esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Enhanced Barrier Membranes
### Membranes with Antimicrobial Factors  
The inclusion of several antimicrobial substances, such as antibiotics or silver ions, has been investigated in the structure of barrier membranes. Metronidazoleazole benzoate can be added to the layer in contact with the epithelial tissue, preventing bacterial adhesion and proliferation [111]. Favorable results of metronidazoleazole supplementation were also obtained by [PERSON] et al. [112] and [PERSON] et al. [113]. Other studies have investigated the use of azithromycin [114], tetracyclines [115; 116] or silver ions [117].  
Techniques for loading silk fibroin with 4-hexylsecrinol have been developed; 4-hexylsecrinol is a natural phenolic compound, with antimicrobial, antioxidant and antimutagenic abilities [118]. Moreover, 4-hexylsecrinol has been shown to inhibit thepathways of nuclear factor-\(\kappa\)B (NF-\(\kappa\)B) [119], a molecule involved in osteoclast differentiation, with a resorptive role. Favorable results have been obtained in vivo and in other studies [120,121]. One of the obstacles to the practical applicability of these membranes is represented by their poor mechanical properties [45].  
The disadvantages observed in the membranes with antimicrobial agents were the fact that most of them generally have a short release time of the drug; moreover, some periodontal tissue infections often occur after a relatively long period of time after surgery [112]. A number of studies have shown that the highest concentration of antibacterial substance is released in the first 3 days postoperatively, decreasing progressively in the following days and often becoming insufficient in situations of need [122, 123, 124]. Thus, the research focused on the development of techniques for making enriched membranes with slow and adequate release of microbial agent in order to prevent the so-called \"waste\" of the agent in unnecessary situations. [PERSON] et al. [125] managed to extend the release time of the antimicrobial agent to 15 days, using fibers with encapsulated nanotubes that were loaded with metronidazole.  
Trying to obtain the \"release as needed\" of metronidazoleic incorporated into the barrier membrane, [PERSON] et al. [112] investigated the effects of esterified and grafted metronidazoleic on the surface of PCL nanofiber pads modified by ester bonds as a barrier membrane; ester bonds can be selectively hydrolyzed by cholesterol esterase. Cholesterol esterase is an enzyme secreted by macrophages accumulated at the site of infection, the concentration of which is positively related to the severity of the infection. Increased cholesterol esterase results in an increased release of metronidazoleic from the membrane, with antibacterial action [112].  
One of the major problems with the use of membranes with antimicrobial agent is the risk of generating resistant bacterial species [126]; thus, there is an absolute need for clear guidelines and protocols for the use of such membranes.
# Narrowband Internet of Things (NB-IoT):
## 4 Narrowband-IoT Possible Deployment Strategies  
This section proposes potential deployment strategies for NB-IoT massive deployments by considering the NB-IoT support for small cells in heterogeneous network scenarios.  
HetNets are effective network deployment strategies in which small cells are incorporated in macrocells with the objective of improving performance in terms of capacity, coverage, and spectral efficiency. In general, the macrocells are characterized by higher transmit power and broader range as compared to small cells. When smalls cells are overlaid in macrocells, interference becomes a concern, especially to small cell edge users. Several techniques for interference cancellation, estimation and coordination that involve frequency hopping, frequency reuse, power control etc. have been proposed; however, the performance trade-offs for the proposed techniques for macrocells and small cells are still challenging [87; 88; 89; 90].  
Similarly, NB-IoT is expected to coexist with the currently deployed legacy LTE as well as the forthcoming 5G networks. This questions the existing interference management techniques i.e., are they applicable to the newly deployed technology since NB-IoT is expected to support different power classes while maintaining the low complexity which can severely affect the channel estimation quality and hence interference estimation quality. In NB-IoT coexistence with the legacy cellular networks, the possible deployment scenarios are as follows:  
* Synchronous NB-IoT deployment in all small cells;
* Asynchronous NB-IoT deployment in all small cells;
* Synchronous NB-IoT deployment in small cells and Macrocells;
* Asynchronous NB-IoT deployment in small cells and LTE in macrocells.  
These scenarios, as shown in Figure 6, are detailed in what follows.  
Figure 6: Summary of NB-IoT deployment strategies. For example, when NB-IoT is deployed in macrocell and LTE in small cell, when LTE is in macrocell and NB-IoT is in small cells, when NB-IoT is in macrocell and small cells support both NB-IoT and LTE, and when LTE is in macrocell and LTE/NB-IoT is in small cells
# ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect
## 1 Introduction  
Automated surveillance for security monitoring will increase more in venues such as airports, casinos, museums, and public places, etc. In these places, the government usually requires a surveillance system installed with intelligent software to monitor security and to detect suspicious behavior of people using cameras. In a remote-control center, human operators could search archived videos to find abnormal activities of people in public places such as college campuses, transport stations, and parking lots, etc. Having this type of surveillance system vastly increases the productivity of the human operator and expands coverage of the security monitoring service. However, the problem of human detection and tracking in video sequences becomes much harder when the camera is mounted on a mobile platform moving in a crowded workspace. If the object of interest is a deformable object, like a human, the problem becomes even more challenging to detect and to track the human.  
A modern mobile robot is usually prepared with an onboard digital camera and computer vision software for human detection and tracking, which can be applied in several real-world applications. In this paper, a real-time computer vision system for human detection and tracking on a wheeled mobile platform, such as a car-like robot, is presented. The contribution presented in this paper is mainly focused on the system design and implementation aspects. In other words, our design aims toachieve two main goals: Human detection and tracking, and wireless remote control of the mobile platform. Human detection and tracking are achieved through the integration of point cloud-based human detection, position-based human tracking, and motion analysis in the robot operating system (ROS) framework [1]. On the other hand, the wireless remote control of the mobile platform is achieved by the HyperText Transfer Protocol (HTTP) [2]. Developing such an intelligent wireless motion platform is very useful for many practical applications such as remote monitoring, data collection, and experimental purpose, etc. Experimental results validate the performance of the proposed system for detecting and tracking of multiple humans in an indoor environment.  
The rest of this paper is organized as follows. Section 2 explores the literature review on some of the related literature to this study. Section 3 explains our system design in detail. In Section 4, we explain details about the implementation of human detection, human tracking, and remote control of the mobile platform. Section 5 presents the experimental results of the proposed system with some discussions. Section 6 concludes the contribution of this paper followed by the future scope of this study.
# Smart Healthcare Network Management: A Comprehensive Review
## 2 Literature Review
### Wban  
The IEEE 802.15.4 standard, published in 2006, specifies the PHY and the MAC layers for short-range, low-power, low-cost, and low-bit-rate networks. These capabilities make it suitable for general WSN applications and IoT applications. However, IEEE 802.15.4 was not intended to support wireless communications in the vicinity of or inside the human body [12]. Therefore, IEEE 802.15 Task Group 6 initiated WBAN standardization activities in November 2007. The group recognized that existing standards did not fully meet the medical (i.e., proximity to human tissues) and relevant communication regulations for some application environments. The first draft was released in May 2010, and the final version was published in February 2012. IEEE 802.15.6 is targeted to serve a range of medical and non-medical applications.  
Since its initial release, the IEEE 802.15.6 standard has undergone revisions to accommodate emerging use cases. The IEEE 802.15.6 ma project is updating the standard to improve dependability for Human Body Area Networks (HBANs) and expand its applicability to Vehicle Body Area Networks (VBANs) [48]. Additionally, an amendment has been introduced to enhance the Ultra-Wideband (UWB) physical layer (PHY) and medium access control (MAC) to support more reliable communication in HBANs [49]. These advancements reflect ongoing efforts to refine WBAN standards to address modern medical and non-medical requirements better.  
[PERSON] et al. [50] surveyed the main WBAN applications, technologies, and standards, as well as issues related to WBAN design and evolution. They also presented a comparative study of the WBAN standards with simulation and experimental results. In another work [51], the authors surveyed the current state-of-the-art WBANs based on the latest standards and publications. They also discussed some open issues and challenges with WBANs. Healthcare systems in residential environments use WBANs for remote monitoring and health management. [PERSON] et al. [52] provided an architecture for residential environment healthcare systems and stated the technologies and networks needed in such an environment.
###### Abstract  
Fusion low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI) are important methods for obtaining high-resolution hyperspectral images (HR-HSI). Some hyperspectral image fusion application areas have strong real-time requirements for image fusion, and a fast fusion method is urgently needed. This paper proposes a fast and stable fusion method (FSF) based on matrix factorization, which can largely reduce the computational workloads of image fusion to achieve fast and efficient image fusion. FSF introduces the Moore-Penrose inverse in the fusion model to simplify the estimation of the coefficient matrix and uses singular value decomposition (SVD) to simplify the estimation of the spectral basis, thus significantly reducing the computational effort of model solving. Meanwhile, FSF introduces two multiplicative iterative processes to optimize the spectral basis and coefficient matrix to achieve stable and high-quality fusion. We have tested the fusion method on remote sensing and ground-based datasets. The experiments show that our proposed method can achieve the performance of several state-of-the-art algorithms while reducing execution time to less than 1% of such algorithms.  
hyperspectral imaging super-resolution; image fusion; matrix factorization +
Footnote †: journal: Applied Sciences
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 7 Challenges
### Si-Based Detectors Are Mainly Sensitive to Infrared Waves  
In 2020, [PERSON] et al. demonstrated a black silicon-based photodetector with high responsivity up to 100 A/W at 400 nm and broad response spectrum from 400 to 1600 nm. In their work, the broad-bandgap responsivity and SNR increase, and the dark current, are mitigated through the utilization of rapid thermal annealing and hydrogenated surface passivation [112], which may be a promising candidate for Si-based detectors for VLC systems.
## 3 Results and Discussion
### Scenario 1  
Figure 7a-c shows the performance of the three controllers. LQR had the worst response with an overshoot of about 20% and the highest acceleration of 0.4247 m/s\({}^{2}\). The actor network and the PID performed significantly better with almost matching results. The average acceleration for the actor network and the PID was 0.2827 m/s\({}^{2}\) and 0.2919 m/s\({}^{2}\), respectively. This encouraged us to further test the actor network in new conditions and environments that were different from the training environment.
## 3 Radar Micro-Doppler Analysis of Vibrating Structures  
This section first lays the groundwork for discussing the radar micro-Doppler characteristics of an idling vehicle by exploring its simple mechanical model. This is followed by a discussion on the radar micro-Doppler features arising from idling vehicles, vehicle starts, gear shifts, and occupancy. We end with the presentation of a theoretical model for micro-Doppler characterization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Type** & **Model** & **Idling Speed** & **Dominant Vibration Frequency** \\ \hline Small Car (3-cyl) & Peugeot 108 (2014) & \(\sim\)900 rpm & \(\sim\)15 Hz (1 \(\times\) crank) \\ Hatchback (4-cyl) & Honda Civic Hatchback (2019) & \(\sim\)650 rpm & \(\sim\)10–11 Hz range \\ Sedan (4-cyl) & Mazda 3 (2025) & \(\sim\)500–550 rpm & \(\sim\)8–9 Hz \\ Compact SUV (4-cyl) & Toyota RAV4 (2.4 L) & \(\sim\)600–700 rpm & \(\sim\)12–15 Hz typical \\ Large SUV (V6) & Jeep Wrangler (3.6 L V6) & \(\sim\)600 rpm & \(\sim\)10–15 Hz \\ Pickup/Large SUV (V6) & Honda Ridgeline (3.5 L V6) & \(\sim\)600 rpm & \(\sim\)10–15 Hz \\ Passenger Van (4-cyl/6-cyl) & Ford Transit/similar & \(\sim\)600 rpm & \(\sim\)10–20 Hz \\ Bus (diesel, heavy) & Generic transit bus & \(\sim\)600 rpm & \(\sim\)10–20 Hz \\ Truck (heavy-duty, diesel) & Commercial truck chassis & \(\sim\)500 rpm & \(\sim\)8–10 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical vibration frequencies for vehicle types.
## 4 Discussion
These findings suggest that the desorption of the EO-PG formulation can be optimized by controlling these factors. The EO-PG desorption kinetics were modeled using the Fickian model of diffusion, and concentration profiles were simulated at various time points, including t = 0 h, t = 1 h, t = 24 h, and t = 360 h. Based on the simulations, it was observed that the Fickian model was able to fit well with the experimental data, and therefore, it could be used to forecast the desorption kinetics of the EO-PG formulation. This simulation has important implications because it enables researchers to determine the amount of oil that remains during biological applications, which is critical for assessing the biological activities of the oil. These profiles provide information about the concentration of the oil at various heights and at different times, enabling researchers to obtain more precise data [48; 49; 50]. The correlations between the parameters of the diffusion model and the parameters of the biological activities were explored using principal component analysis (PCA). The diffusion coefficient \(D\) and the flux \(F\) were found to be perpendicular in the correlation circle, indicating that these two variables were not correlated and were independent. The diffusion coefficient \(D\) was inversely correlated with the mortality \(M\%\) and the length of the aerial part \(L_{A}\), while the flux \(F\) was inversely correlated with the weight W and the length of the root part \(L_{R}\). These findings suggest that the diffusion parameters play an important role in the biological activities of the EO-PG formulation. Finally, the EO-PG formulation shows promising potential as a biological formulation with high biological value. The sustained release of essential oils and the beneficial effects of phosphorus on plant growth make it an effective pest control and growth-promoting agent.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Evaluating Parameters of Simulation
#### 3.16.2 End-To-End Delay  
The overall time required for a message to travel from the transmitter to the recipient vehicle is computed as the total of all delays in the connection induced by intermediary nodes [52]. It is calculated by adding all transmission delays together. It is measured in seconds, milliseconds, or nanoseconds. A lower delay indicates the improved performance of the network.  
\[EED=\frac{(T_{delay}+P_{delay}+Q_{delay}+Pro_{delay})}{totalP_{r}} \tag{19}\]
## 1 Introduction
### Objectives  
The objectives of this research include (1) to provide eco-efficiency assessment for some European countries for the period of 2013-2017, so that these counties can understand their recent performance on this efficiency; (2) to understand the improvement or decrease in efficiency of a country over a specified period of time; and (3) to find some policy implications.  
In this research, a total of 17 European countries were selected as Decision Making Units (DMUs) and evaluated using the Slacks-based measure (SBM) Data Envelopment Analysis (DEA) together with the Malmquist Index (MI). The DEA component is used to measure the relative efficiency of the DMUs, while the MI component measures the improvement or decrease in efficiency of a DMU over a specific period of time [10]. This approach can help us to achieve the first and second objectives. In the SBM DEA model, energy consumption per capita and share of renewable energy in total energy consumption are used as energy inputs, while labor productivity and gross capital formation are used as economy inputs. In terms of output, Gross Domestic Product (GDP) per capita is used as one desired output, and CO\({}_{2}\) emissions per capita is used as one undesired output. For change analysis, we employ Catch-Up and Frontier-Shift and Malmquist productivity index (MPI) to assess the efficiency change, technological change, and total productivity change, respectively for these DMUs. The results are helpful for these countries to understand their current performance.  
The rest of this paper is organized as follows: Section 2 includes a literature review on DEA and relevant research; Section 3 details the methodology; Section 4 details data collection and verification; Section 5 specifies experimental results; and Section 6 gives conclusions and future research directions.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 5 Conclusions  
LPWA Networks have become a de facto communication standard for IoT since, power consumption, coverage are key features. Being an open platform LoRaWAN has become an important protocol among the LPWA Networks. In this study, we introduced LoRaWAN architecture and design details with state of the art literature review of the protocol. LoRa is RF modulation which enables very long from 2 km (urban) to 20 km (rural areas) data transmission ranges. LoRa signals can penetrate through walls and receivers can extract data from very weak signals. LoRa is a closed modulation however, LoRaWAN is an open standard developed to prevent consumption and moderate the network effectively. LoRaWAN Classes introduce TX and RX policies to control medium access and energy consumption of the end-nodes.  
When compared with other communication technologies, LoRaWAN has advantages on being an open standard, builtin security, GPS-free geolocation, ability to have long range communication, low energy consumption, and options to have private deployments. Besides it is advantages, low data rate and duty cycle constraints limit LoRaWAN networks for real-time applications. LoRaWAN best fits scenarios where data transmissions are rare (a few packets in a day) and the size of the payload is around 10-50 bytes. Smart city, smart grids, smart farming, and remote monitoring systems are the areas of the example where LoRaWAN could be best beneficial.  
The majority of the LoRaWAN studies are focused on the data transmission ranges and applicability of the protocol of different application scenarios. The technology is fairly new and there are still open research fields are available to concentrate on such as optimization of high dense LoRaWAN deployments, ADR, Network Management, interoperability of applications and the networks.  
This paper is the result of collaborative work of all authors. Conceptualization, M.A.E. and M.A.A.; writing--original draft preparation, M.A.E., M.T.B., M.A.A. and H.E.; writing--review and editing, M.A.E., M.T.B., M.A.A. and H.E.; Methodology, M.A.E., M.T.B. and M.A.A.; validation, M.A.E. and M.A.A.; visualization, M.A.E.; supervision, M.A.A.  
This research was funded by Istanbul University-Cerrahpasa Scientific Research Projects with title LoRaWAN Next Generation LPWAN Technology Analysis and grant number FBA-2017-24061.  
The authors declare no conflict of interest.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Experimental Scenarios and Classification Measures  
#### 4.3.1 Experimental Scenarios  
After the pre-processing process, 18 features extracted from each domain name form a record in the experimental dataset. We selected three training datasets, namely T1, T2 and T3, and a testing dataset, called TEST from the experimental dataset. The T1 set contains 10,000 records from benign domain names and 10,000 records from malicious domain names. The T2 set contains 5000 records from benign domain names and 15,000 records from malicious domain names and the T3 set contains 15,000 records from benign domain names and 5000 records from malicious domain names. The selection of 3 training datasets with different numbers of benign and malicious domain names to assess their effect on detection performance. The TEST dataset includes 10,000 records from benign domain names and 10,000 records from malicious domain names. Records from benign domain names are labeled \"good\" and records from malicious domain names are labeled \"bad\". Data for testing is not part of the training sets. Table 1 describes the dimensions of the components of the training datasets and the testing dataset.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Evaluation: Horner's Rule  
As previously mentioned, speed is a critical factor for this model, as it is intended to be integrated into more complex EDFA or network models. Therefore, optimizing the computation speed of polynomial evaluation is essential. To achieve this, we employ Horner's method, an algorithm designed to minimize the number of operations required for polynomial evaluation by computing it in a nested form. This method can be generalized for any polynomial [23].  
\[p(x)=a_{n}x^{n}+a_{n-1}x^{n-1}+...+a_{1}x+a_{0}\\ =a_{0}+x(a_{1}+x(a_{2}+...+x(a_{n-1}+a_{n}x)...)) \tag{20}\]  
In this format, an \(n_{th}\) degree polynomial can be evaluated with just \(n\) multiplications and \(n\) additions. While any polynomial can be evaluated using this algorithm, and a general function for this computation could be easily created, Python loops are known to be relatively slow, which is common for interpreted languages. To further speed up the computation, the entire polynomial expression is unrolled into Horner's form and directly implemented within the interface library.