esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.  
SDN; WSN; SDWSN; energy-aware routing +
Footnote †: journal: Sensors
## Materials and Methods  
### Study Area  
Site-level LAI observational data are collected from CERN (Chinese Ecosystem Research Network) [56]. The six sites included are Ban Na Forest (BNF), Ai Lao Forest (ALF), Gong Ga Forest (GGF), Ding Hu Forest (DHF), He Shan Forest (HSF), and Shen Nong Forest (SNF). These sites are spread across southern China between 21.9\({}^{\circ}\) and 31.3\({}^{\circ}\)N, covering major plant functional types from northern subtropical zones to the northern tropical zones in this region (Figure 1). The regional climate is characterized by a wet, warm summer and a dry, mild winter [57]. The site-specific Koeppen climate classifications [58] are Cwa (Temperate zone warm summer; dry winter) for ALF, BNF, HSF, and SNF. Cfa (Temperate zone hot summer; no dry season) for DHF and Dwb (Cold zone dry winter; cold summer) for GGF. The mean annual temperature ranges between 4.2 and 21.8 \({}^{\circ}\)C. Annual precipitation ranges between 1506 mm and 2175 mm. The sites vary in elevation (above sea level) between 70 and 3160 m (Table 1). Forest types include the tropical seasonal rainforests, mixed coniferous forest, evergreen and deciduous mixed broad-leaved forest. The sites used support publically forests which are natural, except for forests at HSF and DHF, which support planted forests over 40 years old. All forests have at least three vertical layers: woody, shrub, and herbs. The tree density (trees per hectare) varies widely across sites. The highest tree density reaches over 7000 and the lowest is around 600 (Table S1). The range of the mean diameter breath heights is 5-20 cm (Table S1). The dominant species composition differs between sites but mainly consists of the evergreen species (Table S1). An image of forests measured at each site is included in Figure S1.  
Figure 1: Map of the study sites in Southern China.
## 2 Overview of Multi-Link Operation in IEEE 802.11 be WLANs
### Multi-Link Device and Network Architecture  
The system architecture for MLO is illustrated in Figure 1. An EHT device that is capable of MLO is called a _multi-link device_ (MLD), while a legacy device supporting SLO only is called a _single-link device_ (SLD). Each link of an MLD is regarded as a conventional single-link station (STA), and an MLD is considered to be a device affiliated with more than one STA. In contrast, an SLD (legacy device) is affiliated with only a single STA. Thus,we use the terms \"legacy device\" and \"legacy STA\" interchangeably. MLDs can be further classified into two types based on the type of the affiliated STAs:  
* AP multi-link device (AP MLD): each of the affiliated STAs is an AP.
* Non-AP multi-link device (non-AP MLD): each of the affiliated STAs is a non-AP (client) STA.  
Each affiliated STA of an MLD is composed of PHY and LMAC components. On top of the set of affiliated STAs, an MLD has one UMAC component to aggregate its set of affiliated STAs and provide LLC with a single MAC SAP. An AP STA and any non-AP STAs associated with it form a basic service set (BSS). As illustrated in Figure 1, an AP MLD can manage non-AP MLDs through the establishment of different links. A non-AP MLD can be linked to multiple non-AP STAs using individual channels, each of which is associated with an AP STA of the AP MLD.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Case Study  
### Data Selection  
The data used in this study was collected in February 2018 on G5513 (from Changsha West to Guanshan/Ningxiang Station) in Changsha, Hunan Province, China. The travel time was detected for all days and the detection interval was five minutes. Furthermore, 288 sequences are included in one day.  
The daily evening peak (17:00-19:00) data from G5513 (Changsha West to Guanshan/Ningxiang Station) was selected as a sample after comparing data of multiple weekdays and weekends, which contains 204 to 228 items. Other variable data items also need to be filtered according to the data above. The dimension feature values are based on the time series and the data requirements according to the prediction model.  
In this study, the regression SVM model is used to establish the model parameters, and the artificial fish swarm algorithm is used to establish the model parameter optimization algorithm. The first step in the optimization process of the artificial fish swarm algorithm is to feed in the training value and the training target through the SVM model to calculate the fitness of the individual. The most adaptable individual is regarded as the optimal value of the current fish group and the corresponding parameters \(\sigma\), \(\varepsilon\), and \(C\) of the current optimal value are saved. In the subsequent iteration, \(\sigma\), \(\varepsilon\), and \(C\) corresponding to the maximum fitness value are taken as the final optimization results. Accordingto the result of the optimization, all kernel function, loss function, and penalty parameters could be adjusted to look for the best SVM model for the prediction. The optimization results are presented in Table 4. The optimization process for the optimal value of the penalty parameter \(C\) is shown in Figure 4.  
The parameters of the artificial fish swarm algorithm are set as follows: the maximum number of iterations of the artificial fish is 100; the population size is 5; the maximum number of trials is 5; the crowding factor \(\delta\) is 0.618; the perceived distance is 0.5; and the moving step is 0.1.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 2 Background
### Intrusion Detection Systems
#### 2.1.1 Volume  
Data are being stored and sent over networks in ever-increasing amounts. The amount of data that was accessible in 2020 was predicted to exceed 44 ZB. By 2025, the amount of data generated each day is expected to reach 463 exabytes globally [4]. As a result, modern networks' traffic capacity has increased dramatically to accommodate the observed traffic level. Numerous contemporary backbone connections currently operate at wire speeds of 100 Gbps or more. To put this into context, a 100 Gbps network can handle 148,809,524 packets per second (PPS) [5]. To function at wire speeds, an IDS must be capable of analyzing within 6.72 nanoseconds. Providing an IDS at such a rapid rate is challenging, and achieving adequate efficacy, accuracy, and efficiency is similarly a challenge.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 2 Quality Check of Broadcast Orbits
### RSW Components of Coordinate Differences and Clock Differences
For the other six satellites the same value is 455, which means Data Validity Status = \"Working without guarantee\" and Health status = \"Signal Component currently in Test\". These satellites are the last four launched on 17 November 2016 (E03, E04, E05, E07) and the two launched on 22 August 2014 (E14, E18) into non nominal elliptical orbit [12]. The week analyzed is later than the epochs of orbit circularization, which were concluded in December 2014 and March 2015, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Satellite** & **Radial (m)** & **Along-Track (m)** & **Cross-Track (m)** & **Clock (ns)** & **Orbit** & **Clock \({}^{3}\)** \\ \hline C06 & \(-1.15\pm 0.31\) & \(0.14\pm 0.51\) & \(-0.01\pm 0.59\) & \(104.8\pm 2.2\) & IGSO \({}^{1}\) & Rb \\ C07 & \(0.99\pm 0.35\) & \(-0.03\pm 0.60\) & \(-0.21\pm 0.44\) & \(121.5\pm 2.0\) & IGSO \({}^{1}\) & Rb \\ C08 & \(0.31\pm 0.48\) & \(0.51\pm 0.39\) & \(0.09\pm 0.86\) & \(113.6\pm 2.4\) & IGSO \({}^{1}\) & Rb \\ C09 & \(-0.28\pm 0.30\) & \(-0.32\pm 0.43\) & \(-0.13\pm 0.77\) & \(94.8\pm 2.0\) & IGSO \({}^{1}\) & Rb \\ C10 & \(-0.14\pm 0.32\) & \(0.39\pm 0.70\) & \(-0.07\pm 0.46\) & \(95.5\pm 2.3\) & IGSO \({}^{1}\) & Rb \\ C11 & \(-0.39\pm 0.55\) & \(-0.20\pm 1.26\) & \(0.04\pm 0.64\) & \(88.4\pm 272\) & MEO \({}^{2}\) & Rb \\ C12 & \(-0.03\pm 0.49\) & \(0.02\pm 1.33\) & \(0.02\pm 0.54\) & \(93.1\pm 2.5\) & MEO \({}^{2}\) & Rb \\ C13 & \(0.26\pm 0.63\) & \(-0.22\pm 0.61\) & \(-0.03\pm 0.81\) & \(55.8\pm 1.6\) & IGSO \({}^{1}\) & Rb \\ C14 & \(0.43\pm 0.45\) & \(-0.53\pm 1.48\) & \(0.50\pm 0.59\) & \(99.9\pm 3.1\) & MEO \({}^{2}\) & Rb \\ \hline \end{tabular} \({}^{1}\)  
\end{table} 
Table 4: BeiDou mean values and standard deviations of coordinates and clock differences.
## 6 Performance Results
In fact, there is still a significant improvement when compared to the CIM-LLAD method, regardless of the SNR. Figures 9-13 illustrate the convergence of the proposed blind approach in a very noise environment (SNR = 0 dB). From these figures and the Std. Dev. depicted in Table 3, it is clear that our approach converges to the true values of cubic kernels with little fluctuation, even if we use a small sample length input such as \(N=1000\). The convergence corresponding to CIM-LLAD algorithm has more fluctuations of the estimated kernels when the SNR varies from 0 to 24 dB values as shown in Figures 9-13, respectively, and Std. Dev. calculated in Table 3 which is still in the same level for various SNR. For example, the Std. Dev. of the first estimated kernels, \(h(1,1,1)\), obtained using the proposed method is about 4 times lower than for CIM-LLAD, once again, with the additional advantage that we can estimate the kernels of the cubic system blindly (i.e., without any information about the excitation non-Gaussian signal), which is not the case of the adaptive algorithm that requires the input and output to identify the kernels of the model. Figure 14, where we present the NMSE as function the SNR, gives us a good idea about the precision of the estimated kernels. For high noise level (SNR = 0 dB), the NMSE using the proposed approach is almost 9 times lower than for the CIM-LLAD. The reason is the same as in system I: the proposed approach uses only the fifth-order cumulants, which are able to remove most of the Gaussian noise, allowing a more accurate identification of the cubic kernels. Figure 11: Estimated cubic kernel, \(h(3,3,3)=-0.688\), for system II. Figure 12: Estimated cubic kernel, \(h(4,4,4)=0.56\), for system II. Figure 4:0 Estimated cubic kernel, \(h(5,5,5)=0.227\), for system II.
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 4 Conclusions  
In this paper, to solve the problem of insufficient structural loose distress data, we propose to generate images based on DCGAN for data augmentation, which solves the problem of poor training effect due to insufficient data volume, and to input the generated images together with the original images into YOLOv5 for training. The recognition accuracy then improves by 5.3%. SAHI is introduced in the training stage, and the original images are sliced according to the set slice size and overlap rate before the original image is fed into YOLOv5 model for training. SAHI is also used in the inference stage and the final recognition accuracy reaches 74.1%, which is improved by 10.8%. The experimental results show that after expanding the data, the detection accuracy can be improved. Then, after introducing the SAHI framework in the training and inference stages, the detection accuracy can be improved more obviously, and most of the missed structural loose distress can be effectively detected, thus reducing the rate of missed detection.  
Conceptualization, R.C. and L.C.; Data curation, L.L.; Formal analysis, R.C. and C.L.; Funding acquisition, L.C. and C.L.; Investigation, R.C.; Methodology, R.C. and L.C.; Project administration, R.C. and L.C.; Resources, L.C. and C.L.; Software, R.C.; Supervision, L.C.; Validation, L.C., C.L. and L.L.; Visualization, L.C.; Writing--original draft, R.C.; Writing--review and editing, R.C. and L.C. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the key project of National Natural Science Foundation of China (G. No: 41930112).  
Not applicable.  
The datasets generated and analyzed during the current study are not publicly available due to being owned by other companies, but are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy.
## 4 Results  
### Measured Results and Geological Interpretation  
Figures 4-6 show the inversion results for lines L1, L3 and L5, respectively. Since the inversion results for lines L2 and L4 are similar to those of the other three lines, they are not included in the figures.  
Figure 4 presents the ERT profile for line L1. Due to the electrical contrasts between different strata, the resistivity profile effectively reflects the presence of underground abandoned mining tunnels. In Figure 4, six low-resistivity zones (V1-V6) are visible, with the resistivity values all being less than 70 \(\Omega\)\(\cdot\)m. Two of these zones are located in Zone-1, while the remaining four are located in Zone-2. Since there was continuous rainfall one week prior to the measurements, it is inferred that these six low-resistivity zones are caused by surface water infiltrating into the underground cavities. As shown in Figure 1, in the section K32+580-K33+30 along the survey line, the mine is relatively shallow and primarily mined through open-pit methods. In the range K33+30-K33+440, where the mine is deeper, tunnel mining was the main method. Based on the locations and depths of the low-resistivity zones, it is hypothesized that the two low-resistivity zones in Zone-1 correspond to water-filled karst caves while the four low-resistivity zones in Zone-2 correspond to water-filled abandoned mining tunnels.  
Figure 5 presents the ERT profile for line L3. In Figure 5, five low-resistivity zones (F1-F5) are visible, all with resistivity values of less than 70 \(\Omega\)\(\cdot\)m. Two of these zones  
Figure 4: ERT profile of L1.  
Figure 5: ERT profile of L3.  
Figure 6: ERT profile of L5.  
are located in Zone-1 and three are located in Zone-2. The two low-resistivity zones in Zone-1 (V1 and V2) are near K32+690 and K32+810, respectively, at depths of 10-15 m, similar to the corresponding zones in line L1 (F1 and F2). The three low-resistivity zones in Zone-2 (F3, F4, and F5) are also located at the same positions and depths as the four low-resistivity zones in line L1 (V3, V4, V5, and V6). Notably, F3 in line L3 appears as two smaller low-resistivity zones (V3 and V4) in line L1, indicating that the low-resistivity zones in this area are not fully connected and can be divided into two smaller zones.  
Figure 6 presents the ERT profile for line L5. In Figure 6, five low-resistivity zones (K1\(\sim\)K5) are visible, all with resistivity values of less than 70 \(\Omega\cdot\)m. Two of these zones are located in Zone-1, and three are located in Zone-2. The positions of these five low-resistivity zones are similar to those of the five low-resistivity zones in profile line L3 (Figure 5). From Figure 6, it can be seen that the low-resistivity zone between K33+260 and K33+350 consists of three small low-resistivity bodies. In contrast, the same location in lines L1 and L3 shows a single continuous large low-resistivity zone which is not fully connected in those lines.
## 3 Results and Discussion
### Nanoparticle Synthesis and Characterization
#### 3.1.2 Gold Nanoparticles Synthesis
For these Au colloids synthesized at room temperature, we tried the synthesis using several molar ratios between the Au precursor and \(\beta\)-CD. However, for 1 mM Au:1 mM \(\beta\)-CD or 1 mM Au:0.5 mM \(\beta\)-CD, we noticed that no reduction occurred using 5 mM K\({}_{2}\)CO, and the solutions turned blue when the potassium carbonate was 10 mM. For this reason, we reduced the concentration of the gold precursor to 0.5 mM and 0.25 mM, respectively, keeping a molar ratio of 1:2 (Au/\(\beta\)-CD). The absorption curves shown in Figure 5d indicate that the plasmonic peak is around 518 nm for all colloids. TEM images (Figure 6) showed spherical AuNPs with a uniform size of approximately 11 nm, well-separated and dispersed in the suspension medium. We present also TEM images of this sample in Figure S10 at different magnifications. All samples can be characterized by spherical AuNPs with mean sizes in the range of 10-12 nm and with a low polydispersity. This result is in contrast with the case of AgNPs synthesized at room temperature, which have almost a double diameter, are polydispersed, and their shapes are rather polygonal.
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 4 Performance Evaluation
### Results
#### 4.2.2 Impact of the Number of Consumers  
The second set of results, reported in Figure 9, measures the metrics of interest when varying the number of consumers.  
Under such settings, the proposed PaCC solution achieves the best performance in terms of content retrieval delay, number of hops, and exchanged NDN packets.  
It can be observed that, as the number of consumers increases, all schemes experience a shorter delay and a lower number of hops. Such a behaviour has to be ascribed to the fact that under the simulated settings, due to the Zipf distribution, a higher number of requests, from different consumers, concentrate on the same few contents, the most popular ones. Hence, such contents are more likely to be cached in the edge domain, instead of being retrieved from the original producer. The number of exchanged packets, instead, reasonably increases with the number of consumers, to account for the increasing number of Interests issued by more consumers and number of Data packets forwarded to each single consumer.
## 1 Introduction
### Co-Registration and Individual Tree Matching
#### 1.2.1 Automated Co-Registration of Field Survey Plots  
[PERSON] et al. [(20)] propose a tree linking and co-registration method based on cross-correlating the Gaussian surface images of terrestrial survey trees and aerial-detected trees for each pixel. To generate these images, the method uses the tree height or crown diameter for the survey trees and the CHMtree height for the aerially-detected trees. Within an inventory plot, all trees within a 10 m radius and with a diameter at breast height (DBH) larger than 5 cm were recorded. Tested with simulated forest stands, the method performed well (above 75% correctly linked trees) if the detection method was characterized by less than 20% commission and omission errors and a random position error with standard deviation below 1 m.  
The method of [PERSON] et al. [4] minimizes the weighted difference between the terrestrial individual tree heights and the CHM for each pixel within a given search window. The weighting factor represents the visibility of a tree in the CHM. A heuristic quality assessment is applied to label a proposed plot center position as \"certain\" or \"uncertain\". The method achieved robust results for inventory plots recorded by angle count sampling. Of the co-registered plot center positions, 68-74% were characterized by a distance of less than 5 m compared to the locations (a priori) identified by a human interpreter.  
Like [PERSON] et al. [20], [PERSON] and [PERSON] [17] use cross-correlation for the co-registration of fixed radius inventory plots and ALS data. The method uses the DBH or tree height of the survey trees to generate a smoothed reference image. The cross-correlation is calculated between the reference image and the CHM for each pixel. If at least five inventory trees were used, more than 91% of the co-registered plot center positions were characterized by a distance of less than 2 m compared with the locations (a posteriori) identified by a human expert. They also found that the method achieved no better results for plots with \"[...] already accurate GNSS positions, low-density ALS data and complex, mixed forests [...]\" ([17], p. 2324).  
Existing co-registration methods are especially challenged when the number of reference trees is low and the stand characteristics are complex for tree matching (high stand density, occurrence of similar tree patterns, evenly-aged stands, high proportion of deciduous trees). Heuristic methods have the disadvantages that the assumptions are defined based on the intuition of the expert, the suitability of these heuristics is hard to evaluate, and finally, the heuristics might compete. In addition, the algorithms provide no higher-order information (beyond quality flagging) about the reliability of the results.