esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### IPv6 Protocol and IoT Communications
#### 4.1.1 IPv6 Adoption for IoT  
The primary driver for IPv6 adoption in the IoT is the need for more IP addresses. IPv4 provides only 4.3 billion addresses, which are now fully allocated, while IPv6 expands the address space to 340 undecillion addresses to support massive IoT growth. Table 6 highlights the projected growth indicators for IoT devices using IPv6.  
As seen in Table 6, cellular IoT connections using IPv6 are forecast to grow at a 98% CAGR from 2020 to 2025, topping 4.8 billion in 2025, as per ABI Research. Also, by 2023, over 50% of global IoT devices are expected to use IPv6, as per ABI Research. IPv6 adoption for the IoT brings other key benefits including simplified packet handling using fixed-length 128-bit addresses, no more network address translation (NAT), built-in IPSec security, better support for mobile devices, efficient multicasting and broadcasting, and extensibility for new features. Leading technology organizations like Google, Facebook, Akamai, Verisign, and Cisco have been early adopters of IPv6, encouraging global IPv6 deployment.
## 3 Experimental Bench and Theoretical Model
### Theoretical Model
#### 3.2.2 Initial Sample and Multi-Objective Optimization  
Determining the initial sample set is the first step in conducting multi-objective optimization [32]. An orthogonal array is a design method used to study the impact of multiple factors on objectives within the fewest possible experiments, effectively analyzing the relationships between factors and objectives and ensuring the validity of the analysis results. Due to the presence of three input factors in this study, an orthogonal design table \(\mathsf{L_{9}}\) (\(3^{4}\)) is employed as the initial sample set [33; 34]. It should be emphasized that experts design orthogonal arrays, and researchers need to choose a reasonable orthogonal table according to the specific research content to obtain comprehensive and reasonable results [34].  
The key to grey relational analysis (GRA) is to calculate the grey relational grade (GRG) of integrated parameters based on weighted allocation in multi-objective scenarios [35]. A higher grey relational grade indicates better outcomes [27].  
Firstly, the multi-objective parameters \(y_{i}(k)\) obtained from the orthogonal array undergo normalization. Equations (7) and (8) are used for objectives favoring larger and smaller values, respectively:  
\[X_{i}(k)=\frac{y_{i}(k)-\min(y_{i}(k))}{\max(y_{i}(k))-\min(y_{i}(k))} \tag{7}\]  
\[X_{i}(k)=\frac{max(y_{i}(k))-y_{i}(k)}{\max(y_{i}(k))-\min(y_{i}(k))} \tag{8}\]The grey relational coefficient \(\gamma\) can be expressed as [26]:  
\[\gamma(X_{0i},X_{i}(\mathbf{k}))=\frac{\Delta_{min}+\zeta\Delta_{max}}{\Delta_{ik} +\zeta\Delta_{max}} \tag{9}\]  
\[\Delta_{ik}=|X_{0i}-X_{i}(k)| \tag{10}\]  
\[\Delta_{min}=min\{\Delta_{ik}\} \tag{11}\]  
\[\Delta_{min}=max\{\Delta_{ik}\} \tag{12}\]  
where \(\zeta\) is the weight coefficient assigned to objectives, ranging from 0 to 1.  
A larger grey relational degree \(\delta(R_{0},R_{R})\) indicates better results, and it is calculated as [26; 27]:  
\[\beta_{i}=\frac{1-T_{i}}{\sum_{i}^{N_{ex}}(1-T_{i})}i=1,2,\ldots,N_{qc} \tag{13}\]  
\[T_{i}=-\frac{\sum_{k=1}^{N_{ex}}p_{ik}lnp_{ik}}{lnN_{ex}} \tag{14}\]  
\[p_{ik}=\frac{\gamma(X_{0i},X_{i}(\mathbf{k}))}{\sum_{k=1}^{N_{ex}}\gamma(X_{0 i},X_{i}(\mathbf{k}))}k=1,2,\ldots,N_{ex} \tag{15}\]  
\[\delta(R_{0},R_{k})=\sum_{i=1}^{N_{qc}}\beta_{i}\gamma(X_{0i},X_{i}(\mathbf{k })) \tag{16}\]  
where \(N_{ex}\) is the number of the experiment, and \(N_{qc}\) is the number of the objective.
## 3 Results and Discussion
### GC-IMS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.3.2 Monoterpenes  
According to the brightness of points, it can be found that monoterpenes in Figure 5c were of the highest content in different dried citrus peels, in accordance with GC-MS data in Table 1. Monoterpenes impart typical aromas of citrus, mint and turpentine, contributing the aromatic notes in orange peel oil [35; 45]. However, different from GC-MS analysis, the effect of drying methods on monoterpenes of dried citrus peels was unclear in GC-IMS analysis, probably because of saturated detection due to much higher concentration of monoterpenes in dried citrus peels and great sensitivity of the IMS detector.
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching
### Caching in High Liquidity Period  
The high moving speed and big number of mobile users, which produce a high frequency and variety of requests, are the distinguishing characteristics of the high liquidity period. Priority can be given to the diversity and richness of cached content in order to satisfy the needs of a wide variety of users. Paper [30] proposes a lightweight on-path cooperative caching strategy to ensure the diversity of content, called GAC. In GAC, caching decisions are divided into two phases. However, it does not consider scenarios of user mobility and the two rounds of caching decisions increase latency. We improved the GAC and applied it in high liquidity period.  
We use only one path traversal to find the least hot cached copy and replace the current content with it. The user will receive messages once the information has been found in the network. The min_heat and node_addr of each node will be continually updated in the message on the return path. The cache node we choose is the node_addr in the message that was finally returned to the destination. By adding new fields to the message that is returned and capturing the node information there, the new approach increases traversal efficiency by 50%.  
Algorithm 2 shows the specific method. The requested content is initially parsed in the current region, and when the parsing is successful, the user is immediately sent to the cached content. Otherwise, get the data from the source and initialize min_heat and node_addr field. On the returned path, minimum content popularity value of the current node will be compared with min_heat, the value of min_heat and node_addr will be constantly updated. After traversing all the nodes of the path once, content will be cached into the node that the node_addr field eventually records. Since ICN-GW and gNodeB do not participate cache in the 5G-ICN BN, we use request_node to represent the end of the path in step 8. It will analyze the received data packets and pass the content to the node recorded by node_addr. As shown in Figure 3, the node 3 with the lowest min_heat on the return path is selected as the cache point.
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 3 Methods  
The seamless stitching method focuses on eliminating discontinuities in mosaic images. The flow is shown in Figure 1. First, the feature extraction algorithm is used to register the two input images to obtain the homographic wrap for aligning them. Then the visual perception of these images' overlapped region is quantified to indicate the influence on the stitching result. Human visual discrimination and attention are involved in the process mentioned above. In general, the image processing is divided into 4 parts, including visual non-linearity (VN), luminance difference (LD), visual masking property (VM), and visual saliency (VS) respectively, and they are then combined to a weight map (WM) for selecting stitching line pixels. Edge detection is also implemented to distinguish safe edges and misaligned edges in order to provide a reference for seamline-pixel selection. An optimal stitching line is then detected based on WM and the reference edge. Lastly, images are stitched at the stitching line by multi-band blending to be a seamless mosaic image.  
As is stated and analyzed above, the key to this method is tracing a stitching line which can avoid discontinuous edges that will attract the attention of the human eyes strongly. Therefore, the WM of visual perception and the optimal stitching line detection method are crucial in our approach.
## 4 Discussion
### Future Research  
The high accuracy of the tree cover map derived from SPOT5 data with 5 m pixels supports previous research that images with pixels less than or equal to 10 m are required to accurately map small patches of remnant vegetation [29]. The use of multi-temporal data also contributed to the accurate mapping of tree cover and FPC, through distinguishing persistent overstorey tree cover from fluctuating understorey vegetation. Despite this, manual editing (selection of thresholds and local digitising) was still required to improve the utility of the final maps, mainly to reduce the commission of false positives due to persistent understorey vegetation. This editing was a time-consuming process, which future research should look at reducing. Two main options are apparent: increasing the temporal frequency of the imagery, or combining the multi-spectral data with information on vegetation structure derived from L-Band radar [69; 70; 71].  
Firstly, using more than four images across four years to model tree cover probability would capture more variation in understorey vegetation growth and senescence across the seasons and so improve the model accuracies. However, some vegetation types with no tree cover, such as evergreen pastures and wetlands, have grasses that rarely senesce and additional multi-spectral data may not improve the results. Furthermore, purchasing dense time-series of high resolution data for large areas would be prohibitively expensive. Free data from the Sentinel-2 satellite constellation will avoid this problem, and may provide the required spatial (10 m multispectral), spectral (13 bands), and temporal (5-day revisit cycle) characteristics to improve the current method.  
Secondly, data from L-Band radar satellites, such as the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR), are able to map differences in vegetation structure relating to the presence of tree trunks and branches [69; 70; 71]. The combination of PALSAR and Landsat data has previously improved vegetation mapping in QLD [69; 70; 71], and future research should investigate methods of combining with Sentinel-2 data.
# Trajectory Planning for Intelligent Robotic and
###### Abstract  
Trajectory planning is a crucial and challenging problem for research on intelligent robotic and mechatronic systems, which play a pivotal role in modern manufacturing processes, and especially within the framework of Industry 4.0 [1]. Indeed, in every robotic application, it is required to define not only a path, but also a motion law that can guarantee a feasible and safe operation of the system according to the requirements of the task and the limits of the robot [2]. Many approaches to the problem of trajectory planning have been developed and investigated in the literature, with applications that span from industrial, collaborative and, more in general, autonomous and intelligent robotic and mechatronic systems [3, 4].  
The motion law of a robotic system can be planned by considering different goals. The design of a proper motion law can be evaluated, for instance, in relation to the energy consumption of a robotic or mechatronic system, and, therefore, optimal trajectories can be determined based on the best performance of the robot in terms of time-energy consumption [5, 6, 7]. Another interesting field of application is that of vibration reduction. Indeed, many automatic machines and mechatronic applications require smooth and jerk-limited trajectories during the prescribed operation [8, 9, 10]. Moreover, emerging scenarios of industrial robotics, such as collaborative robotics and human-robot interaction, demand advanced strategies for the planning of robot trajectories to ensure smoothness, safety, and fluency during the execution of a task for a robot working alongside a human operator [11, 12, 13]. Finally, the trajectory planning for robotic and mechatronic systems is also tightly coupled with the motion control problem of such systems to guarantee high performance in the execution of the demanded motion law [14, 15].  
In this Special Issue, we invited researchers to contribute with original works and review articles related to trajectory planning for intelligent mechatronic systems, autonomous machines, industrial and collaborative manipulators, as well as mobile and reconfigurable robots. Original research papers on these themes have been sought, with a focus on both theoretical investigations and real-world applications on these topics.  
Suitable topics included, but were not restricted to, the following: path and trajectory planning, dynamic modelling, energy efficiency, vibration suppression, smooth trajectories, motion profile optimization, motion control, intelligent robotic and mechatronic systems, collaborative robotics, and motion planning for human-robot interaction.  
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract
* Another critical advantage observed in the literature with ML and DL algorithms is that detection is possible with only one leak sensor. * For localizing leaks, the most predominantly applied tool is cross-correlation. However, two sensors are needed for correlation, leading to an increase in system complexity. * The existing leak detection and localization methods use two different architectures: ML/DL architecture for leak detection and cross-correlation for localization. * The ML and DL detect the leak with reasonable accuracy, irrespective of the sensor used to collect data. * With an appropriate denoising technique, the cross-correlation gives an improved accuracy. * The MFCC helps to extract essential features and convert one-dimensional data to two-dimensional data. The primary objective of this paper is to propose a novel standalone architecture to detect and localize leaks with a single sensor with more accurate leak detection and localization accuracy to overcome all the research gaps listed above. This paper proposes a novel EMD with an optimal mode selector, an MFCC, and a 2 DCNN for detecting and localizing the leak with a single acousto-optic sensor. As EMD is the most potent denoising technique, this paper studies the variants of EMD before proposing a novel EMD with an optimal mode selector. The structure of this paper is as follows: (1) background study on pipeline leak detection methods, (2) experimental setup, (3) proposed leak detection and localization method, (4) results and discussion, and (5) conclusion.
## 1 Introduction
When it is not possible to improve the physicochemical parameters of chlorin molecules using chemical modifications, a solution that can help overcome this problem is the use of combinations of chlorins with nanoparticles [9]. The conjugation between metallic nanoparticles and porphyrinoid-type PSs, such as chlorins, is a field of great interest because nanocariers can overcome most of the limitations of classic PSs in PDT [10]. Many possibilities have so far been considered, such as the direct functionalization of PS with monoclonal antibodies or specific tumor-targeting molecules (proteins, peptides, and aptamers) [11; 12]. Photoactive porphyrinoids can be connected to several types of nanoparticles, including gold, titanium(IV) oxide, magnetic and upconverted nanoparticles, quantum dots, and metal-organic frameworks, to enhance the photocatalytic activity required in PDT or PACT [13; 14]. Therefore, contrary to classic drug delivery systems, the release of active PSs by metallic nanoparticles is not always required [10]. It is worth noting that nanoparticle surfaces can be modified with various functional groups, targeting agents, and components, including imaging agents, drugs, targeting ligands, and agents preventing their interference with the immune system. Such combinations are known to reveal many advantages. These are, in no specific order, an increase in the PS biodistribution, improved pharmacokinetics, and cell uptake, as well as targeting abilities around the tumor tissue and hampering the rapid elimination of PS accompanied by possible deactivation with plasma components. In addition, such combinations can prevent the unwelcome accumulation of PS in healthy tissue. Many nanoparticles can increase the amphiphilicity of PSs leading to better distribution through the cardiovascular system and enabling the enhanced permeability and retention effect (EPR) [11]. Among many types of porphyrinoids, various chlorin-type derivatives/metallic nanoparticle hybrids were recently investigated for imaging and therapeutic purposes. This review focuses on Ce6 in conjugation with gold and silver nanoparticles, magnetic, ZnO, TiO\({}_{2}\) nanoparticles, metal-organic frameworks (MOFs), and upconverting nanoparticles (UCNs). Examples of such nanoparticles presented in the scientific literature between 2010 and 2023 were collected with special emphasis on their applications in nanomedicine with a particular focus on PDT, PACT, PTT (photothermal therapy), and tumor tissue imaging. This study can also be considered a continuation of the lately published review, where the conjugates of metallic nanoparticles with BODIPY dyes--structurally different PSs--were presented [15].
# Nanofiber-Based Materials for Persistent Organic Pollutants in Water Remediation by Adsorption
## 3 Phenolic Compounds
### Nanofibers for Disinfectant Adsorption
#### 3.2.4 Kinetic Performance of Nanofiber Adsorbents  
Figure 8 shows the relationship between adsorption capacity and reaction equilibrium time for different adsorptive structures against phenol. These graphs aim to show the average shorter equilibrium times (under 5 h) reported for nanostructures compared to macro-adsorbents, as can be noticed in Figure 8A, as well as the yield of higher adsorption capacities for nanofibers compared to other nano-structures, with, for instance, activated carbon nanofibers reaching a capacity of 256.1 mg/g after 1.3 h (Table 7, \(N\)20). The difference in adsorptive performance and kinetics can be explained by the stability of nanofibers in water, while nanoparticle adsorbents tend to agglomerate under the effect of surface interactions such as Van der Waals, hence hindering the adsorption reaction. The difference in kinetics between nanofibers and macro-structures can be the result of the higher surface to volume ratio of nanofibers. Figure 8B details the composition of the reported adsorbents, revealing a combination of faster kinetics and higher performance for carbonaceous adsorbents, hence suggesting electron interaction as the most efficient predominant adsorption mechanism in the capture of phenol [91].  
Figure 7: Adsorption performance related to the adsorption test pH for several adsorbents compositions, against Phenol (**A**); 2-Chlorophenol (**B**); and Pentachlorophenol (**C**).  
2.5. Nanofiber Performance Comparison Regarding Initial Contaminant Concentration and Adsorbent Dose  
Figure 9 shows bar charts representing the adsorption capacity with the corresponding test conditions of initial disinfect concentration and adsorbent dose for each nanofiber adsorbent and selected benchmark adsorbents. Comparatively to relatively hydrophilic pesticides, nanofiber adsorbents demonstrated an adsorption capacity in the range of the one performed by activated carbon adsorbents, yet for a lower adsorbent dose. For instance, in the case of phenol capture, activated carbon treated with potassium permanganate demonstrated a capacity of 142.15 mg/g for a contamination of 100 mg/L and a dose of 1 g/L (Table 7, \(N6\)). For the same initial contamination, activated carbon microfiber-nanofiber composites showed a capacity of 150 mg/g for a dose of 0.6 g/L (Table 7, \(N16\)). It can be seen from all charts that carbon-based adsorbents are amongst the best against phenol and chlorophenols.  
Figure 8: Adsorption performance related to the adsorption test equilibrium time against Phenol, for several adsorbents structures (**A**) and compositions (**B**).  
Figure 9: Relationship between adsorbent dose (blue column, left scale), initial pesticide concentration (green column, middle right scale) and adsorption capacity (red scatter, far left scale) in the adsorption of Phenol (**A**); 2-Chlorophenol (**B**) and Pentachlorophenol (**C**).
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 4 Conclusions  
With oily sludge pyrolysis residue as a carrier to load CuO, ZnO, and TiO\({}_{2}\), composite materials (AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\)) were obtained, which were then used for the removal of methylene blue solution. Compared with that of CuO, ZnO, and TiO\({}_{2}\), AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) had a better methylene blue removal effect. Among them, AC-TiO\({}_{2}\) had the best removal effect of 5 mg/L methylene blue, and the removal capacity was 4.85 mg/g. Moreover, the possible removal process of methylene blue by composite materials was mainly chemical adsorption and was also controlled by both liquid film diffusion and intraparticle diffusion. From this study, oily sludge pyrolysis residue was applied to synthesize metal oxides-loaded composite materials and then used to remove methylene blue pollutants, which gave a possible idea to realize synergistic disposal of oily sludge pyrolysis residues and methylene blue pollutants.  
Designing the experiments, L.H. and J.L.; performing the experiments, L.H. and T.Z.; contributing the reagents, materials, and analysis tools, C.Q.; analyzing the data, T.Y. and B.Y.; writing the data, gents, materials, L.H. and T.Z.; writing the data, gents, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science of China (21808182, 51974245), Key Research and Development Program of Shaanxi Province (2022 SF-045, 2022 GY-128), Key Laboratory Scientific Research Project of Shaanxi Provincial Department of Education (18 JS088), and Science and Technology Plan Project of Xi'an (2020 KJRC0097, 2020 KJRC0098).  
Not applicable.  
**Data Availability Statement**: All data were presented in the main manuscript.  
The authors declare no conflict of interests. 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.