esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Remote sensing is a key technology that enables us to scale up our empirical, _in situ_ measurements of carbon uptake made at the site level. In low leaf area index ecosystems typical of semi-arid regions however, many assumptions of these remote sensing approaches fall short, given the complexities of the heterogeneous landscape and frequent disturbance. Here, we investigated the utility of remote sensing data for predicting gross primary production (GPP) in pinon-juniper woodlands in New Mexico (USA). We developed a simple model hierarchy using climate drivers and satellite vegetation indices (VIs) to predict GPP, which we validated against _in situ_ estimates of GPP from eddy-covariance. We tested the influence of pixel size on model fit by comparing model performance when using VIs from RapidEye (5 m) and the VIs from Landsat ETM+ (30 m). We also tested the ability of the normalized difference wetness index (NDWI) and normalized difference red edge (NDRE) to improve model fits. The best predictor of GPP at the undisturbed PJ woodland was Landsat ETM+ derived NDVI (normalized difference vegetation index), whereas at the disturbed site, the red-edge VI performed best (R\({}^{2}_{\text{adj}}\) of 0.92 and 0.90 respectively). The RapidEye data did improve model performance, but only after we controlled for the variability in sensor view angle, which had a significant impact on the apparent cover of vegetation in our low fractional cover experimental woodland. At both sites, model performance was best either during non-stressful growth conditions, where NDVI performed best, or during severe ecosystem stress conditions (e.g., during the girdling process), where NDRE and NDWI improved model fit, suggesting the inclusion of red-edge leveraging and moisture sensitive VI in simple, data driven models can constrain GPP estimate uncertainty during periods of high ecosystem stress or disturbance.  
Keywords:semi-arid; red-edge; NDWI; woody mortality +
Footnote †: journal: Remote Sensing
## 3 Proposed Scheme
### Authentication Stage
#### 3.2.2 Perceptual Hash Process and Authentication  
The authentication process is similar to the mainstream perceptual hashing algorithm. The authentication process in this paper is implemented by comparing two strings: Reconstructed perceptual hash value and the original one. A common way to determine how large the difference between the two sequences is to calculate the Hamming distance between them. However, since different algorithm parameters make the length perceptual hash values different, the number of bits with variable length cannot reflect the degree of value change very well. For example, two sequences of length 64 and 256 bits, if the same number of bits changed, it is obvious that the former is changed more. In this paper, we adopt the \"normalized Hamming distance\" [46] shown below:  
\[\mathit{Dis}=\left(\sum_{i=1}^{L}\left|h_{1}(i)-h_{2}(i)\right|\right)/L \tag{3}\]  
where \(h_{1}\) and \(h_{2}\) are perceptual hash values with \(L\) length. It is observed that the higher difference between the perceptual hash values', the larger the corresponding \(\mathit{Dis}\), but not greater than 1. If \(\mathit{Dis}\) is lower than the threshold \(T\), it means that the corresponding area is content-preserving. Otherwise, the content of the corresponding area has been tampered.
## 3 Results  
### Study Description  
As shown in Figure 2, after the initial search, a total of 147 articles were obtained: 78 from PubMed, 20 from SCOPUS and 49 from Web of Science. After the first analysis, 36 duplicates were discarded, leaving 111. Subsequently, a reading of the title and abstract of these articles was carried out, in which 41 more articles were eliminated as they were not relevant to the subject matter, leaving 70 articles to which the inclusion and exclusion criteria were applied. A review of these 70 articles was then carried out, discarding those articles that did not include IC\({}_{50}\) or the mechanism of anticancer action, resulting in a total of 48 articles. Those in which the nanoparticle or extract under study was purchased, or its origin was unknown, those that were not written in English or Spanish, and those in which the extract under study did not belong to the Plantae kingdom were eliminated, leaving 33 articles, i.e., those that did not meet the inclusion criteria. Finally, we excluded articles that did not evaluate the binding activity between the extract and the nanoparticle, as compared to the binding activity between the isolated extract or nanoparticle, to assess whether the results of the combination were superior. This left 17 articles on which this review was based.  
Given the diversity in the composition of nanoparticles, the results obtained in this systematic review are presented based on the different types of nanoparticles used. Of the articles included, eight used silver nanoparticles, with this material being the most employed, followed by zinc nanoparticles (four articles), gold nanoparticles (two articles), and calcium phosphate, and iron and lipid nanoparticles coated with chitosan, respectively (Figure 3). Regarding plant-derived compounds with antitumor activity against CRC, it should be noted that the plant part most frequently used to obtain these compounds was leaves, followed by seeds, skin, fruits, petals, aerated parts, bark and even the whole plant. On the other hand, the solvent most often used to obtain the extract was distilled water, followed by ethanol and methanol.  
Figure 2: Schematic diagram representative of the systematic search process carried out for this review.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 3 System Model and Problem Definition
### System Model  
Consider the problem of finding a landing site for a drone in an urban region \(\mathcal{A}\). The region \(\mathcal{A}\) is assumed to be a plane partially occupied by structures, including buildings, traffic lights, billboards, vegetation, and similar _obstacles_ typical of built environments. The drone, equipped with a downward-looking camera, explores the region \(\mathcal{A}\) by following a trajectory that lies on the plane \(\pi^{h}\) parallel to \(\mathcal{A}\) at a constant altitude \(h\). The region \(\pi^{h}\) is divided into two complementary sub-regions \(\pi^{h}=\pi^{h}_{f}\cup\pi^{h}_{o}\), where the former is the area where the drone is permitted to fly safely and securely while the latter is a no-fly zone (NFZ), i.e., an area where the drone cannot fly due to obstacles or regulations.  
Throughout the flight, subsets of \(\mathcal{A}\) are projected onto the camera image plane. Let \(\mathcal{I}_{k}=\mathcal{I}(w_{k})\) and \(\mathcal{F}_{k}\subset\mathcal{A}\) denote the image taken by the camera at the waypoint \(w_{k}\in\pi^{h}_{f}\) and the corresponding footprint on \(\mathcal{A}\). The image taken at waypoint \(w_{k}\) will be such that its center lies at the intersection between the camera's line of sight and the image plane. As illustrated in Figure 1, the size of the camera footprint \(\mathcal{F}_{k}\) is a function of the drone altitude and the FOV of the camera. The dimensions of the footprint can be calculated using the following:  
\[\begin{split} L_{1}&=2\cdot h\cdot\tan\left(\frac {\text{{HFOV}}}{2}\right)\\ L_{2}&=2\cdot h\cdot\tan\left(\frac{\text{{VFOV }}}{2}\right)\\ r_{fp}&=\min(L_{1},L_{2})/2\end{split} \tag{1}\]  
In order to guarantee a safe landing place, \(\mathcal{A}\) can be discretized into \(M\times N\) identical grid cells \(c_{mn}\) such that \(\bigcup c_{mn}=\mathcal{A}\). As shown in Figure 2, the cell dimensions are derived  
Figure 1: (**a**) The properties define the camera footprint size. As the orientation of the drone is unknown at the early stages of planning, the circle inscribed in the rectangle image is considered as its footprint. (**b**) The position of the drone. (**c**) The image captured by the drone camera.  
from the size of the body and propellers of the drone together with the flying conditions (e.g., wind). This partition is in line with discrete search theory (see, e.g., [28]) but with a different motivation.  
Following this, a probability \(p_{mn}\), with \(0\leq p_{mn}\leq 1\), is attached to each cell \(c_{mn}\), resulting in a probability map modeling the likelihood that each cell is suitable for landing. Then, the idea is to re-explore cells with a relatively high probability and update their values. As the level of certainty increases, the probabilities are likely to approach '1' or '0' until one or more probabilities exceed a certain threshold, making it possible to determine that the corresponding cell is suitable for landing. See [12] for a detailed discussion.
## 7 Discussions and Future Directions
### Distributed Computing and Edge Intelligence and Control  
The flying base station adds greater computing, intelligence, and control on the telecommunication network edge due to its aerial mobility and its location close to the user/recipient of the connectivity provision. In fact, the flying base station provides the first-hop, last-mile wireless link to the user, located between the user and the stationary base station. Edge sensing and computing enable the autonomous and ad-hoc operations of such flying base stations, as opposed to merely relying on the stationary base station's control communication command transmissions. Edge sensing on the flying base station of the channel state information (CSI), spectrum access, and location can provide richer information to the telecommunication infrastructure (including both types of base stations) to better serve the user. The edge computing on the flying base station can provide greater flexibility and coverage scope across multiple cells/stationary base stations and even connect via another flying base station. Blockchain, a.k.a. distributed ledger technology has synergy with edge computing because it introduces greater computing and intelligence to the distributed nodes, including those on the networking edge. Blockchain, in addition to distributed computing on a distributed set of nodes, introduces the information storage to replace the centralized server providing information access via the query-response model involving remote networking. Blockchain can enable critical edge functionalities including enabling security and distributed control communications to control and set up the operations (e.g., [36; 37]) or to provide the root of trust in mobile/vehicular networking from which the other functionalities can be implemented (e.g., [38; 39; 40]).
# Quantum Key Distribution in Kubernetes Clusters
###### Abstract  
Quantum Key Distribution (QKD) represents a reasonable countermeasure to the advent of Quantum Computing and its impact on current public-key cryptography. So far, considerable efforts have been devoted to investigate possible application scenarios for QKD in several domains such as Cloud Computing and NFV. This paper extends a previous work whose main objective was to propose a new software stack, the Quantum Software Stack (QSS), to integrate QKD into software-defined infrastructures. The contribution of this paper is twofold: enhancing the previous work adding functionalities to the first version of the QSS, and presenting a practical integration of the QSS in Kubernetes, which is the de-facto standard for container orchestration.  
Quantum Key Distribution; Quantum Cryptography; software-defined infrastructures +
Footnote †: journal:  
0000-0001-4600-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000  
though those are essential and relevant contributions to the development of the QSS, this paper's major contribution lies in the integration of the QSS in a Kubernetes cluster. Kubernetes (which we examine in Section 2.2) is the de-facto standard for container orchestration and a platform widely used in many modern paradigms such as Cloud, Fog, and Edge Computing, Network Functions Virtualisation (NFV), and Internet-of-Things (IoT). Given these critical application scenarios, QKD integration into Kubernetes represents a consistent contribution on a practical level and paves the way for the adoption of QKD systems in multiple domains.
# High-Precision Roller Supported by Active Magnetic Bearings
## 5 Automatic Alignment Function Test of Precision Roller with Magnetic Bearings
### Prescale Film Test  
The effectiveness of the automatic alignment of the roller was tested using a prescale film (LLW, Fujifilm Co., Tokyo, Japan), which can visually confirm the force distribution of the roller (Figure 17a). The prescale film is a kind of pressure-sensitive film that changes color depending on the pressing force [13]. This sort of automatic alignment could be practically realized by attaching load beneath the base plate of the printing equipment shown in Figure 17a. The prescale film tests were performed under two conditions, in which the base plate and roller were misaligned and aligned, respectively, and the results were compared. First, a force of 250 N was applied to the prescale film with a roller under the misalignment condition, and the result is shown in Figure 17b. Then, the roller was aligned with the base plate using the automatic alignment procedure and forces of 250 and 500 N were applied onto the prescale film by the roller. These results are shown in Figure 17c,d. The effectiveness of the automatic alignment of the roller can be visually confirmed through the uniform width of the marks on the prescale film.  
Figure 16: Test results of the improved automatic alignment control; reference force, 5 N; movement 1, 10 \(\upmu\)m; movement 2, 2 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test No.** & **Reference Force** & **Measured Force Error between Both Ends of the Roller** \\ \hline
1 & 5 [N] & 0.15 [N] \\
2 & 5 [N] & 0.24 [N] \\
3 & 5 [N] & 0.12 [N] \\
4 & 10 [N] & 0.21 [N] \\
5 & 10 [N] & 0.16 [N] \\ \hline \end{tabular}
\end{table}
Table 3: Automatic alignment test results for random misalignment.
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 3 Control Methods  
### PID Controller  
PID controllers are frequently selected for feedback control in automated industry. To measure the resulting error, the PID controller calculates the gap within the measured value of process and optimal set point value. PID has the potential of reducing the steady-state error by regulating the process control inputs.  
Figure 3 demonstrates the block diagram of PID controller for the active suspension system. Building blocks of PID controller includes three special variables which in result becomes the reason of the name of controller, i.e., Proportional, Integral and Derivative denoted by \(k_{p},k_{i}\) and \(k_{d}\). respectively. These variables are tuning gains of this controller.  
\[u(t)=k_{p}e(t)+k_{i}\int e(t)dt+k_{d}\frac{de(t)}{dt} \tag{8}\]  
where \(e(t)=y_{ref}-y\) represents the change among the existing value \(y\) and relative reference value \(y_{ref}\) of the system.
# Research on the Attitude Control Strategy of TBM Digging with V-Shaped Propulsion System
## 4 Joint Simulation and Result Analysis
### Simulation Results Analysis  
The digging attitude tracking of the TBM was visualized using the SimMechanics tool in Matlab. In this study, the V-TBM employed in the construction of the drainage corridor for the Zhejiang Jinyun pumped storage power station serves as the reference object [15]. The minimum turning radius of the designed axis for this TBM tunnel is set to 30 m. Based on this minimum turning radius, Figure 8 illustrates the tunneling attitude tracking of the V-TBM propulsion system under the influence of the tunneling attitude control strategy.  
As shown in Figure 8, the front shield platform undergoes significant positional adjustments as the propulsion system performs the design axis-following digging task. These adjustments include translation along the Z-axis and noticeable deflection around the Y-axis. Given the stringent requirements of the propulsion system under these specific working conditions, the minimum turning radius of the tunnel design axis was set to 30 m during the simulation. The control effectiveness of the propulsion system under this condition is compared and analyzed against the traditional PID control strategy. The detailed tracking of the propulsion system and the corresponding attitude change parameters are shown in Figure 9.  
By comparing the positional change parameters of the front shield platform under the digging attitude control strategy and PID control, it is evident that, at the beginning of the V-TBM propulsion stage, the front shield platform experiences slight disturbance due to the acceleration process. However, the control strategy effectively mitigates the impact of this disturbance on the overall positional change during the digging process. Once the propulsion system reaches a stable response state, the positional attitude trajectory of the front shield platform under the digging attitude control strategy is tracked more accurately, and the motion process demonstrates greater stability. Under the application of the digging attitude control strategy, the positional attitude change in the forward shield platform is primarily reflected in a translation of approximately 1000 mm along the Z-axis, a 17.6 mm offset along the X-axis, and a deflection angle of 1.89\({}^{\circ}\) around the Y-axis. In contrast, the PID control exhibits inferior performance in terms of both position tracking accuracy and attitude stability.  
Figure 8: Follow-up situation of single lane excavation posture.  
Further calculations show that the minimum turning radius of the propulsion system during this section of the trip is approximately 30.3 m under the influence of the digging attitude control strategy, which meets the design requirements. In summary, the proposed digging attitude control strategy outperforms the traditional PID control in terms of attitude control for the V-TBM propulsion system.
## 3 Basics of ML Algorithms
### Federated Learning (FL)  
The common way ML is applied to RIS applications is to train a learning algorithm using the signals that arrive at the RIS as input data, generating a model to enhance transmission performance. Figure 10 depicts a traditional learning scheme for the improvement of an RIS-assisted wireless communication link. Here, we have a centralized model directly computed at the BS, which is trained with data collected from different users within the network. The model requires raw data transmission to a central server, where training is performed. One thing to point out is that privacy and security concerns are not considered due to the sensitive nature of the wireless link. Moreover, the latency issues and scalability limitations can affect the centralized model. Figure 10 shows that the input settings are the goal to achieve, the parameters for the ML algorithm, and the optimization choices. To evaluate the model, an adequate performance metric has to be chosen according to the task selected.  
Figure 10: A traditional ML scheme for RIS-assisted communication systems.  
In contrast to the traditional ML scheme, FL is a _decentralized_ ML paradigm which suggests the collaboration of mobile devices to train a _global model_ without sharing its private data [53]. Each user device computes updates on its local model. The RIS surface distributes the trained parameters from the user equipment (UE) to the BS using the parameters learned from the users; then, the BS trains a global model using these parameters. The function of the aggregation unit is to update and manage the global model associated with the BS to improve it. Systematic improvement is made by selecting local models with the best conditions, such as proper channel scenarios and relevant training updates. This process is repeated until the global model achieves the desired performance or the system reaches a defined deadline [54]. Figure 11 illustrates the FL-based application of an RIS communication system.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
###### Abstract  
In recent years, interest in Unmanned Aerial Vehicles (UAVs) as a means to provide wireless connectivity has substantially increased thanks to their easy, fast and flexible deployment. Among the several possible applications of UAV networks explored by the current literature, they can be efficiently employed to collect Internet-of-Things (IoT) data because the non-stringent latency and small-size traffic type is particularly suited for UAVs' inherent characteristics. However, the implications coming from the implementation of existing technology in such kinds of nodes are not straightforward. In this article, we consider a Narrow Band IoT (NB-IoT) network served by a UAV base station. Because of the many configurations possible within the NB-IoT standard, such as the access structure and numerology, we thoroughly review the technical aspects that have to be implemented and may be affected by the proposed UAV-aided IoT network. For proper remarks, we investigate the network performance jointly in terms of the number of successful transmissions, access rate, latency, throughput and energy consumption. Then, we compare the obtained results on different and known trajectories in the research community and study the impact of varying UAV parameters such as speed and height. Moreover, the numerical assessment allows us to extend the discussion to the potential implications of this model in different scenarios. Thus, this article summarizes all the main aspects that must be considered in planning NB-IoT networks with UAVs.  
U +
Footnote †: journal: Advances in Neural Information Processing Systems  
1
Footnote 1: institutetext: Corresponding: [PERSON]
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
###### Abstract  
Superpixel segmentation has become a crucial pre-processing tool to reduce computation in many computer vision applications. In this paper, a superpixel extraction algorithm based on a seed strategy of contour encoding (SSCE) for infrared images is presented, which can generate superpixels with high boundary adherence and compactness. Specifically, SSCE can solve the problem of superpixels being unable to self-adapt to the image content. First, a contour encoding map is obtained by ray scanning the binary edge map, which ensures that each connected domain belongs to the same homogeneous region. Second, according to the seed sampling strategy, each seed point can be extracted from the contour encoding map. The initial seed set, which is adaptively scattered based on the local structure, is capable of improving the capability of boundary adherence, especially for small regions. Finally, the initial superpixels limited by the image contour are generated by clustering and refined by merging similar adjacent superpixels in the region adjacency graph (RAG) to reduce redundant superpixels. Experimental results on a self-built infrared dataset and the public datasets BSD500 and 3 Dircadb demonstrate the generalization ability in grayscale and medical images, and the superiority of the proposed method over several state-of-the-art methods in terms of accuracy and compactness.  
infrared image segmentation; superpixel; content-adaptive; seed strategy; superpixel refinement +
Footnote †: journal: Applied  
16 December 2021  
Accepted: 6 January 2022  
Published: 9 January 2022  
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**Copyright**: © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).