8b.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{_L/h_} & \multicolumn{2}{c}{**Elastic Critical Moment (kN.m)**} & \multirow{2}{*}{**Difference (\%)**} \\ \cline{2-3} \cline{5-4}  & **[PERSON] and [PERSON] [29]** & & **FEA** \\ \hline
10 & 1894.96 & 1891.12 & \(-\)0.20 \\
15 & 1003.31 & 996.84 & \(-\)0.65 \\
20 & 670.97 & 663.96 & \(-\)1.06 \\
25 & 503.75 & 497.04 & \(-\)1.35 \\
30 & 404.05 & 397.88 & \(-\)1.55 \\
35 & 337.94 & 332.32 & \(-\)1.69 \\
40 & 290.83 & 285.72 & \(-\)1.79
# IoT Services for Monitoring Food Supply Chains
## 3 Methodology and Implementation
### Platform Architecture
In addition, Forward Error Correction (FEC) methods, such as block codes and convolutional codes, add redundancy to the messages so that errors can be corrected without requiring retransmission [27]. Adaptive Data Rate (ADR) enables the network to dynamically adjust transmission parameters, such as power and data rate, based on link quality, thereby ensuring optimal performance. Link quality monitoring enables dynamic adjustments in response to changes in network conditions, such as varying signal strength. Retransmission strategies often employ exponential backoff to manage retries, while timeout mechanisms ensure that devices do not indefinitely wait for responses. To further enhance redundancy, LoRa networks utilise multiple gateways and employ mesh networking, providing multiple paths for data transmission and increasing the likelihood that messages will be successfully delivered even if one path fails. Additionally, channel diversity enables devices to switch between multiple channels, thereby avoiding congestion or interference. Finally, Quality of Service (QoS) mechanisms prioritise critical messages, ensuring important data is delivered reliably even in the presence of network congestion or failures. These combined approaches make LoRa networks highly resilient, ensuring robust communication in challenging environments. LoRaWAN networks predominantly employ the Aloha protocol as their primary method for facilitating communication between end devices and the corresponding network servers. This approach allows end devices to transmit data by routing it through a gateway to the network server. Data transmission occurs specifically when one or more of the sensors integrated into the end devices detect a notable change in their surrounding environment. Additionally, data transmission can be initiated by other significant events, such as when certain predefined thresholds are surpassed, like exceeding temperature limits. This system enables efficient and responsive communication, ensuring that critical environmental changes are promptly relayed to the network for processing and action [28].