esa-sceva/satcom-chunk-collection huggingface dataset:
# A SAR-Based Index for Landscape Changes in African Savannas
###### Abstract  
Change detection is one of the main applications in earth observation but currently there are only a few approaches based on radar imagery. Available techniques strongly focus on optical data. These techniques are often limited to static analyses of image pairs and are frequently lacking results which address the requirements of the user. Some of these shortcomings include integration of user's expertise, transparency of methods, and communication of results in a comprehensive understandable way. This study introduces an index describing changes in the savanna ecosystem around the refugee camp Djabal, Eastern Chad, based on a time-series of ALOS PALSAR data between 2007 and 2017. Texture based land-use/land cover classifications are transferred to values of natural resources which include comprehensive pertinent expert knowledge about the contributions of the classes to environmental integrity and human security. Changes between the images are analyzed, within grid cells of one kilometer diameter, according to changes of natural resources and the variability of these changes. Our results show the highest resource availability for the year of 2008 but no general decline in natural resources. Largest loss of resources occurred between 2010 and 2011 but regeneration could be observed in the following years. Neither the settlements nor the wadi areas of high ecologic importance underwent significant changes during the last decade.  
ALOS PALSAR; multi-temporal analysis; resource monitoring; refugee camps; synthetic aperture radar (SAR) +
Footnote †: journal: Remote Sensing
# Blind Compensation of \(I/q\) Impairments in Wireless Transceivers
## 3 Simulation Results
### BER Performance in the Presence of Multi-Path Channel
#### 3.2.2 Parameter Estimation under Unknown Channel  
In the previous section, the imbalance parameters were estimated after removing the channel effect assuming that the channel response was known. The next step, is to understand the effect of channel on the PDF of the output signal and estimate the \(I/Q\) imbalance parameters in the presence of unknown wireless channel response. In order to understand this, the following definitions are made:  
\(g_{\text{in}}\): KDE estimate of the input signal's PDF.  
\(g_{\text{CH}}\): KDE estimate of the output signal's PDF under the influence of Tx and Rx impairments and multi-path channel.  
\(g_{\text{IQ}}\): KDE estimate of the output signal's PDF under the influence of Tx and Rx impairments alone in AWGN channel.  
\(g_{\text{S}}\): KDE estimate of the output signal's PDF under the influence of Tx and Rx impairments and multipath channel effects after gain and phase synchronization.  
Table 3 compares these PDFs to understand the effects of wireless channel on signal's PDF. KL divergence and square of Hellinger distance are higher when comparing \(g_{\text{in}}\) with \(g_{\text{CH}}\). This is due to the combined effect of transceiver impairments and channel. These merits are comparatively lower when comparing \(g_{\text{in}}\) with \(g_{\text{IQ}}\) as \(g_{\text{IQ}}\) only considers the effects of transceiver impairments alone and not the multi-path channel. However, if we perform gain and phase synchronization on the output signal under the influence of transceiver's impairments and multipath channel, we obtain \(g_{\text{S}}\). Table 3 and Figure 9 show that the divergence between \(g_{\text{S}}\) and \(g_{\text{IQ}}\) is considerably reduced once a gain and phase synchronization procedure is performed. Due to this closeness, the proposed mitigation strategy can be applied to estimate the desired parameters. This has been verified in Figure 10, which shows the BER using the proposed estimation methodology.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \(\mathbf{D_{\text{KL}}}\) & \(\mathbf{D_{\text{H}}^{2}}\) \\ \hline \(g_{in}\parallel g_{CH}\) & 0.0588 & 0.0135 \\ \(g_{in}\parallel g_{\text{IQ}}\) & 0.0162 & 0.0042 \\ \(g_{\text{IQ}}\parallel g_{\text{S}}\) & 0.0039 & 9.59 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of PDFs for \(g=g^{\prime}=0.5\) dB and \(\theta=\theta^{\prime}=-2\) degrees under 20 dB SNR and Multipath Channel for 16-QAM OFDM signal.  
Figure 9: Contour plot for the PDF of 16-QAM OFDM \(I/Q\) signals using the KDE method under multipath channel and 20 dB SNR. The colored contours show the variations in PDF due to Tx and Rx \(I/Q\) imbalance only under Additive White Gaussian Noise (AWGN). The black contours show the PDF of the output signal under the influence of \(I/Q\) imbalance and channel after gain and phase synchronization.
## 3 Results
### Factors Affecting the Performance of Fully Grouted Rock Bolts
#### 3.1.1 Rock-Bolt-Related Factors  
Type and Shape  
Fully grouted bolts are divided into two groups, i.e., rock bolts and cable bolts, which are further subdivided into smooth surface bars, ribbed bars, single conical lugged bars, double conical lugged bars, D-bolts, plain cage, bird cage, nut cage, mini cage, etc. [23; 24; 25], of which a few are shown in Figure 2. RMR, Q, and RMi systems focus on rock-bolt designs in tunneling because cable bolts are generally used in mining engineering applications, whereas rock bolts are used in both mining and civil engineering projects [26]. The bolt type has a strong influence on their deformation behavior [27; 28; 24].  
Grouted rock bolts (CMC type) have the advantage of high load-bearing capacity, owing to their better load-transfer mechanisms. The load transfer in fully grouted rock bolt is due to adhesion or friction and/or mechanical interlocking. There is a small amount of adhesion between the grout-bolt interface [29], although some researchers conclude that there is no adhesion [30]. The main mechanism  
Figure 2: Different types of bolts: (**a**) fully grouted bolt, (**b**) D-bolt, (**c**) expansion shell bolt, and (**d**) inflated steel-tube rock bolt [23].  
involved in load transfer is mechanical interlocking. Mechanical interlocking is the result of filling the irregularities between the rock bolt and drilled-hole peripheries (rock). The test results show that the bolt surface profile has a dominant influence on the load transfer capacity [24; 25]. Rougher bolt profiles result in improved load transfer as compared to the plane surfaces. The magnitude of the shear stress and stiffness developed along the bolt/resin/rock interface is influenced by the bolt profile configurations, i.e., rib spacing and rib height [31]. For a rib height of 1.8 mm, experimental results show an increase of more than 25% occurs in the peak load during the rock bolt pull-out tests for 48 mm rib spacing, compared to those for the 12 mm rib spacing [32].
## 1 Introduction
Projections anticipate that this industry will expand to approximately USD 210 billion by 2026 [30]. Therefore, strategies to reduce food waste by upcycling them to produce new food ingredients/extracts are paramount [31]. This aligns with multiple Sustainable Development Goals (SDGs), contributing to the global agenda for sustainable development and addressing interconnected challenges, such as hunger, environmental degradation, and economic inequality. For instance, by upcycling GF peels into new extracts/ingredients, we contribute to reducing hunger and ensuring access to nutritious food for all (SDG 2), while enhancing human well-being with bioactive ingredients in the upcycled food (SDG 3) and promoting sustainable consumption and production patterns (SDG 12). In addition, food waste generates significant greenhouse gas emissions when it decomposes in landfills. Therefore, using GF by-products to produce new value-added ingredients will mitigate climate change (SDG 13), while contributing to the preservation of terrestrial ecosystems (SDG 15) [32; 33]. Promoting the integration of upcycling practices within food systems holds significant promise for enhancing circularity within the food industry. Exploring this potential has emerged as a prominent topic of interest throughout the food value chain. This research work aims to evaluate the antioxidant, antimicrobial, and anti-inflammatory activity of an EO and polyphenol-rich extracts from GF peels cultivated in Egypt. In addition, a multi-compound cascade approach is employed to collect bioactive extracts from the same by-product, increasing the circular economy value. The antioxidant capacity was evaluated using two standard methods: DPPH and nitric oxide; the antimicrobial activity was tested against both Gram-positive (Gram+) and Gram-negative (Gm\(-\)) bacteria as well as fungi; and the anti-inflammatory activities were analyzed by the cyclooxygenase enzyme expression (COX1 and COX2). The chemical composition of the EO and polyphenol-rich extracts was analyzed by GC/MS and LC/MS.
## Finite Element Models
### Finite Element Type and Mesh
#### 2.3.3 Interface and Contact Processing  
The interface element that was provided by ABAQUS was used to simulate the matching contact surfaces between steel tubular and concrete. Simultaneously, to consider the normal and tangential contact between two surfaces, interfacial friction was modeled by using the Coulomb friction model, of which the friction coefficient was set as from 0.2 to 0.6 [68]. In this paper, the coefficient of friction was selected as 0.25, which was also suggested in the research by [PERSON] [69], [PERSON] et al. [70], [PERSON] et al. [71], and [PERSON] et al. [72]. The contact surface model can simulate infinitesimal sliding and friction between the concrete and the steel tube, in which the concrete and the steel tube were allowed to separate, but not to penetrate, each other [73; 74].  
Considering that steel tubes being modeled as shell elements, the rigid plates at the end of L-shaped CFST columns and L-shaped inner diaphragm were solid elements and the connections between them were welded, the interface contact relationships were defined between them as the Shell-to-Solid Coupling. Since the H-shaped steel beam was welded to the L-shaped CFST columns, the *TIE constraint relationship was used to simulate the welding. The hard contact was applied between rigid plate, L-shaped inner diaphragm and core concrete. In the experiment, all composite slabs were in the elastic stage, and no slide appeared between the composite slabs and the steel beams. Therefore, we used the approach of nodal coupling to simulate the contact between the slabs and steel beams.  
Figure 3: Typical finite element models of the L-shaped concrete-filled steel tubular (CFST) frame. (a) CF1 and CF2; (b) CF3 and CF4.
# Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data
###### Abstract  
The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements--i.e., landmarks, paths, nodes, edges, and districts--by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses--i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city.  
city image; Lynchian elements; image processing; sentiment analysis; social media analytics; urban analytics; big data analytics; urban informatics; Colombo; Sri Lanka
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 6 Evaluations
When the block length is \(L=256\), the FPBFxLMS IV and the FPBMFxLMS algorithms with a delay of \(D=512\) do not gain any noise reduction, and the FPBFxLMS III algorithm with a delay of \(D=256\) only exhibits a 10-dB noise reduction. The experiment demonstrates that the delay in the signal path has a major effect on the broadband noise reduction. In Figure 11, we investigate the stability of the frequency-domain algorithms with and without the modified filtered-x structure. Specifically, we compare the FPBFxLMS IV, FPBMFxLMS, and three delayless algorithms. The narrow band white noise is used as the reference. We use a small step size \(\mu=0.01\) for Figure 11a and a large step size \(\mu=0.08\) for Figure 11b. All the algorithms involved exhibit a similar convergence performance when the step size is small. However, when a large step size is adopted, the frequency-domain algorithms without the modified filtered-x structure diverge, but the modified filtered-x algorithms are stable because the available step size of the modified filtered-x algorithm is larger than those of the FxLMS-type algorithms. The proposed delayless algorithm outperforms all the other methods. Figure 10: Learning curves of the frequency-domain ANC algorithms with narrow band noise as input. (**a**) \(L=64\). (**b**) \(L=128\). (**c**) \(L=256\). Figure 9: Learning curves of the frequency-domain ANC algorithms with multi-tone signal as input.
## 1 Introduction
Linear phase-based motion magnification fails for moving objects because large motions overwhelm small temporal variations. In practical measurement, only low-level small variations are magnified rather than large motions, such as object motion or the camera. For this reason, an acceleration motion magnification is employed to handle high-speed video before deviation extraction [30]. The image signal is first separated into multi-frequency bands and orientations by the complex steerable pyramid. By assuming that the large motion is approximately linear at the temporal scale of the small change, a second-order temporal derivative filter is used to calculate non-linear phase acceleration variations of video frames. Compared with that in the linear-based approach, the video after acceleration magnification has better control over image blurring and artifacts so that the structural contour is less influenced by the magnification. Subtle low-level deviation on the idealized edge profile can be effectively enhanced in the magnified high-speed video. This study proposes an approach for extracting the low-level valuable vibration signal from deviations of the structural idealized edge profile. The video acceleration magnification algorithm is applied to enhance subtle low-level variations along the selected contour. The deviations of the idealized edge profile in all video frames are then extracted and used to reconstruct an analysis matrix. Given that the relative intensity variations along the structural edge profile reflect their vibration characteristics, a method based on singular-value decomposition (SVD) is introduced to extract useful vibration signals involved in the analysis matrix. Without the requirement of textures on the target,our approach handles intensity variations that are vertical to straight-line or curve edges, and subtle structural vibration signals can be extracted from another perspective. The rest of the paper is organized as follows. The theoretical part first discusses derivations of the deviation extraction algorithm. An experiment on a saw blade is conducted to illustrate the specific deviation extraction. The video acceleration magnification algorithm for enhancing low-level subtle variations is then introduced in the following subsection. A simple black circle simulation test is given to show the effect of motion magnification. The SVD-based vibration extraction approach is presented to extract valuable variation signals from the generated analysis matrix. Two experiments are conducted in the verification part to demonstrate the practical effect of the video acceleration magnification and SVD-based extraction. The low-level dynamic responses of a clamped cantilever beam and a noise barrier are analyzed thoroughly by using the proposed method and high-speed camera systems. The discussion and conclusions are presented in the final section.
## 4 Experimental Section
### Experimental Results
#### 4.3.2 Effectiveness of the Cluster Formation Techniques  
For the purposes of this study, effectiveness is a measure of the average distance between a Mobile Node and the Anchor Nodes that track it: the lower the average distance, the more effective the cluster formation technique. For this metric, both the number of Anchor Nodes and the cluster formation technique are significant. More specifically, the greater the number of Anchor Nodes, the shorter the distance between Mobile Nodes and tracking Anchor Nodes (due to a higher concentration of Anchor Nodes in the network). This result is shown in the main effect plot for effectiveness in Figure 8.  
Figure 8: Main effect plot for effectiveness. \(n=160\), ANOVA, \(1\)_df, \(p<0.05\)_.  
Figure 7: Interval plot for load distribution. \(n=160\), ANOVA, \(1\)_df, \(p<0.05\)_.  
As shown in Figure 9, of the three clustering techniques, the _ad hoc_ technique results in the closest spacing between Mobile Nodes and their tracking Anchor Nodes. This result is supported by Figure 3, which shows a tight clustering of Anchor Nodes around the Mobile Node path.
## 3 Results and Discussion
In this work, it has been determined that there are significant differences in the position and inclination of the sensor. However, the results indicate that they are of no practical importance. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Factor** & **Df** & **Sum Square** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline A & 2 & 0.0829 & 0.04145 & 680.78 & \textless{}0.001 \\ Failure & 3 & 0.3211 & 0.10704 & 1757.89 & \textless{}0.001 \\ Classifier & 1 & 0.0065 & 0.00655 & 107.49 & \textless{}0.001 \\ A:Failure & 6 & 0.1427 & 0.02378 & 390.56 & \textless{}0.001 \\ Failure:Classifier & 3 & 0.0689 & 0.02296 & 377.06 & \textless{}0.001 \\ A:Classifier & 2 & 0.0089 & 0.00446 & 73.31 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: ANOVA factorial test for inclination. Figure 9: Interaction factors for inclination. (**a**) Accelerometer:Classifier. (**b**) Failure:Classifier. (**c**) Accelerometer:Failure. Figure 11: _Cont._  
Figure 10: Tukey’s Honestly Significant Difference (HSD) post-hoc for position. (**a**) Accelerometer. (**b**) Failure. (**c**) Classifier. (**d**) Accelerometer:Classifier. (**e**) Accelerometer:Failure. (**f**) Failure:Classifier.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 5 Improved V2I/N Link Reliability with Proposed Collaborative RSU for Resource Management
### UE-type RSU with Cell Range Expansion  
Cell range expansion technology supports higher spectral efficiency and energy efficiency in Heterogenic Networks (HetNet) consisting of conventional macro and small cells. We propose CRE technology to adjust the range of small-cell RSUs. CRE technology adds bias to the measured reception signal when connecting cell support VUE in HetNet, where macro cell RSUs and small-cell RSUs exist simultaneously, balancing the macro and small-cell RSUs [29]. A CRE technique would efficiently utilize UE-type RSUs with higher throughput performance in urban environments of 5G-NR-V2X networks, and prevent terminals adjacent to UE-type RSU boundaries from connecting to BS-type RSUs, expanding UE-type RSUs. When the received signal bias for the cell region expansion is \(\alpha\), we apply CRE using (13).  
\[\begin{split} SINR_{BStype}&< SINR_{UEtype}+\alpha\ \ for\ Serving\ BS\ type\ RSU\\ SINR_{BStype}&> SINR_{UEtype}+\alpha\ \ for\ Serving\ UE\ type\ RSU\\ \end{split} \tag{13}\]  
Here, we calculate the bias \(\alpha\) value applied to the received signal, as shown in Equation (14).  
\[\alpha=\frac{P_{Rx}(u)}{P_{int}^{UE}(u)+P_{int}^{BS}(u)+N_{0}}-\frac{P_{Rx}(u)} {P_{int}^{BS}(u)+N_{0}} \tag{14}\]  
Thus, we calculate each item of \(\alpha\) considering each case of the SINR of (13). When applying the cell expansion technique, we calculate the \(\alpha\) value by excluding interference from the BS-type RSU in the UE-type RSU.  
\[\begin{split} SINR(u)&=\frac{P_{Rx}(u)}{P_{int}^{ UE}(u)+N_{0}}\ for\ UE\ type\ RSU\ SINR\\ &=\frac{P_{Rx}(u)}{P_{int}^{BS}(u)+N_{0}}\ for\ BS\ type\ RSU\ SINR\\ &=\frac{P_{Rx}(u)}{P_{int}^{UE}(u)+P_{int}^{BS}(u)+N_{0}}\ for\ Cell\ Range\ Expansion\\ \end{split} \tag{15}\]  
Here, \(P_{Rx}(u)\) represents the received signal power of the VUE, and we calculate the total interference size received from the BS/UE-type RSU as \(P_{int}^{BS}(u)\) and \(P_{int}^{UE}(u)\) to model the SINR of the VUE.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 7 Conclusions  
With the development of wireless sensor networks, a large number of sensor data are collected. Finding a function model from the massive and distributed sensor data is very difficult. The requirement for the data mining techniques for wireless sensor network data led to the development of data mining algorithms. Each of the data mining algorithms solves certain problems of WSNs. Function mining is a significant part of data mining. With the quick increment of sensor nodes, a huge volume of dynamic, geographically distributed data are collected. How to efficiently analyze and transform this to usable knowledge by data mining is very important to the development and application of WSNs.  
In order to better find a function model from massive and distributed sensor data, this paper proposes a function finding algorithm using gene expression programming (FF-GEP) with adaptive population generation strategy, and global model generation algorithm based on unconstrained nonlinear least squares (GMG-UNLS). On the basis of FF-GEP and GMG-UNLS, a distributed global function model finding for wireless sensor networks data (DGFMF-WSND) is present. In order to better evaluate performance of the proposed algorithm, in this paper, a grid computing platform based on WS-Core and four test datasets are provided. The experimental results show that compared with GA, GP and GEP, FF-GEP has an advantage in time-consumption, error of fitness and prediction accuracy, and DGFMF-WSND has lower time-consumption, higher degree of fitness and excellent speed-up ratio and scale-up ratio.  
With the progress of sensor technology, applications for wireless sensor networks will become more mature and popular. All kinds of sensor data will become richer. Data mining techniques will be very important to execute in-depth analysis and improve performance of WSNs.  
The authors would like to thank the anonymous reviewers for their comments and constructive suggestions that have improved the paper. The subject is sponsored by the National Natural Science Foundation of China (No. 51507084, 61202354) and NUPTSF (No. NY214203).  
[PERSON] and [PERSON] contributed to the conception of the study; [PERSON] and [PERSON] contributed significantly to analysis and manuscript preparation; [PERSON] and [PERSON] performed the data analyses and wrote the manuscript; and [PERSON], [PERSON], and [PERSON] helped perform the analysis with constructive discussions.  
The authors declare no conflict of interest.